Browse Topic: Driving control assistance

Items (321)

Challenges and advantages in implementing ADAS systems in buses: a broad overview (Desafios e vantagens na implementação de sistemas ADAS em ônibus: uma visão abrangente)

2026-36-0344

7/30/2026

The implementation of ADAS in buses represents both a significant opportunity and a complex challenge for the future of urban mobility. While ADAS technologies such as lane departure warning, adaptive cruise control, blind spot detection, and autonomous emergency braking have been widely adopted in passenger cars and trucks, their integration into buses has been slower due to unique operational and safety concerns. This paper provides a broad overview of the advantages and obstacles associated with ADAS deployment in public transport vehicles, with particular emphasis on passenger safety, regulatory frameworks, and operational efficiency. Key barriers include the vulnerability of standing passengers during sudden braking events, the unpredictability of pedestrians and cyclists in dense urban environments, and the economic constraints faced by bus operators. At the same time, regulatory initiatives such as Transport for London’s Bus Safety Standard, the European Union’s General Safety Regulation, and Brazil’s MOVER program are driving the gradual adoption of these systems. The benefits of ADAS in buses extend beyond accident reduction, encompassing improved driver ergonomics, reduced fatigue, lower maintenance costs, and enhanced passenger comfort. Case studies from Europe, Brazil, and Asia highlight both the safety potential and the reluctance of drivers to fully embrace these technologies, often due to knowledge gaps and perceived inconvenience. The analysis underscores that successful implementation requires not only technological adaptation but also comprehensive driver training, infrastructure readiness, and public policy support. Ultimately, ADAS in buses should be understood as a transitional step toward autonomous mobility, offering immediate safety gains while reshaping the paradigm of urban transport.

Marcon, Ederson, Michelon, Gabriel, do Nascimento, Vagner

An Investigation of Drowsy Lane Departures from Driving Simulator Studies

09-14-01-0014

5/4/2026

NHTSA is conducting research to evaluate the current state-of-the-art technology for lane departure warning (LDW) and lane-keeping assistance (LKA) technology. NHTSA is undertaking research to understand the nature of real-world lane departures and recovery behaviors. While some information about lane departures can be learned from crash datasets, the purpose of this work was to mine simulator datasets for lane departures, analyze them in greater detail than is possible from crash reports or naturalistic studies, and link their characteristics to driver drowsiness. The objective of the study was to determine whether there are differences in lane departure characteristics as a function of driver drowsiness. This research used a novel approach by combining data from six different driving simulator studies on driver drowsiness. The dataset included a sample of 380 drivers. Study drives occurred during overnight hours after periods of sleep deprivation, with participants being awake for at least 16 h prior to driving. Study drives ranged in duration from relatively short 45-min to nearly 4 h. The datasets were reduced to characterize 5805 individual lane departures. Lane departures were delineated into three phases (pre-departure, departure, and recovery) and two transition points (onset and reentry) to capture driver behaviors under drowsiness. We hypothesized that lane departures would look different under different levels of drowsiness. Drives took place across a range of roadway environments that included interstate highways, rural highways, rural roads, and low-speed urban areas. Drowsiness was sampled at points before, during, and after the drive using self-ratings [Karolinska Sleepiness Scale (KSS) or Stanford Sleepiness Scale (SSS)] as well as the expert Observational Rating of Drowsiness (ORD). High levels of drowsiness were associated with a narrow speed range at highway speeds and the least amount of throttle input, while low levels of drowsiness had more steering activity, more throttle input, and a broader range of speeds. The results of this study will improve understanding of vehicle kinematics and driver behavior in drowsy lane departures using a safe methodology to help address crash dataset limitations.

Schwarz, Chris, Gaspar, John, Shull, Emily, Venegas, Michael

Characterization of Lane Keeping and Departure Scenarios Using Large-Scale Naturalistic Driving Data

09-14-01-0022

5/4/2026

Roadway departures remain a major cause of crashes, injuries, and fatalities on U.S. roads. Technologies such as lane keeping assist (LKA) and lane centering assist (LCA) can help mitigate these crashes, but their development involves extensive characterization of the parameter space in which they operate. Lane and road departures (LDs/RDs) and lane changes (LCs) must be systematically described and quantified to distinguish kinematic features, identify contributing factors, and benchmark system influence on lateral control. This study developed a unified pipeline to mine over 36 million miles of naturalistic driving study (NDS) data collected from more than 3800 participants. The pipeline integrates various types of signals to detect roadway boundary crossings, classify LKA-relevant scenarios, and extract roadway, driver, environmental, and assistance-related parameters. Lane keeping epochs with and without LKA were also extracted to quantify system influence on lateral control. In the NDS analysis, crashes include both object contact events and RDs, defined as non-premeditated departures from the intended travel surface involving at least one tire. Analysis of pre-identified crashes in the NDS showed that unintentional RDs accounted for 5.67%, unintentional LDs for 1.76%, and intentional LCs for 1.55%, corresponding to lower-bound rates of 2.7, 0.8, and 0.7 crashes per million vehicle miles traveled. RD crashes were predominantly right-sided, LD crashes left-sided, and both were overrepresented on curves and under adverse conditions. Loss of control preceded 22% of RD crashes and 69% of LD crashes. Beyond crashes and near-crashes (CNCs), the algorithm identified approximately 3 million LCs and 0.3 million LDs/RDs. LCs typically involved larger crossing angles that decreased with speed, while departures clustered within 0°–2°. Compared with CNCs, these occurred at higher speeds and smaller angles. LKA consistently reduced lateral variability without biasing the mean offset.

Ali, Gibran, Terranova, Paolo, Williams, Vicki, Holley, Dustin, Saffy, Joshua, Antona-Makoshi, Jacobo, Kefauver, Kevin, Shull, Emily, Li, Eric, Venegas, Michael

A Dynamic Driver Comfort Index for Stabilizing Mixed Traffic Behavior

2026-01-0099

4/7/2026

Despite remarkable advances in vehicle technology - enhancing comfort, safety, and automation – productivity of transportation over the road continues to decline. Stop-and-go driving remains one of the most persistent inefficiencies in modern mobility systems, leading to greater travel delays, energy waste, emissions, and accident risk. As vehicle volumes rise, these effects compound into systemic challenges, including driver frustration, unstable flow dynamics, and elevated greenhouse gas (GHG) emissions. To address these issues, an extensive data-driven evaluation was performed characterizing the underlying causes of traffic instability and uncovering hidden behavioral parameters influencing traffic flow. This research led to the identification of a previously unrecognized metric - the Driver Comfort Index (DCI) - which quantifies an inter-vehicle spacing behavior that reflects intrinsic human driving behavior. Building on this discovery, mixed traffic is explored to identify its phenomena, where human-driven and machine-controlled vehicles coexist and share the road. It appears that adaptive cruise control (ACC) and connected autonomous vehicles (CAV) are controlled by a non-intrinsic parameter so that traffic mix suffers from a mismatch of vehicle dynamics. This mismatch is explored, and it is proposed to harmonize traffic dynamics by adopting the natural DCI parameter as the single control mechanism. Analytical studies demonstrate that DCI-based traffic flow orchestration, applied integrally to human- and machine-controlled vehicles, enhances traffic flow stability, mitigates stop-and-go oscillations, and significantly improves network efficiency, safety, and environmental performance.

Schlueter, Georg J.

Prediction of Pressure Pulsation in Brake Line Considering Wave Propagation Characteristics of Fluid Contained in Flexible Hose for Automotive Hydraulic Brake System

2026-01-0225

4/7/2026

Brake pulsation noise caused by fluid-borne vibration, which is generated by pressure pulsations from the pump in the Electronic Stability Control (ESC) modulator, occurs when the control brake function is activated under various driving conditions, such as Adaptive Cruise Control (ACC) and regenerative-friction brake coordination. This noise is particularly noticeable in Battery Electric Vehicles (BEVs), where the background noise from the power source is lower than that of internal combustion engine vehicles. The simulation of pressure pulsations in the brake system requires the excitation force of the pump built into the ESC modulator, the characteristics of valves, and the characteristics of the flexible hose; however, it is extremely difficult to determine these parameters with high accuracy from the design specifications. For this reason, in this study, the pump and valves were experimentally identified, while the flexible hose was represented by a three-element Voigt model to describe its viscoelastic properties. The pressure pulsation prediction model of the brake line was constructed by formulating the characteristics of all hydraulic components using four-pole matrix equations consisting of pressure, flow rate, and impedance, along with the continuity equation. This paper describes the method for creating a prediction model of pressure pulsation, the measurement results of the transfer matrix of the flexible hose, the modeling and parameter identification method of the flexible hose, and the accuracy verification results from a bench test of a brake system equivalent to an actual vehicle. Since a high-accuracy prediction model has been constructed, by predicting the pressure pulsation at any position in the brake line for any pump rotation speed, it can be utilized for designing the pump rotation speed that achieves both braking performance and brake pulsation noise reduction, and for examining bending and clamp positions of the brake line that avoid the amplification of excitation force.

Koike, Yohei, Komada, Masashi, Yano, Masahiro, Yoshioka, Nobuhiko

Personalized Driver Model for On-Ramp Merging Using LSTM

2026-01-0525

4/7/2026

This study develops a personalized driver model for expressway merging, embedding individual driving characteristics into automated longitudinal and lateral control via Long Short-Term Memory (LSTM) networks. Uniform assistance (Advanced Driver Assist System, ADAS) can feel uncomfortable when it does not match a driver’s style; we therefore target the merge maneuver—a safety-critical task requiring anticipation and timing—and test whether merging-related context improves model fidelity. Driving data were collected in a high-fidelity motion-base simulator across two merging scenarios (13 licensed drivers in total). Inputs comprised ego speed, Headway distance and relative speed to the lead vehicle, and geometric context variables (distance to the end of the acceleration lane and to the hard/soft nose); outputs were longitudinal and, in the cross-scenario study, lateral accelerations. Models were trained per driver and evaluated by root mean square error (RMSE). Including merging context reduced longitudinal error in Experiment 1 (Gotemba IC) by about 30% on average relative to models without context, while errors remained below 0.5 m/s2. In Experiment 2 (Tokyo–Nagoya Expressway vs. Tokyo Metropolitan Expressway), longitudinal and lateral errors were low across both geometries; group-mean trends favored context but were non-significant, reflecting small sample size and inter-individual variability. Questionnaire-based evaluations in the simulator showed ratings close to real driving for discomfort, merge timing, and perceived safety; similarity and willingness to use were slightly higher in the urban expressway scenario, suggesting good user acceptance in constrained conditions. These findings indicate that incorporating merging context enables personalized control that better reflects individual driving behavior, while pointing to future work on generalization across geometries, speed ranges, and richer interaction semantics.

Shen, Shuncong, Hirose, Toshiya

Enhancing Traditional Adaptive Cruise Control through Map-Based Feature Integration: A Performance Evaluation Study on Stationary Vehicle Response at High Speeds

2026-01-0056

4/7/2026

Adaptive Cruise Control (ACC) has become a widely adopted driver-assist technology, designed primarily to regulate a vehicle’s longitudinal movement while maintaining a safe following distance from the preceding vehicle. A key performance criterion is the system’s ability to detect and respond to both moving and stationary target vehicles within the ego vehicle’s path. While manufacturers typically validate ACC performance within specific speed ranges, responding to stationary objects remains particularly challenging due to limited sensor range, difficulty in detecting distant stationary targets, and constrained deceleration capabilities. Beyond certified operating limits, overall system reliability may degrade. Nonetheless, increasing industry and regulatory expectations are driving the need to extend ACC functionality across wider and more clearly defined speed domains. Modern ACC systems are further evolving to recognize and respond to various road features, including traffic lights, STOP signs, intersections, curved road segments, and roundabouts—an expanding set of scenarios enabled by multi-sensor fusion and map integration using standard definition (SD) and high definition (HD) maps. Regulatory frameworks are increasingly addressing these map-based functionalities. This paper investigates the interaction between map-based functionalities and traditional ACC behavior, specifically examining how map integration enhances ACC responsiveness to critical scenarios. Due to the wide variety of possible cases, this study focuses on stationary vehicle encounters, recognized as the most challenging and safety-critical scenario, particularly at higher speeds. Simulation studies are conducted to evaluate the impact of map-based augmentation on ACC performance, with results demonstrating performance improvements. For instance, at 50 mph on straight roads, the ego vehicle safely stopped ~4 meters from the target stationary vehicle using map-based anticipatory braking, compared to less than 1 meter with traditional ACC. These findings highlight the extended operational capability and safety benefits offered by the proposed approach, even beyond conventional speed limits.

Awathe, Arpit, Patel, Darsh, Mathur, Dhruv, Raut, Abhinandan Vijay

Integration and Validation of Adaptive Cruise Control across Different Modes

2026-01-0076

4/7/2026

This paper presents the integration and validation of Adaptive Cruise Control (ACC) algorithms on a student-team-developed vehicle as part of the U.S. Department of Energy EcoCAR EV Challenge. The competition provided each team with a 2023 Cadillac Lyriq, which was modified to an all-wheel-drive configuration and re-architected to support the development of SAE Level 3 autonomous features including Adaptive Cruise Control (ACC), Automatic Intersection Navigation (AIN), Lane Centering Control (LCC), and Automatic Parking (AP). The scope of this paper, however, is limited to the development, implementation, and validation of a Level 2 longitudinal ADAS function. Higher-level automation requirements such as Operational Design Domain (ODD) definition and Driver Monitoring System (DMS) enforcement are addressed at the vehicle architecture and competition level but are not the focus of this work. The major contribution of this work is the development of ACC with Vehicle-to-Infrastructure (V2I) integration, highlighting the end-to-end implementation of the ACC algorithm and its interaction with key actuation systems in the modified vehicle architecture. The ACC algorithm encompassed multiple applications: conventional cruise control to maintain speed, adaptive cruise control to respond to a lead vehicle, and initial deceleration handling for intersection navigation in a single straight lane. By implementing a unified algorithm, transitions between these modes were smooth and more efficient compared to developing separate algorithms for each application. Track-based testing and calibration were conducted to validate these modes under real-world scenarios, ensuring safe operation while addressing the challenges of blended actuation. Multiple track tests were used to measure stopping distances at intersections for different entry speeds, evaluate controller performance during different driving scenarios, and identify system limitations. Results demonstrated that the controller maintained steady-state speed error within +/- 1 km/hr, preserved a minimum following distance of 8 m at a complete stop, and limited acceleration within +/- 2 m/s2 to support driver comfort. The work demonstrates the progression from simulation to real-world deployment using an empirical approach to system-level validation of ACC with V2I integration. The findings provide insights into calibration methodology, mode transition, and the benefits of a unified control framework for advancing software-defined vehicle features.

Gupta, Ishika, Estrada, Tyler, Tambolkar, Pooja, Midlam-Mohler, Shawn

Energy-Efficient Predictive Cruise Control in Heavy-Duty Electric Trucks with Optimization of the Penalty Factor as a Function of Payload

2026-01-0042

4/7/2026

Heavy-duty Class 8 battery electric trucks not only offer the potential to significantly reduce greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance energy and freight efficiency, Predictive Cruise Control (PCC) algorithms can be developed that generate optimal acceleration profiles for the vehicle by minimizing a cost function which combines both energy consumption and deviation from the desired velocity. A critical component of the cost function is the penalty factor, which governs the tradeoff between energy use and travel time, which are two conflicting objectives in freight logistics. Selecting an appropriate penalty factor is essential, as freight deliveries are time sensitive, but minimizing energy consumption remains a priority. Moreover, variations in payload significantly affect vehicle dynamics and energy usage, making it critical to adapt the penalty factor to different payload conditions and maintain consistent performance. This study presents a method for optimally selecting the penalty factor for various payload scenarios. A validated powertrain simulator which is calibrated using data from an actual electric truck, was used to conduct 100 simulations across a spectrum of payloads, from no load to fully loaded. The resulting discrete search space of energy and time was used to perform a brute-force (exhaustive) search to determine the optimal penalty factor for each scenario. The proposed algorithm incorporates adjustable weightings of the penalty factor for energy and time preferences. This allows flexibility for the driver or fleet operator to prioritize either objective. The results demonstrate that using a fixed penalty factor is suboptimal for heavy-duty electric trucks. In contrast, the optimal selection of the penalty factor significantly improves consistency across different payloads. A reduction of the variation in travel time to within approximately 4% across all loading conditions was observed. This work shows the importance of adaptive penalty tuning in PCC for real-world deployment in freight applications, ensuring both energy efficiency and timely deliveries under varying payload demands.

Safder, Ahmad Hussain, Villani, Manfredi, Wang, Eric, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer

Dynamic Programming–Based Fuel-Saving Predictive Cruise Control

12-09-03-0022

4/2/2026

This article investigates the optimization problem of fuel economy for heavy-duty commercial vehicles. A Dynamic Programming–Based Fuel-Saving Predictive Cruise Control (DP-FSPCC) method is proposed, which is based on the Bellman optimality principle and uses the cost function to evaluate the optimal feedback control gain, thereby improving the fuel economy of heavy-duty commercial vehicles on complex roads with varying slopes. To address the issues of low accuracy in road feature representation and poor adaptability to different driving conditions in existing slope reconstruction algorithms, the road ahead is dynamically segmented for high-precision processing by integrating ADASIS (Advanced Driver Assistance Systems Interface Specifications) map information with significant turning point detection and dynamic sensitivity analysis. An engine fuel consumption mapping model based on local gradient information is established to provide an accurate cost function for dynamic programming. Furthermore, a feedforward optimization mechanism based on slope classification is proposed. This mechanism adopts a differentiated cost function weight design strategy for different road conditions, making the control strategy more in line with actual driving experience, effectively reducing the computational complexity of dynamic programming and improving the real-time performance and optimization efficiency of the algorithm. Finally, through numerical simulations and real-vehicle tests on highways, the effectiveness and superiority of the proposed method are verified.

Jin, Dapeng, Shuai, Yue, Wu, Xin, Jia, Tong, Qiao, Zhiyuan, Chang, Shiwei, Mu, Tong

Steering Automation for Lane Keep Assistance

2026-28-0117

2/12/2026

The design of advanced driver-assistance systems (ADAS) is essential to improve the safety and autonomy of rear wheel driven four-wheel vehicle in harsh conditions. This work introduces the design and development of a steering automation system for Lane Keep Assistance (LKA) in an rear wheel driven four-wheel vehicle with a parallel steering system. The system utilizes an ArduCam module to take real time images of the ground in front, and these are processed via machine learning techniques on a Raspberry Pi in order to identify lane edges with great precision. The corrective steering maneuvers are carried out by a motorized steering actuator based on the visual data after processing, and an encoder that is built into the actuator constantly tracks the steering angle and position. This closed-loop feedback affords accurate, real-time corrections to ensure lane discipline without driver intervention. Extensive calculations for steering effort, torque, and gear design confirm the system's mechanical viability. Combining low-cost vision sensing, embedded machine learning, and encoder-based feedback control, this project presents a viable and flexible solution for Lane Keep Assistance in rear wheel driven fourwheel vehicle, paving the way for safer, more autonomous off-road driving.

A R, Arundas, Sadique, Anwar, Rafeek, Aayisha

Dynamic Integration of Bilateral and Adaptive Cruise Control for Intuitive Vehicle Behavior and Congestion Mitigations

2026-26-0037

1/16/2026

Bilateral Cruise Control (BCC) is a new concept that has been shown to reduce traffic congestion and enhance fuel/energy efficiency compared to Adaptive Cruise Control (ACC). BCC considers both lead and trailing vehicles to determine the ego vehicle’s acceleration, effectively damping any disturbance down the vehicle string and reducing possibilities for congestion. Despite the advantages demonstrated with BCC, one major limitation is its non-intuitive behavior, which stems from the fact that the BCC reacts not just to the lead vehicle but also to the trailing vehicle’s movement. This paper identifies key issues with BCC control and proposes solutions that retain the benefits of BCC while maintaining intuitive behavior. Specifically, a novel switching strategy is proposed to switch between ACC and BCC control modes by critically analyzing the driving conditions. The proposed system ensures acceptable driving behavior with predictable braking and acceleration, resulting in an intuitive and smooth traffic flow. Through seamless integration of ACC and BCC, the system can prevent traffic congestion problems while closely aligning with human driving expectations.

A, Arya, A, Aishwarya, D, Vishal Mitaran, M, Senthil Vel, Kumar, Vimal

Crowdsourcing Indian Traffic Scenarios for ADAS Development and Testing

2026-26-0046

1/16/2026

In the Indian context, introduction of ADAS can play a positive role in improving road safety by assisting the driver and preventing unsafe driver behaviour. Technologies like Automated Emergency Braking (AEB), Lane Keep System, Adaptive Cruise Control, Driver Drowsiness Detection, Driver Alcohol detection etc., if deployed safely and used in a safe manner can help prevent many of the current road deaths in India. Safe deployment and safe use of such ADAS technologies require the systems to operate without failure within their operational design domains (ODD) and not surprise the drivers with sudden or unpredictable failures, to help develop their trust in the technology. As a result, identifying test scenarios remain a key step in the development of Advanced Driver Assistance Systems (ADAS). This remains a challenge due to the large test space especially for the Indian context due to the unpredictable traffic behaviour and occasional road infrastructure. In this paper, we introduce a novel open-access crowd-sourcing public platform, Safety Pool™ Studio, to enable crowdsourcing of traffic scenarios in the Indian context. Safety Pool™ Studio platform enables any member of the public or the road traffic ecosystem (e.g. traffic police, local authorities, academia etc.) to create a traffic scenario using a graphical interface, like a LEGO making exercise. This would enable the users to share their real-life experiences of traffic scenarios in a simple, accessible and inclusive manner and contribute to a global pool of traffic scenarios in the Indian context. Safety Pool™ Studio provides multi-language support for India’s regional languages like Hindi, Bengali, Tamil, Marathi, Punjabi, Kannada, Telugu, Gujrati among others. Safety Pool™ Studio has been developed in a way the graphical scenarios can automatically be converted into programmatic description of scenarios for traditional simulation-based testing of ADAS.

Serry, Hamid, Dodoiu, Tudor, Alakkad, Fadi, Zhang, Xizhe, Khastgir, Siddartha, Jennings, Paul

Adapting ADAS to Indian Driving and User Needs

2026-26-0045

1/16/2026

The penetration of ADAS in automotive markets is increasing rapidly. However, their effectiveness and acceptance are significantly influenced by regional driving behaviours and infrastructure. This study explores the interaction between naturalistic driver behaviour in India and the operational characteristics of ADAS systems (FCW, ACC, LCF and BSD) with focus on cars. Using real-world driving data collected from Indian roads, the research aims to highlight the divergence between ADAS design assumptions often based on structured Western traffic environments and the complex, dynamic nature of Indian traffic, characterized by frequent human negotiation, informal road practices, and different vehicle types. The study characterizes multiple driver’s driving pattern through naturalistic driving and ADAS systems behaviour in corresponding situations, notably how they adapt to unstructured Indian scenarios such as lane ambiguity, pedestrian unpredictability, traffic flow unpredictability and frequent road encroachments. The study also aims to identify gaps in ADAS system performance and Indian drivers’ expectations by capturing customer’s voice, underlining the need for context-aware ADAS development tailored to emerging Indian markets. The paper concludes with recommendations for enhancing ADAS usability, safety, and localization strategies in India.

Sankpal, Krishnath Namdev, Magar, Akshay, Khot, Ankush, Kulkarni, Alok, Perez, Marc

A Centralized Framework for Sustaining Safety-Critical Vehicle Systems Through Periodic Servicing

2026-26-0003

1/16/2026

As vehicles are becoming more complex, maintaining the effectiveness of safety critical systems like adaptive cruise control, lane keep assist, electronic breaking and airbag deployment extends far beyond the initial design and manufacturing. In the automotive industry these safety systems must perform reliably over the years under varying environmental conditions. This paper examines the critical role of periodic maintenance in sustaining the long-term safety and functional integrity of these systems throughout the lifecycle. As per the latest data from the Ministry of Road Transport and Highways (MoRTH), in 2022, India reported a total of 4.61 lakh road accidents, resulting in 1.68 lakh fatalities and 4.43 lakh injuries. The number of fatalities could have been reduced by the intervention of periodic services and monitoring the health of safety critical systems. While periodic maintenance has contributed to long term safety of the vehicles, there are a lot of vehicles on the road which are not serviced regularly. This paper aims to fill this critical gap by proposing a system where the government agencies actively collect and monitor vehicle maintenance data and diagnostics data ensuring that all vehicles on the road undergo mandatory periodic servicing to uphold the integrity of safety-critical systems. This paper concludes by proposing a centralized framework for data sharing and proactive monitoring to ensure the sustained performance of safety-critical systems—ultimately reducing preventable road fatalities and improving overall vehicular safety across India.

HN, Sufiyan Ahmed, Khan, Furqan, Srinivas, Dheeraj

DRIVING SAFETY FORWARD: How simulation and MODSIM accelerate ADAS innovation

25AUTP12_07

12/1/2025

Simulation has become mission-critical for ADAS development. Model-based systems engineering can integrate modeling and simulation from the start of the design process. Advanced Driver Assistance Systems (ADAS) are transforming vehicle safety, acting as the bridge between conventional driving and full autonomy. From adaptive cruise control to emergency braking and blind-spot detection, these technologies rely on a dense network of radar sensors, antennas, electronic control units and software. What unites them is the need for precise functionality under complex real-world situations. Achieving full reliability requires more than testing on the road; it demands a virtual approach grounded in simulation. Simulation has become mission-critical for ADAS development. As new vehicles integrate dozens of sensors into tightly constrained spaces, even subtle design decisions can affect system performance. Radar solutions, in particular, present unique challenges, especially as vehicle surfaces grow more complex and the number of onboard systems increases.

Eichler, Jan

Comparative Analysis of Battery Degradation in Hybrid and Electric Vehicles Through Integrated System-Level Simulation

2025-24-0143

9/7/2025

This study presents the development and validation of a numerical model for a hybrid electric vehicle (HEV) and battery electric vehicle (BEV), with a focus on analyzing battery degradation under various driving scenarios and modes. The proposed model integrates a comprehensive vehicle dynamics framework with a detailed battery model to evaluate the impact of different driving conditions on battery performance and longevity. The vehicle model captures the hybrid powertrain's behavior, including energy management strategies, while the battery model incorporates electrochemical dynamics to predict degradation mechanisms such as capacity fade and resistance increase. Two primary driving scenarios are examined: urban driving, characterized by frequent stops, accelerations, and transitions between aggressive and relaxed driving styles, and long-distance highway driving, where cruising speeds and driving patterns vary. The urban scenario emphasizes the effects of stop-and-go traffic and varying acceleration rates, while the highway scenario explores the influence of sustained high speeds and adaptive cruise control strategies. Additionally, leveraging the validated HEV model, a fully BEV version is developed to compare battery degradation cycles between the hybrid and electric configurations. By simulating these scenarios, the study quantifies the trade-offs between driving behavior, energy consumption, and battery degradation for both vehicle types. The results provide insights into optimal driving strategies to minimize battery wear while maintaining vehicle performance, as well as a comparative analysis of battery lifespan in hybrid versus electric configurations. This work contributes to the growing body of knowledge on vehicle electrification by offering a predictive tool for battery lifespan estimation, aiding in the design of more efficient energy management systems and informing drivers on practices that extend battery life.

Martinez, Santiago, Merola, Simona, Irimescu, Adrian, Bibiloni Ipata, Sebastian

Integration of Safety Standards for a Unified Safety Framework for Automated Vehicles

2025-01-8717

4/1/2025

This paper proposes a structured safety framework tailored for the concept phase of Level 2 and Level 3 automated vehicles, addressing the unique challenges posed by these advanced systems. The framework integrates key principles from ISO 26262 and ISO 21448 to create a safety approach that spans hardware reliability, functional safety, and system performance. Central to the framework is a broad analysis that combines methodologies from System-Theoretic Process Analysis (STPA) and Hazard Analysis and Risk Assessment (HARA). This dual approach enables the identification of potential risks arising from both hardware failures and the intended functionalities of the system. The framework further details a combined specification and design process that aligns the strengths of each standard, ensuring robust sensor architectures and reliable decision-making processes. A case study on Adaptive Cruise Control with Lane Keeping is presented to demonstrate the practical implementation of the framework. The study highlights the complexities of integrating multiple safety standards and highlights areas for improvement as automated vehicle technologies evolve. The results indicate that while the framework provides a solid foundation for safety, further adjustments are necessary to accommodate real-world challenges and the fast-paced evolution of technology.

Sari, Ayse Aysu, Soleimani, Morteza

Evaluating the Impact of Sensor Accuracy on Adaptive Cruise Control Performance: A Simulation Study

2025-01-8063

4/1/2025

Adaptive Cruise Control (ACC) is an advanced driver assistance system designed to manage a vehicle's longitudinal motion. Its effectiveness is critically dependent on the precision of the sensors used. While ACC algorithms are optimized for performance, the overall efficacy of the system is significantly influenced by sensor accuracy and variability. Quantifying the impact of these factors on ACC performance poses a challenge. This paper explores the effects of sensor accuracy on ACC performance through a simulation study that replicates the sensor accuracy and variability observed in realworld vehicles. Additionally, the paper examines potential strategies to mitigate performance fluctuations caused by sensor variability.

Awathe, Arpit, Varunjikar, Tejas, Raut, Abhinandan Vijay, Patel, Darsh

Impact of Connected and Automated Vehicles on Longitudinal and Lateral Performance of Heterogeneous Traffic Flow in Shared Autonomy on Two-Lane Highways

2025-01-8098

4/1/2025

Intelligent transportation systems and connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. Consequently, traditional human-driven vehicles (HDVs), CAVs, and human-driven connected and automated vehicles (HD-CAVs) will coexist on roads for the foreseeable future. Simultaneously, car-following behaviors in equilibrium and discretionary lane-changing behaviors make up the most common highway operations, which seriously affect traffic stability, efficiency and safety. Therefore, it’s necessary to analyze the impact of CAV technologies on both longitudinal and lateral performance of heterogeneous traffic flow. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for different vehicle types, considering human factors and cooperative adaptive cruise control. Then, this paper incorporates CAV penetration rates, shared autonomy rates, and string intensity into a Markov chain model to represent heterogeneous traffic flow. To verify the comprehensive performance of CAVs, this paper introduces both theoretical derivations and numerical simulations. A generalized linear string stability criterion and a fundamental diagram model are introduced to evaluate CAVs’ impact on longitudinal performance. Theoretical results indicate that large-scale deployment of CAVs improves string stability and traffic capacity. Besides, a discretionary lane-changing scenario on two-lane highways, based on the MOBIL model, is established to assess CAVs’ overall performance. Simulations demonstrate that increasing CAV penetration rates enhance traffic speed, efficiency, ride comfort and safety across different traffic densities. Appropriately reducing the proportion of HD-CAVs also benefits the overall performance. A parameter sensitivity analysis further reveals that higher driver compliance rates significantly improve traffic flow while higher weighted coefficients of communication gain achieve better safety by sacrificing other performance. These findings underscore the substantial impact of CAV technologies on mixed traffic flow in shared autonomy and lay foundation for developing control strategies tailored to heterogeneous traffic flow on two-lane highways.

Wang, Tianyi, Guo, Qiyuan, He, Chong, Li, Hao, Xu, Yiming, Wang, Yangyang, Jiao, Junfeng

Modular Dynamometer Testing Framework to Evaluate Energy Impacts of Longitudinal Automated Driving Systems

2025-01-8065

4/1/2025

As longitudinal Automated Driving System (ADS) technologies, such as Adaptive Cruise Control (ACC), become more prevalent, robust testing frameworks that encompass both simulation and vehicle-in-the-loop (VIL) methodologies are essential to ensure system reliability, safety, and performance refinement. Although significant research has focused on ACC algorithm development and simulation testing, existing VIL dynamometer testing frameworks are typically tailored to specific vehicle models and sensor simulation tools. These highly customized approaches often fail to account for broader interoperability while overlooking energy consumption as a key performance metric. This paper presents a novel modular framework for ACC dynamometer testing, designed to enhance interoperability across a diverse range of vehicle platforms, simulation tools, and dynamometer facilities with a focus on evaluating impacts of automated longitudinal control on the overall energy consumption of the vehicle. The platform leverages a standardized interface to facilitate seamless communication between the simulation environment, vehicle control systems, and the dynamometer. This interface synchronizes virtual test environments with physical dynamometer setups, enabling versatile testing configurations and allowing any vehicle to be evaluated within the simulation environment of choice, tested under the driving scenario of choice. The framework’s architecture and the standard interface are detailed, alongside initial experimental results that demonstrate improvements in testing efficiency, flexibility, and a brief energy performance evaluation. This framework was successful in demonstrating the ACC performance, energy consumption performance, and the propulsion system performance on a single vehicle tested under three different scenarios.

Goberville, Nicholas, Hamilton, Kayla, Di Russo, Miriam, Jeong, Jongryeol, Das, Debashis, Ord, David, Misra, Priyashraba, Crain, Trevor

Robust V2X Cruise Control for Class 8 Trucks in the Presence of Traffic Lights

2025-01-8033

4/1/2025

An implementation of a robust predictive cruise control method for class 8 trucks utilizing V2X communication with connected traffic lights is presented in this work. This method accounts for traffic signal phases with the goal of reducing energy consumption when possible while respecting safety concerns. Tightened constraints are created using a robust model predictive control (RMPC) framework in which constraints are modified so that the safety critical requirements are satisfied even in the presence of disturbances, while requiring only the expected bounds of the disturbances to be provided. In particular, variation in the actuator performance under different conditions presents a unique challenge for this application, which the approach applied in this work is well-suited to handle. The errors resulting from lower-level control and actuator performance are accounted for by treating them as bounded and additive disturbances on the states of the model used in the higher level MPC, and the RMPC method is demonstrated to satisfy constraints in the presence of arbitrary bounded disturbances that can be modeled in this way. Simulation results show that these tightened constraints successfully account for error due to low-level control and actuator performance for class 8 trucks. Furthermore, tests were performed on hardware which show the capability for real-time application.

Ellison, Evan, Ward, Jacob, Brown, Lowell, Bevly, David M.

Feasibility of Road Longitudinal Profiles for Automated Vehicles Based on Vehicle Dynamics

2025-01-7150

3/19/2025

With the development and maturity of new generation digital technologies such as artificial intelligence, Internet of Things, and 5G mobile communication, their integration with physical products is becoming increasingly seamless. Automobiles serve as a prime example in this regard. In recent years, automated vehicle (AV) technologies have emerged as a prominent focal point, witnessing an escalating acceptance in the market and a growing number of self-driving vehicles on the roads, existing roads are primarily designed for traditional human-driven vehicles (HVs). Due to the differences in perception between automated systems and human drivers, it is essential to assess AVs' feasibility to current road infrastructure. This paper analyzes the safety and comfort of automated vehicles equipped with adaptive cruise control systems (ACC-AVs) on longitudinal road profiles from the perspective of vehicle dynamics. Firstly, a co-simulation platform integrating PreScan, CarSim, and Simulink software is established, providing a comprehensive environment for simulating AV behavior. Secondly, an evaluation system is developed to assess AV’s feasibility on longitudinal roads, based on safety indicators (rear-end collisions occurrence) and comfort indicators (axial acceleration, vertical acceleration, and vertical acceleration change rate). Lastly, the feasibility of ACC-AVs on existing longitudinal profile roads is simulated and evaluated. The results indicate that, on straight slope section, ACC-AVs may experience rear-end collisions on downhill sections with design speeds below 100 km/h; when safety requirements are met, both uphill and downhill sections exhibit good comfort levels. For the vertical curve section, comfort is also favorable in segments with low design speeds and large vertical curve radii, however, as curve radii decrease or design speeds increase, comfort deteriorates. The findings of this study provide a reference for optimizing highway profile design for AVs.

Li, Zezhou, Cai, Mingmao, Gu, Tianqi, Yu, Bin

Model-Based Vehicle Mass Estimation for Enhanced Adaptive Cruise Control Performance

02-18-02-0009

2/19/2025

This study presents the development and integration of a vehicle mass estimator into the ZF’s Adaptive Cruise Control (ACC) system. The aim is to improve the accuracy of the ACC system’s torque control for achieving desired speed and acceleration. Accurate mass estimation is critical for optimal control performance, particularly in commercial vehicles with variable loads. The incorporation of such mass estimation algorithm into the ACC system leads to significant reductions in the error between requested and measured acceleration during both flat and uphill driving conditions, with or without a preceding vehicle. The article details the estimator’s development, integration, and validation through comprehensive experimental testing. An electric front-wheel drive van was used. The vehicle’s longitudinal dynamics were modeled using D’Alembert’s principle to develop the mass estimation algorithm. This algorithm updates the mass estimate based on specific conditions: zero brake torque, high longitudinal acceleration, minimal slope, adequate speed, minimal wheel slip, and low yaw rate. These conditions ensure accurate mass estimation by minimizing the effects of nonlinearities and external disturbances. Experimental results showed that the mass estimator converges to the actual mass value as more samples are collected. Tests with varying loads confirmed the estimator’s accuracy, achieving a maximum absolute error of 72 kg and a percentage error of 1.71 %. When integrated into the ACC system, the estimated mass improved the control accuracy, especially in acceleration phases, reducing the time to reach the desired speed. Both cruise control and follow control tests, performed on flat and uphill roads, demonstrated that the ACC system with the mass estimator achieved the desired acceleration more accurately than without it. This improved the overall responsiveness and comfort of the ACC system under different driving conditions. The findings highlight the importance of accurate mass estimation for enhancing adaptive vehicle control technologies, representing a significant advancement in ACC systems.

Marotta, Raffaele, D’Itri, Valerio, Irilli, Alessandro, Peccolo, Marco

Software Architecture for Autonomous Vehicles using MATLAB Simulink

2025-28-0190

2/7/2025

Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication between these components is facilitated through the CAN protocol, which ensures accurate and reliable data transfer essential for real-time decision-making. AEB achieves precise emergency braking and enhances driver comfort through the use of PID controllers, while ACC leverages radar data to maintain a safe distance from the vehicle ahead. LD employs the Hough Transform algorithm for accurate road edge detection. Furthermore, a trained neural network within the system identifies and responds to traffic signals, signage, pedestrians and vehicles. The camera interfaces directly with the Jetson Nano, while radar data is shared with the IMU through a dedicated CAN bus. This integrated approach represents a significant advancement in autonomous vehicle control, thus contributing to enhanced safety, comfort and reliability for both drivers and passengers. This software architecture is designed based on aBaja 2024 competition and according to its rules, regulations, requirements and specifications, the controllers and simulations were designed.

Ann Josy, Tessa, Sadique, Anwar, Thomas, Merlin, Manaf T M, Ashik, Vr, Sreeraj

Neural Network-Enhanced Model Predictive Cruise Control for a Heavy Truck with LLM-Assisted Data Collection for Training

2025-01-7033

1/31/2025

Predictive Cruise Control (PCC) is a promising approach for improving fuel efficiency and reducing operational costs in heavy trucks. However, its implementation using conventional Nonlinear Model Predictive Control (NMPC) methods is hindered by computational limitations, often restricting the use of long-horizon slope information. This paper addresses these challenges by proposing a neural network-enhanced slope-adaptive NMPC framework. A Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is employed to integrate long-horizon slope information and dynamically update control parameters, effectively overcoming computational constraints of traditional NMPC. To further enhance efficiency, an automated simulation scheduling system is developed, leveraging Large Language Models (LLMs) and expert knowledge to optimize parameter tuning and streamline data collection, significantly reducing training overhead. Validation on a high-fidelity simulation platform demonstrates that the proposed method achieves fuel savings of 0.53% on long downhill slopes and 0.88% during incline-to-flat transitions at a constant speed of 72 km/h, outperforming fixed-parameter PCC approaches. The automated simulation scheduling system reduces human involvement in data preparation by 60%, highlighting the potential of integrating LLMs into control systems. These results confirm the feasibility and advantages of the proposed method for real-world applications in fuel-efficient heavy truck operations.

Han, Xiao, Song, Kang, Lv, Qing Fang, Zhang, Yi, Xie, Hui

Enhanced Adaptive Cruise Control Strategies Considering Front and Rear Vehicles

10-09-01-0007

1/29/2025

Adaptive cruise control (ACC) systems have increasingly become more robust in adapting to the motion of the preceding vehicle and providing safety and comfort to the driver. But conventional ACC hangs with a concern for rear-end safety in the presence of traffic or aggressive car maneuvers. It often leads to getting dangerously close to the vehicle behind in scenarios where there is less space and time for the rear vehicle to adjust. This research article develops an ACC approach that considers the rear vehicle in addition to the front vehicle, thereby ensuring safety with the rear vehicle without compromising the safety of the front vehicle. Two novel methodologies are devised to enhance the ACC system. The first approach involves utilizing fuzzy logic to associate the inputs with the throttle and brake based on the inference rules within a fuzzy logic controller overseeing both vehicles. The other utilizes a cascaded model predictive control (MPC) system framework that integrates a novel formulation based on vehicle kinematics to devise an optimal reference speed to maintain safe distance with both vehicles, which is fed to the lower level MPC that generates the corresponding throttle and brake values to track the reference speed while ensuring smooth speed transitions. Priority is given to the front vehicle in conflicting situations. Finally, the efficacy of the control strategies is validated using industry-standard simulation software Prescan by assessing the comparative performances of both the control strategies. The results of this study will provide valuable insights into the enhancement of ACC systems by improving the distance safety margin of the ACC-equipped vehicle with respect to the rear vehicle along with the front, ultimately contributing to better throughput of traffic and safer road mobilization.

Sharma, Vishrut, Sengupta, Somnath, Ghosh, Susenjit

A Reinforcement Learning-Based Parameter Tuning Approach for a Secure Cooperative Adaptive Cruise Control System

12-08-04-0033

1/25/2025

Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the controller, observer, and FDI attack estimator, ensuring the safety and security of the developed CACC under varying conditions. We evaluated the performance of the developed controller before and after optimizing parameters, and the results show about a 50% improvement in accuracy of the FDI attack estimation and a 76% enhancement in safe following distance with the optimized controller in each scenario.

Javidi-Niroumand, Farahnaz, Sargolzaei, Arman

Cooperation Dynamics in Multiagent Systems: Modeling Vehicular Cooperation through Game Theory

12-08-03-0026

12/24/2024

Cooperation lies at the core of multiagent systems (MAS) and multiagent reinforcement learning (MARL), where agents must navigate between individual interests and collective benefits. Advanced driver assistance systems (ADAS), like collision avoidance systems and adaptive cruise control, exemplify agents striving to optimize personal and collective outcomes in multiagent environments. The study focuses on strategies aimed at fostering cooperation with the aid of game-theoretic scenarios, particularly the iterated prisoner’s dilemma, where agents aim to optimize personal and group outcomes. Existing cooperative strategies, such as tit-for-tat and win-stay lose-shift, while effective in certain contexts, often struggle with scalability and adaptability in dynamic, large-scale environments. The research investigates these limitations and proposes modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. By analyzing existing cooperative strategies, the research investigates their effectiveness in encouraging group-oriented behavior in repeated games. It suggests modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. Furthermore, it extends to scenarios with exponentially growing agent populations (N → +∞), addressing computational challenges using mean-field game theory to establish equilibrium solutions and reward structures tailored for infinitely large agent sets. Practical insights are provided by adapting simulation algorithms to create scenarios conducive to cooperation for group rewards. Additionally, the research advocates for incorporating vehicular behavior as a metric to assess the induction of cooperation, bridging theoretical constructs with real-world applications.

Nidamanuri, Jaswanth, Sathi, Vaigarai, Shaik, Sabahat

INTEGRATING SENSOR DATA: Selecting an ADAS decision-making process

24AUTP12_01

12/1/2024

Exactly when sensor fusion occurs in ADAS operations, late or early, impacts the entire system. Governments have been studying Advanced Driver Assistance Systems (ADAS) since at least the late 1980s. Europe's Generic Intelligent Driver Support initiative ran from 1989 to 1992 and aimed “to determine the requirements and design standards for a class of intelligent driver support systems which will conform with the information requirements and performance capabilities of the individual drivers.” Automakers have spent the past 30 years rolling out such systems to the buying public. Toyota and Mitsubishi started offering radar-based cruise control to Japanese drivers in the mid-1990s. Mercedes-Benz took the technology global with its Distronic adaptive cruise control in the 1998 S-Class. Cadillac followed that two years later with FLIR-based night vision on the 2000 Deville DTS. And in 2003, Toyota launched an automated parallel parking technology called Intelligent Parking Assist on the Prius.

Ramsey, Jonathon

Study on the Test Procedure of Fuel Economy and CO ₂ Emissions for Vehicles with Adaptive Cruise Control System

2024-01-4308

11/5/2024

With advancements in the development of the automated driving technology, vehicles equipped with such technology can be observed widely in market. However, there is a lack of clarity regarding fuel consumption and emission characteristics with automated driving functions. In fact, it cannot be found that any technical papers that have conducted such an evaluation. Therefore, in this study, it was investigated that an evaluation method to understand fuel consumption and emission characteristics by using the adaptive cruise control (ACC) function. The function is one of the automated driving functions which is widely used and corresponds to Level 2 defined by SAE International (Society of Automotive Engineers). In the approach taken for this study, two vehicles were used as the real driving emission test and the preceding vehicle—equipped with a driving robot—was driven accurately to trace the speed pattern defined by the test cycle. The trailing vehicle was driven using the ACC function, and various performance information of the vehicle was acquired. As the result, it was found that various vehicle performance information with the ACC function was evaluated accurately and the usefulness of the method taken for this study was confirmed experimentally.

Okui, Nobunori

Virtual Identification of Driver-Specific Characteristics for Assisted Lateral Control

2024-01-5090

9/2/2024

Advanced driver assistance systems (ADAS) have become an integral part of today’s vehicle development. These systems are designed to provide secondary support to the driver, but the driver is primarily responsible for the driving task, e.g., lane-keeping assist (LKA). The driving setup and testing of these LKA systems is very time-consuming and usually applied in the car, based on experiences and subjective evaluation. This results in a cost-intensive calibration of the system. An objective-based calibration procedure can increase efficiency. For a targeted calibration of the system, it is necessary to define and identify key performance indicators (KPIs), which are able to describe the secondary support in sufficient detail. Usually, subjective feelings are used to derive KPIs. Vice versa, there are no results on how to design an LKA without any subjective assessment, before the calibration. With this in mind, this paper is focused on filling this unknown aspect by using virtual methods to identify driver-specific KPIs in a free driving scenario. A model sequence feedback control (MSFC) is used for the LKA. In addition, three different driver types (sporty, normal, and gentle) are parameterized, and the driving environment is modeled based on a statistical analysis of rural roads. Based on a design of experiments (DoE), the inputs of the LKA are varied, and the variation is measured using KPIs. The DoE output results in KPIs, which allow driver-specific conclusions to be drawn, in a closed-loop scenario. In addition, principal components (PCs) for the characteristic parameters were generated, and each type of driver can be described with sufficient precision with only three PCs. The drivers have 25 distinguishable KPIs in common. These KPIs aren’t vehicle-specific and can be used at a higher level for the driver-specific closed-loop description and for a model-based LKA calibration.

Baumann, Benjamin, Iatropolous, Jannes, Panzer, Anna, Henze, Roman

Modelling and Analysis of a Cooperative Adaptive Cruise Control (CACC) Algorithm for Fuel Economy

2024-01-2564

4/9/2024

Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model. Simulations were run to model and test the Eco-CACC algorithms with different lead vehicle driving behaviors. The performance of the new Eco-CACC model is then compared to a Proportional Derivative (PD) based Adaptive Cruise Control (ACC) system that aimed to follow the lead vehicle as closely as possible. The PD controller was tuned for nominal performance. The preliminary results show that the proposed CACC model was able to decrease the rate of acceleration and decelerations experienced by the ego vehicle to attain a smooth speed profile that consumed less fuel than its PD-controlled ACC counterpart.

Kavas-Torris, Ozgenur, Guvenc, Levent

An Evaluation of the Bosch Radar-Based Adaptive Cruise Control System on a 2022 Ducati Multistrada V4S

2024-01-2478

4/9/2024

Starting in 2021 Ducati introduced a radar based adaptive cruise control (ACC) developed by Bosch. It utilizes a single radar unit on the front of the motorcycle to detect the presence of vehicles ahead, as well as the separation distance. The system is not an automatic emergency braking (AEB) system but does have similar features. The Ducati ACC system does have limitations, some of which are explored in the subject research. Initial testing was conducted to document the engine braking in each gear. Following initial testing, several tests were performed at high closing speeds of over 100 kph. It was determined that at a closing speed of approximately 100 kph the ACC system would not react to a moving vehicle ahead. Additionally, the system will not react to a stopped vehicle or a “swerve-around” stopped vehicle that suddenly appears. Another series of tests were performed while actively following a vehicle at various speeds, with the front vehicle suddenly slowing to a stop and another series suddenly slowing to an approximate 10-20 kph roll. Lastly, a series of tests were conducted where a sudden lane change was made in front of the motorcycle at a closing speed of roughly 20 kph. In most cases, when the ACC system determines the motorcycle needs to slow; first the throttle is rolled off over an average of 0.6 seconds, then roughly 0.1 seconds later the rear brake application starts, after another approximate 0.2 seconds the front brake application starts. The throttle roll-off time was dependent on motorcycle speed, gear and RPM. Acceleration and acceleration rates (jerk) were explored. The peak deceleration rate achieved was about 0.59g.

Fatzinger, Edward C., Gonzaga, William

On-Road Testing to Characterize Speed-Following Behavior in Production Automated Vehicles

2024-01-1963

4/9/2024

A fully instrumented Tesla Model 3 was used to collect thousands of hours of real-world automated driving data, encompassing both Autopilot and Full Self-Driving modes. This comprehensive dataset included vehicle operational parameters from the data busses, capturing details such as powertrain performance, energy consumption, and the control of advanced driver assistance systems (ADAS). Additionally, interactions with the surrounding traffic were recorded using a perception kit developed in-house equipped with LIDAR and a 360-degree camera system. We collected the data as part of a larger program to assess energy-efficient driving behavior of production connected and automated vehicles. One important aspect of characterizing the test vehicle is predicting its car-following behavior. Using both uncontrolled on-road tests and dedicated tests with a lead car performing set speed maneuvers, we tuned conventional adaptive cruise control (ACC) equations to fit the vehicle’s behavior. We developed specific methods of applying the dedicated tests to separately fine-tune ACC equation components (speed, headway gap, system delays). The results showed a strong alignment between the tuned equation outputs and the observed data. The additional tuning methodologies show promise and invite researchers to explore them further.

Duoba, Michael, Vellamattathil Baby, Tinu, Pulpeiro Gonzalez, Jorge, HomChaudhuri, Baisravan

Enhancing Lateral Stability in Adaptive Cruise Control: A Takagi-Sugeno Fuzzy Model-Based Strategy

2024-01-1962

4/9/2024

Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system. And the input penalty factor of the adaptive cruise control system is designed as a variable parameter for the collaborative control model. On this basis, the longitudinal speed, reciprocal of speed and penalty factor are used as advance variables to design fuzzy rules of the system. And the nonlinear Takagi-Sugeno fuzzy model is established by fuzzifying the local linear model. Then, the vehicle following cruise controller considering the lateral stability is designed by parallel distribution compensation method. Finally, the TruckSim/Simulink co-simulation model was built for testing. The test results show that the proposed controller can improve the lateral stability of the vehicle during the following process, reduce the risk of instability of the vehicle, and improve the overall safety of the automatic driving system.

Yan, Yang, Xin, Yafei, Zheng, Hongyu

A Method for Determining Mileage Accumulation for Robustness Validation of Advanced Driver Assistance Systems (ADAS) Features

2024-01-1977

4/9/2024

Robustness testing of Advanced Driver Assistance Systems (ADAS) features is a crucial step in ensuring the safety and reliability of these systems. ADAS features include technologies like adaptive cruise control, lateral and longitudinal controls, automatic emergency braking, and more. These systems rely on various sensors, cameras, radar, lidar, and software algorithms to function effectively. Robustness testing aims to identify potential vulnerabilities and weaknesses in these systems under different conditions, ensuring they can handle unexpected scenarios and maintain their performance. Mileage accumulation is one of the validation methods for achieving robustness. It involves subjecting the systems to a wide variety of real-world driving conditions and driving scenarios to ensure the reliability, safety, and effectiveness of the ADAS features. Following ISO 21448 (Safety of the intended functionality-SOTIF), known hazardous scenarios can be tested and validated through robustness testing and validation. Unknown hazardous scenarios can be exposed and identified as known hazardous scenarios through accumulated miles. However, determining the mileage needed for acceptance still poses a challenge. This paper presents a potential methodology utilizing the Sequential Probability Ratio Test (SPRT) as acceptance criteria to determine the required mileage accumulation and to evaluate the robustness of the ADAS feature. Selection of the baseline ratio for SPRT depends on the maturity level of the ADAS features and Operational Design Domain (ODD) / Object Event Detection Response (OEDR) coverage. Furthermore, SPRT utilizes the likelihood ratio approach to establish an acceptable, rejection and continuation regions. Number of hours/miles of accumulation and the number of mishaps/hazards are the two main factors for the robustness example shown in the paper. This paper demonstrates how to use these established regions to gain various levels of confidence and prove out the robustness of the ADAS features.

Almasri, Hossam, Fan, Hsing-Hua, Mudunuri, Venkateswara Raju

Design, Prototyping, and Implementation of a Vehicle-to-Infrastructure (V2I) System for Eco-Approach and Departure through Connected and Smart Corridors

2024-01-1982

4/9/2024

The advent of Vehicle-to-Everything (V2X) communication has revolutionized the automotive industry, particularly with the rise of Advanced Driver Assistance Systems (ADAS). V2X enables vehicles to communicate not only with each other (V2V) but also with infrastructure (V2I) and pedestrians (V2P), enhancing road safety and efficiency. ADAS, which includes features like adaptive cruise control and automatic intersection navigation, relies on V2X data exchange to make real-time decisions and improve driver assistance capabilities. Over the years, the progress of V2X technology has been marked by standardization efforts, increased deployment, and a growing ecosystem of connected vehicles, paving the way for safer and more efficient automated navigation. The EcoCAR Mobility Challenge was a 4-year student competition among 12 universities across the United States and Canada sponsored by the U.S. Department of Energy, MathWorks, and General Motors, where each team received a 2019 Chevrolet Blazer from General Motors and was tasked with achieving SAE Level 2 automation to increase the vehicle’s energy efficiency, performance, and connectivity, among other features. Specifically, teams were challenged to add V2I connectivity, requiring the ability to transmit Basic Safety Messages (BSMs) with real-time vehicle information and receive BSMs from other vehicles and SPaT/MAP (Signal Phase and Timing/Map Data) data from Roadside Units (RSUs). This integration of V2X messages (BSM/SPaT and MAP) in cooperative driving systems enhances overall road safety by providing real-time, detailed information about the conditions and intentions of vehicles, fostering a more secure and efficient transportation ecosystem. The Ohio State University’s EcoCAR’s Connected and Automated Vehicles (CAVs) Sub team was able to implement V2I technology successfully using Cohda Wireless MK5 DSRC (Dedicated Short Range Communication) On-Board Unit (OBU) connected with a Mobilemark MGW-303 antenna, combining two 5.9GHz antennas for DSRC with an active GNSS antenna.

Chowduri, Suhrit, Midlam-Mohler, Shawn, Singh, Karun Prateek

Expanding the Use of Vehicle Specific Power in Analysis of Platoon Performance

2024-01-2057

4/9/2024

Platooning is a coordinated driving strategy by which following trucks are placed into the wake of leading vehicles. Doing this leads to two primary benefits. First, the vehicles following are shielded from aerodynamic drag by a “pulling” effect. Secondly, by placing vehicles behind the leading truck, the leading vehicles experience a “pushing” effect. The reduction in aerodynamic drag leads to reduced fuel usage and, consequently, reduced greenhouse gas emissions. To maximize these effects, the inter-vehicle distance, or headway, needs to be minimized. In current platooning strategy iterations, Coordinated Adaptive Cruise Control (CACC) is used to maintain close following distances. Many of these strategies utilize the fuel rate signal as a controller cost function parameter. By using fuel rate, current control strategies have limited applicability to non-conventional powertrains. Vehicle Specific Power (VSP) has shown promise as a metric by which the performance of such controllers can be measured. This study uses VSP to characterize the platooning performance of each vehicle participating in multiple testing campaigns. The data set includes a variety of platoon headway setpoints, two and four-truck platoon configurations, two different testing locations, and experimental results utilizing vastly different platooning control strategies. This effort validates the use of VSP as a platooning characterization metric through the inclusion of a larger data set than prior studies. Additionally, the current work illustrates the broad applicability of VSP as a platooning assessment metric by utilizing multiple vehicles operating in a variety of configurations. VSP also has the potential to serve as a powertrain-independent replacement for the use of fuel rate in a CACC predictive control cost function. In exploration of this idea, several vehicles’ fuel rate and fuel consumption were compared to VSP results. VSP and fuel consumption are shown to possess a direct relationship; but a stronger correlation is found between fuel consumed and the sum of positive VSP. Recommendations for the adaptation of VSP to platoon performance and control are made.

Bentley, John, Stegner, Evan, Bevly, David M., Hoffman, Mark

A Hazard Analysis Approach for Automated Driving Shared Control

2024-01-2056

4/9/2024

Systems-Theoretic Process Analysis (STPA) is being used as a hazard analysis technique within automotive, due in part to its systems engineering viewpoint making it suitable to automated driving feature analysis and with several new and emerging standards and guidelines suggesting its use as one option its familiarity is increasing. Approaches incorporating the human into the STPA Control Structure Diagram (CSD) have been proposed, such as Engineering for Humans: A New Extension to STPA [1]. Such approaches position the human as the top controller in the CSD hierarchy. While placing the human at the top of the CSD is suited to reasoning about supervisory human machine interactions, perhaps in an industrial control setting, we argue that a different approach is needed to address automotive shared control. In an automotive context the driver is integral to vehicle control. Even for vehicle features delivering partial or conditional automation, low level vehicle control tasks may be shared between the driver and the automation. For example, Lane Keep Assistance (LKA) haptic lateral shared control or steer-by-wire input-mixing lateral shared control. In such situations human and machine control is shared between high-level supervisory tasks and lower-level manoeuvring and control tasks. This necessitates modelling the driver differently within the STPA CSD. In this paper we present a vehicle control model and STPA inspired method, which when used together can help the analyst reason about the nature of shared control and potential hazard causes in an automated driving context.

Monkhouse, Helen Elizabeth, Ward, David

Employing a Model of Computation for Testing and Verifying the Security of Connected and Autonomous Vehicles

12-07-03-0020

3/5/2024

Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time. The simulation results show that the resilient controller outperforms the traditional controller in terms of maintaining a safe distance, staying below the speed limit, and the accuracy of the FDI estimation.

Alnaser, Ala Jamil, Holland, James, Sargolzaei, Arman

Driver-Vehicle Interface Considerations for Lane Keeping Assistance Systems

J3048_202402 (Current)

2/27/2024

The purpose of this document is to provide guidance for the implementation of DVI for momentary intervention-type LKA systems, as defined by ISO 11270. LKA systems provide driver support for safe lane keeping operations via momentary interventions. LKA systems are SAE Level 0, according to SAE J3016. LKA systems do not automate any part of the dynamic driving task (DDT) on a sustained basis and are not classified as an integral component of a partial or conditional driving automation system per SAE J3016. The design intent (i.e., purpose) of an LKA system is to address crash scenarios resulting from inadvertent lane or road departures. Drivers can override an LKA system intervention at any time. LKA systems do not guarantee prevention of lane drifts or related crashes. Road and driving environment (e.g., lane line delineation, inclement weather, road curvature, road surface, etc.) as well as vehicle factors (e.g., speed, lateral acceleration, equipment condition, etc.) may affect the operation of the LKA system. As used in this document, the term “LKA” refers to lateral control driver assistance that automatically intervenes to address a lane departure if the driver either does not signal intent to change lanes (e.g., via turn signal activation) and/or does not initiate corrective steering action to prevent the lane departure. LKA is temporary in nature and distinct from lane centering assistance, which performs sustained steering adjustments to maintain the vehicle’s lateral position within a given lane. This document addresses DVI parameters for original equipment LKA systems on light-duty vehicles (i.e., passenger cars and light trucks) with a Gross Vehicle Weight Rating of less than 10000 pounds. This document does not apply to the installation of aftermarket LKA systems or those on motorcycles or medium- and heavy-duty vehicles. This document does not address system or operational requirements for LKA systems, which are specified by ISO 11270. The responsibility for the safe operation of the vehicle always remains with the driver.

Advanced Driver Assistance Systems (ADAS) Committee

Letter from the Focus Issue Editors

14-13-01-0001

2/2/2024

Letter from the Focus Issue Editors

Song, Ziyou, Feng, Shuo, Wu, Guoyuan, Li, Zhaojian

Implementation of Predictive Adaptive Cruise Control Strategy Based on ADAS Map

2023-01-7111

12/31/2023

Heavy vehicles are major fuel consumers in road transportation, and the traditional way to reduce fuel consumption is to reduce weight, resistance, improve mechanical transmission efficiency, and improve engine thermal efficiency. However, European heavy-duty truck companies took the lead in realizing predictive cruise control (PCC) technology on the basis of cruise through intelligent network technology, based on ADAS maps, and achieved good fuel saving effects. In this paper, by studying the fuel consumption characteristics of trucks, designing the dynamic parameters of the load and whole vehicle, the predictive adaptive cruise control (PACC) technology is realized based on the predictive cruise strategy, and the statistics of fuel saving rate under different cruise ratio conditions are analyzed through the big data platform.

Qian, Guoping, Lu, Zhenghua, Tian, Juntao, Liu, Lianfang, Xi, Chong, Zhou, Xiaoying

An Energy-Efficient Merge-Aware Cruise Control Method for Connected Electric Vehicles

14-13-01-0010

12/28/2023

This article presents a merge-aware cruise control method that incorporates vehicle-to-vehicle (V2V) information and aims at improving the energy efficiency of vehicles and reducing speed disruptions of merging traffic during highway merges. During the events of highway merges, the gap between the ego and the preceding vehicle reduces drastically, which can result in sudden braking of the ego vehicle and thus reduction of its energy efficiency. We propose a rather simple cruise control algorithm to eliminate such sudden variations in the gap and velocity with respect to the preceding vehicle during highway merges, thus reducing the large accelerations and braking during such events and thereby improving energy efficiency. The proposed algorithm incorporates future traffic information and has computational requirements similar to adaptive cruise control methods, hence it is real-time applicable. Data used in this article are taken from on-road experiments using a 2020 Tesla Model 3. Simulation results show the efficacy of our proposed control algorithm.

Vellamattathil Baby, Tinu, HomChaudhuri , Baisravan

Passenger Vehicle Lane Departure Warning, Lane Keeping Assistance, and Lane Centering Assistance Systems Test Procedure

J3240_202312 (Current)

12/20/2023

This SAE Recommended Practice establishes a test procedure for the evaluation of lane departure warning (LDW), lane keeping assistance (LKA), and lane centering assistance systems used in passenger vehicles and light trucks. This test procedure does not intend to exclude any particular system or sensing technology. The recommended practice can be used to test the functionality and performance of LDW, LKA, and lane centering assistance systems by assessing their ability to (1) warn (LDW) or control (LKA, lane centering assistance) in response to an unintended lane departure, and (2) the ability to indicate a system disengagement. The human machine interface (HMI) is not addressed herein but is considered in SAE J2808. The recommended practice specifies lane markers to enable lane departure testing, or road edges, to enable testing of road departure mitigation systems. The document is separated into two tiers. Tier One establishes a recommended minimum set of performance criteria for LDW, LKA, or lane centering assistance system operation. Tier Two defines additional tests to provide a measure of the system’s anticipated performance in more challenging environments.

Active Safety and Driver Support Systems Standards Committee

Closet In-Path Vehicle Detection and Recognition Algorithm Based on Camera and Millimeter-Wave Radar Fusion

12-07-02-0010

10/11/2023

The closet in-path vehicle (CIPV) is recognized relying on the detection results for road lane lines in most current ACC system, which may not work well in the poor conditions, for example, unclear road lane lines, low light level, bad weather, and so on. To solve this problem, the article proposes a sensor fusion-based CIPV recognition algorithm independent of road lane lines. First, a robust Kalman filter based on the global coordinate system is designed to fuse the millimeter-wave radar and camera targets. The fusion algorithm can dynamically adjust the covariance matrix of sensor observations to avoid the influence of anomalous observations on the fusion results. Stable detection of targets by the fusion algorithm is the basis of the CIPV recognition algorithm. Then, the CIPV recognition algorithm generates virtual lane lines using the motion parameters of self-vehicle or the driving trajectory of vehicle target and develops a mode switch strategy for virtual lane lines generation based on the driving state of target. This strategy can flexibly switch to the applicable virtual lane lines generation method in different scenarios. Finally, field tests are conducted in typical scenarios to verify the performance of the CIPV recognition algorithm. The results show that the algorithm is able to recognize CIPV stably and accurately without relying on road lane lines.

Yang, Yifei, Zhao, Zhiguo, Yu, Qin, Deng, Yunhong, Li, Wenchang

Considerations for ISO 26262 ASIL Hazard Classification

J2980_202310 (Current)

10/2/2023

This SAE Recommended Practice presents a method and example results for determining the Automotive Safety Integrity Level (ASIL) for automotive motion control electrical and/or electronic (E/E) systems. The ASIL determination activity is required by ISO 26262-3, and it is intended that the process and results herein are consistent with ISO 26262. The technical focus of this document is on vehicle motion control systems. The scope of this SAE Recommended Practice is limited to collision-related hazards associated with motion control systems. This SAE Recommended Practice focuses on motion control systems since the hazards they can create generally have higher ASIL ratings, as compared to the hazards non-motion control systems can create. Because of this, the Functional Safety Committee decided to give motion control systems a higher priority and focus exclusively on them in this SAE Recommended Practice. ISO 26262 has a wider scope than SAE J2980, covering other functions and accidents (not just motion control or collisions as in SAE J2980).

Functional Safety Committee

Vinfast needs to slow down

23AUTP09_11

9/1/2023

Startups are famous for moving quickly. Vinfast may want to slow things down. It was only 2019 when the Vietnamese company built its first cars, rebodied versions of gasoline BMWs that became hits in its home market. Vinfast speedily developed four electric SUVs, including the inaugural VF8 that SAE Media drove in southern California. At the same time, a cargo ship docked near San Francisco, carrying nearly 2,000 VF8s for customers in California and Canada. The next day, Vinfast announced plans to go public via a SPAC merger. And Vinfast recently broke ground on a $4 billion factory in North Carolina, targeting 150,000 units of annual capacity and more than 7,000 jobs.

Ulrich, Lawrence

Battery Electric Vehicle Control Strategy for String Stability Based on Deep Reinforcement Learning in V2V Driving

2023-24-0173

8/28/2023

This works presents a Reinforcement Learning (RL) agent to implement a Cooperative Adaptive Cruise Control (CACC) system that simultaneously enhances energy efficiency and comfort, while also ensuring string stability. CACC systems are a new generation of ACC which systems rely on the communication of the so-called ego-vehicle with other vehicles and infrastructure using V2V and/or V2X connectivity. This enables the availability of robust information about the environment thanks to the exchange of information, rather than their estimation or enabling some redundancy of data. CACC systems have the potential to overcome one typical issue that arises with regular ACC, that is the lack of string stability. String stability is the ability of the ACC of a vehicle to avoid unnecessary fluctuations in speed that can cause traffic jams, dampening these oscillations along the vehicle string rather than amplifying them. In this work, a real-time ACC for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm is also designed in order to achieve the string stability. It relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain energy savings up to 11% comparing the first following vehicle and the Lead on standard cycles and good adaptability to driving cycles different from the training one.

Borneo, Angelo, Miretti, Federico, Acquarone, Matteo, Misul, Daniela

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