Browse Topic: Advanced driver assistance systems (ADAS)

Items (1,357)

Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control

2026-01-0622

To be published on 04/07/2026

This paper presents a testing platform for the development of lateral stability control systems in independent motor electric vehicles (EVs). A 10 degree of freedom (DOF) vehicle simulation and a radio control test vehicle are constructed to enable controls validation scalable to full size vehicles. These vehicle simulations, or ‘digital twins’, have been widely adopted throughout the automotive industry due to their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as stability control. As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a high fidelity vehicle model is constructed in the Matlab/Simulink environment. To capture the effect of suspension, the digital twin is capable of

Petersen, Nicholas Conner, Robinette, Darrell

A Study of Tire Grip Performance Evolution under Dry, Wet and Snow Roads with Tire Wear

2026-01-0589

To be published on 04/07/2026

This study focused on investigating how tire grip performance on dry, wet, and snowy road surfaces varied with the different level of tire wear. New, 50% worn, and end-of-life tires were prepared following worn tire preparation standards. Additionally, worn tires obtained under real driving conditions in the market were used. Tire grip performances on dry, wet and snowy roads were characterized respectively by using an indoor flat belt machine, an outdoor trailer, and a specially designed snow truck. The results demonstrated an evolution of grip performance as a function of tire wear. The study identified differences in impact between worn tire preparation methods —real driving versus artificial—particularly on snowy road surfaces. Furthermore, the effects of tire stiffness, reduced tread depth, and tread surface roughness of worn tires were investigated for each type of road surface. The objective of this study is to enhance the understanding of tire behavior throughout its lifecycle

Kim, Changsu, Saito, Yoshinori

Personalized Driver Model for On-Ramp Merging Using LSTM

2026-01-0525

To be published on 04/07/2026

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

Shen, Shuncong, Hirose, Toshiya

Predictive AEB Activation for Secondary Collision Risk Reduction

2026-01-0518

To be published on 04/07/2026

Although the evaluation criteria of New Car Assessment Programs (NCAP) continue to evolve, they still predominantly focus on one-to-one collision scenarios. However, accident analyses based on traffic databases from the National Highway Traffic Safety Administration (NHTSA) in the United States and the Institute for Traffic Accident Research and Data Analysis (ITARDA) in Japan indicate that in real-world traffic environments, particularly at intersections with multi-lane arterial roads, complex situations involving multiple vehicles are likely to arise. Further examination of these crash configurations suggests that AEB activation, depending on the resulting stopping position, may entail a potential secondary collision risk under certain intersection conditions. To mitigate secondary collision risks, this study introduces a Secondary Collision Mitigation Logic (SCM Logic), which estimates Time-To-Intercept (TTI) for multiple crossing vehicles to predict when each vehicle will reach the

Kobayashi, Fumiya, Fukuda, Kentaro, Tani, Hiroaki

The Balance of Illumination and Glare Prevention of Upward Directed Lighting

2026-01-0516

To be published on 04/07/2026

This study examines the ongoing challenge of balancing sufficient forward illumination for vehicle operation with the need to limit glare experienced by other road users. This analysis specifically focuses on the portion of a headlight's beam pattern intensity distribution located above the horizontal plane, which is particularly relevant for lighting overhead signs and the upper portions of vulnerable road users but is also a potential contributor to glare. In particular, the study investigates how the adoption of LED headlamp technology has influenced upward-directed lighting relative to historical halogen beam intensity distributions. Two different comparative analyses were performed within this study. The first analysis was the calculation of intensity on targets positioned at multiple locations relative to the headlamps considering vehicle conditions. The second analysis was performed as at selected discrete points referenced directly to the headlamp and independent of vehicle

Allen, Jodi Mary Jean

Holistic Solution of Unified Framework for Integration of Safety Process into Automotive Software Development Process

2026-01-0068

To be published on 04/07/2026

The rapid advancement of advanced driver assistance systems (ADAS), automated driving and electrification has significantly increased the software content and complexity within modern vehicles. Consequently, ensuring both high process quality and compliance or qualification with functional safety standards becomes critically important. Automotive Software Process Improvement and Capability Determination (ASPICE 4.0) focus on Process quality and Capability Maturity, while ISO 26262:2018 emphasizes engineering guidelines for functional safety and risk mitigation. The efficient integration of the process and standard remains a key challenge due to differences in their objectives, terminologies, and assessment criteria. The misalignment between ASPICE 4.0 and ISO 26262:2018 standard often results in duplicated efforts, rework of work products, and delays in product release schedules. This paper proposes a unified framework to bridge ASPICE 4.0 process areas with ISO 26262:2018 safety

Ravi, Reshma, Easwaramoorthy, Prasad Vignesh, Promise, Dinu

Real-Time End-to-End Stop Sign and Traffic Light Detection Development and Vehicle Testing

2026-01-0014

To be published on 04/07/2026

With the rise of end-to-end autonomous driving, visual perception for environmental understanding has become a key research topic in advanced driver assistance system (ADAS) development. Most existing end-to-end models generate only executable control commands or planned trajectories, making the prediction process difficult to interpret. In this study, we present an end-to-end approach for traffic-light recognition and stop-sign detection built on top of the open-source openpilot framework. Instead of deploying separate object detection networks, we extend the existing backbone with two lightweight multi-task heads: a traffic-light detection and classification head, and a stop-sign detection head with confidence estimation. The modified architecture preserves openpilot’s core driving functionality by reusing shared features and incorporating compact residual and feed-forward layers. The additional perception outputs are appended to the original outputs, ensuring that the model’s

Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin

Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management

2026-01-0047

To be published on 04/07/2026

The increased integration of radar and vision sensors in modern vehicles has significantly improved environmental perception, safety, and automation. Nevertheless, conventional camera modules capture images in fixed, continuous frames, leading to unnecessary data processing, power consumption, and heat generation in the limited space of small sensors. The paper discusses the technology of Radar Based Dynamic Pixel Activation (RDPA); whereby radar data can be used to dynamically activate specific pixels on the camera sensor, optimizing image capture and processing. Through a systematic literature review of peer-reviewed articles published between 2021 and 2025, we examined the literature on radar-camera fusion, adaptive imaging, and sensor design that is efficient in power consumption. The review indicates a research gap that there is no current paradigm that dynamically activates sensor pixels at the hardware level using radar data. We aggregated ten topical studies and proposed a

Kasarla, Nagender Reddy

A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements

2026-01-0013

To be published on 04/07/2026

Embedded vision systems are essential for contemporary applications, including robotics, advanced driver assistance systems (ADAS), and intelligent surveillance; yet they frequently experience diminished image quality due to resource constraints, environmental variability, and inconsistent illumination conditions. Such degradations impact multiple visual attributes—sharpness, contrast, color accuracy, noise levels, and structural similarity—that are critical for reliable perception in safety- and performance-driven domains. This study introduces a comprehensive system-level calibration architecture that integrates three coordinated layers: sensor-level adjustment, firmware optimization, and adaptive software enhancements. At the sensor level, exposure control, gain tuning, and white balance adjustments mitigate luminance imbalance and color shifts under changing light conditions. Firmware optimization leverages image signal processor (ISP) parameters to reduce temporal and spatial

Indrakanti, Rama Kiran Kumar, Vishnoi, Nitin, Kamadi, Venkata

Impact of Instantaneous Center of Rotation Variability on Path Planning and Performance of Park Assist Systems

2026-01-0053

To be published on 04/07/2026

Parking assist systems are among the most widely adopted driver-assistance features in modern vehicles. A key component of these systems is the path planning module, which ensures accurate vehicle alignment within a parking slot while satisfying various constraints such as maintaining slot centering, avoiding collisions in confined spaces, minimizing maneuver count, and achieving the shortest feasible path. Multiple path generation techniques—such as geometric, polynomial-based, and search-based methods—have been developed to enable safe and efficient parking maneuvers. However, most of these approaches rely on the simplifying assumption that the vehicle’s instantaneous center of rotation (ICR) is fixed, typically located on the non-steering axle. In practice, the ICR is not constant and can vary significantly across vehicles due to several physical and kinematic factors, including steering geometry, tire slip characteristics, suspension configuration, and weight distribution

Awathe, Arpit, Patanwala, Abizer, Jain, Arihant, Varunjikar, Tejas

A Cloud-Vehicle Integrated Closed-Loop Testing Framework for Intelligent Chassis

2026-01-0051

To be published on 04/07/2026

Vehicle system testing serves as a critical phase in obtaining road certification for prototype vehicles. While direct road testing with physical vehicles yields the most authentic data, this approach entails significant costs, challenges in reproducing extreme scenarios, and inherent safety risks. In contrast, virtual vehicle-based testing technologies represent advanced simulation methodologies for enhancing development efficiency and quality, effectively mitigating risks associated with complex real-world operating conditions and hazardous physical testing. However, virtual vehicle models often rely on idealized parameters, limiting their ability to reflect real-world dynamics and resulting in lower credibility of test outcomes. Furthermore, as evidenced in current mainstream virtual testing software, environmental simulations predominantly remain confined to the visual domain, with limited direct interaction between dynamic environmental changes and virtual vehicle responses. To

Liao, Yinsheng, Cheng, Qing Hua, Qu, Wenying, Wang, Zhenfeng, Wu, Yan, He, Chengkun, Zhang, Junzhi, Lu, Yukun

A Dynamic Driver Comfort Index for Stabilizing Mixed Traffic Behavior

2026-01-0099

To be published on 04/07/2026

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

Schlueter, Georg J.

The In-Vehicle Connectivity Foundation Powering Autonomous Driving

2026-01-0009

To be published on 04/07/2026

Achieving full vehicle autonomy is not just about adding sensors or compute - it requires a fundamental shift in how vehicles are architected. Autonomous systems rely on higher-resolution sensors, massive processing power, and the ability to fuse data from multiple sources in real time. Centralized in-vehicle architectures, which consolidate computing and enable sensor fusion, place unprecedented demands on connectivity. Precise time synchronization across systems becomes critical, as does advanced control to ensure safe and reliable operation. Any delay or data loss can impact decision-making, making robust, resilient communication links essential. High-performance connectivity is the backbone of this evolution. It must deliver the highest bandwidth to handle massive streams of sensor data, support long-reach connections across the vehicle, and maintain error-free performance even in the most challenging electromagnetic environments. This combination of speed, reach, and reliability

Shwartzberg, Daniel

An Enhanced Canny-Based Vision System for Vanishing Point Detection in Camera Calibration and Sun Glare Mitigation for ADAS

2026-01-0016

To be published on 04/07/2026

Edge detection is fundamental for intelligent vehicle applications, directly supporting ADAS functions such as lane detection, obstacle recognition, and scene understanding. The conventional Canny edge detection method exhibits notable shortcomings, especially in color-image processing, adaptive threshold selection, and preserving edge integrity under noisy conditions. In this study, we present an enhanced Canny edge detection framework tailored for ADAS-oriented intelligent vehicle systems, incorporating a quaternion-based weighted averaging scheme for color preservation, adaptive thresholds derived from gradient-amplitude histograms, multiscale edge localization via scale multiplication, and a novel gravitational-field-intensity operator for improved gradient robustness. Moreover, we extend the method to vanishing-point estimation an essential ADAS capability by performing precise intersection calculations combined with clustering techniques such as DBSCAN and RANSAC. Experimental

Uppala, Rohit Raj, Kaye, Murali, Zadeh, Mehrdad, Tan, Teik-Khoon

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

To be published on 04/07/2026

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

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

Enhancing LiDAR Performance under Fog with Adaptive Multi-Echo and Feature-Based Filtering

2026-01-0011

To be published on 04/07/2026

LiDAR (Light Detection and Ranging) systems are essential for autonomous driving (AD) and advanced driver-assistance systems (ADAS), providing accurate 3D perception of the surrounding environment. However, their performance significantly deteriorates under adverse weather conditions such as fog, where laser pulses are scattered by airborne particles, resulting in substantial noise and reduced ranging accuracy. This scattering effect makes it difficult to detect objects within or behind particulate matter, posing a serious challenge for reliable perception in real-world driving scenarios. To address this issue, we propose an algorithm that combines adaptive multi-echo signal processing with a feature-integrated, rule-based denoising framework to enhance LiDAR performance in noisy environments. The multi-echo approach selectively utilizes meaningful signal returns by evaluating both intensity and relative echo positions. Based on predefined rules, the algorithm identifies the echo most

Kaito, Seiya, Zheng, Shengchao, Fujioka, Ibuki, Beppu, Taro

Study of Automatic Emergency Braking Effects on Human and Surrogate Passengers

2026-01-0556

To be published on 04/07/2026

The influence of modern Automatic Emergency Braking (AEB) on the head and neck behavior of the occupants in a vehicle continues to be an active area of research. Occupant kinematics and kinetics were evaluated using a vehicle equipped with a pedestrian AEB system. The vehicle was tested in several different scenarios with speeds between 15 and 45 mph. Two instrumented 50th-percentile male Hybrid-III Anthropomorphic Test Devices (ATD) were positioned in certain seats of the vehicle, while minimally instrumented human volunteers occupied the remaining seats. Displacement transducers and video analysis were utilized to capture the kinematics of each occupant. The findings of this study indicate that in AEB-only events with belted-occupants, the test vehicle did not result in any occupant motion that would have placed the occupants out-of-position (OOP) had an impact occurred immediately following the AEB event. This means that when evaluating real-world AEB events, it may not be necessary

Bartholomew, Meredith, Dahiya, Akshay, Russell, Caleb, Morr, Douglas, Castro, Elaine, Nguyen, An

Mathematical Modeling of Trust Calibration for Human–Automation Safety

2026-01-0530

To be published on 04/07/2026

Trust calibration is vital for safe human–automation interaction but remains largely qualitative. This study develops multiple quantitative frameworks modeling trust as a function of automation reliability. Four progressive models of binary, linear, triangular, and logistic formalize the calibrated trust zone, defining where human reliance aligns with system performance. The framework corrects major misconceptions: that trust is purely qualitative, that low trust–low reliability states are acceptable, and that overtrust and distrust pose equal risk. It establishes a minimum reliability threshold for meaningful trust and identifies distrust as the safer default in high-risk contexts. A case study on an empirical observation of 32 AI applications plotted in the trust–reliability space confirms the analysis, revealing a consistent distrust tendency where reliability exceeds user confidence and other observations. By quantifying trust through reliability, the study reframes it as a

Wen, He, Mounir, Adil

AI-Enhanced Functional Safety in ADAS Controllers: Predictive Fault Management under ISO 26262

2026-01-0036

To be published on 04/07/2026

Ensuring ISO 26262 functional safety in advanced driver assistance systems (ADAS) is increasingly complex as these platforms integrate artificial intelligence (AI) for perception, decision-making, and vehicle control. Traditional safety mechanisms are largely deterministic, but AI introduces non-determinism, creating challenges for verification, validation, and certification. Real-time vehicle telemetry, sensor outputs, and environmental inputs are processed through machine learning algorithms that forecast hardware and software faults before they escalate into hazardous conditions. These predictions are systematically integrated with ISO 26262 safety measures, enabling adaptive diagnostics, fault isolation, and rapid recovery strategies. The AI model introduces hazards such as data bias, model drift, opaque decision-making, and unsafe automation. A dedicated AI Hazard Analysis and Risk Assessment addresses data quality, validation, monitoring, explainability, and fail-safe mechanisms

Abdul Karim, Abdul Salam

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

2026-01-0042

To be published on 04/07/2026

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

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

Driving with Influence: Exploring Crash Factors of Automated Systems in Different Roadway Contexts

2026-01-0526

To be published on 04/07/2026

Although SAE Level 2 Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been shown to provide some safety benefits, they have largely been constrained to specific driving contexts, namely motorways for ADAS and lower speed roadways for ADS. As more advanced systems are entering the roadways and their operating conditions are expanding, it remains an ongoing challenge to assess the safe operation of vehicles with automation in different roadway contexts and leverage lessons learned from real-world incidents to create safer and more robust systems. As of August 2025, NHTSA’s Standing General Order on Crash Reporting offers systematic data on such incidents, providing at least a cursory overview of where and how they occur. From this source, a total of 1,375 crash records were extracted, 657 for ADAS systems and 715 for ADS systems. Through the application of association rule mining and a novel metric termed influence, patterns in ADAS- and ADS-related

Astle, W. Abram, Haus, Samantha

Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation

2026-01-0050

To be published on 04/07/2026

The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two

Karve, Omkar, Saurav, Saket, Purwar, Prabhanshu

Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems

2026-01-0022

To be published on 04/07/2026

Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software

Indrakanti, Rama Kiran Kumar

Integration and Validation of Adaptive Cruise Control across Different Modes

2026-01-0076

To be published on 04/07/2026

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

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

Evaluation of PAEB Performance of a GM Vehicle Using EDR and Ground Truth Data

2026-01-0054

To be published on 04/07/2026

In order to determine the on-board EDR data recording characteristics of a GM vehicle, a 2023 GMC Sierra Denali was tested in several Pedestrian Automatic Emergency Braking (P-AEB) scenarios. Using a variety of test tools, including the STRIDE robotic platform and its onboard data systems, a GPS/IMU installed in the vehicle, and several camera units, the vehicle was put into collision imminent scenarios in which the crash avoidance systems were actuated. The flags in the EDR data, the order in which EDR events were written, and the correlation between the EDR and data recorded by the aforementioned external acquisition systems were examined for each test case. Testing was done in both forward and reverse scenarios and at low speeds only. These results provide a picture of the current state of the additional data available in current EDRs installed on GM vehicles equipped with P-AEB capability, as well as an insight into the accuracy and meaning of that data which should prove

Bartholomew, Meredith, Arnett, Michael, Guenther, Dennis

Navigation Integrated End-To-End Path Planning Development and Vehicle Testing

2026-01-0032

To be published on 04/07/2026

In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes’ intersections, and it shows improved route performance in vehicle testing. The proposed model is validated in a Comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test results

Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin

High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety

2026-01-0034

To be published on 04/07/2026

Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. Through this framework, CLFs constraints

Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin

MEDAS: A Multi-Dimensional Framework for Vehicle Detection Model Selection

2026-01-0025

To be published on 04/07/2026

Recent years have seen a rapid rise in edge-oriented object detection models, including new YOLO variants and transformer-based RT-DETR. Choosing an appropriate model for vehicle detection, however, remains challenged because common metrics such as precision, recall, and mAP capture only part of the trade-off between accuracy and computational cost. To better support model selection, we introduce the Multi-dimensional Equilibrium Detection Assessment Score (MEDAS), which evaluates detectors across four practical dimensions: performance, balance, efficiency, and adaptability. The framework includes a normalization strategy and adjustable weighting so that evaluations can reflect specific deployment needs, especially in resource-limited settings. Experiments on the MS-COCO vehicle dataset show that while RT-DETR models offer competitive accuracy, they require substantially more computation. In contrast, lightweight YOLO variants provide a stronger balance between accuracy and efficiency

Guo, Bin

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

2026-01-0056

To be published on 04/07/2026

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

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

Data-Driven Calibration of Vehicle Simulations for Accurate Control Response in Development and Testing

2026-01-0058

To be published on 04/07/2026

With the steady increase in autonomous driving (AD) and advanced driver-assistance systems (ADAS) aimed at improving road safety and navigation efficiency, simulation tools have become a critical part of the development process, allowing systems to be tested while mitigating the risk of physical injury or property damage upon failure. Physics-based simulators are central to virtual vehicle development, yet their control responses often differ from real vehicles, potentially limiting the transfer of controllers and algorithms developed in simulation. As these simulations play an important role in the vehicle design and validation process, a critical question is how well their predicted behavior translates to real-world physical systems. This paper presents a calibration framework for an autonomous vehicle platform that learns the motion characteristics of an experimental vehicle and uses that knowledge to correct the actuator response of a simulation model. The model is trained by

Soloiu, Valentin, Sutton, Timothy, Mehrzed, Shaen, Lange, Robin, Zimmerman, Charles, Peralta Lopez, Guillermo

Securing the AI-Driven Vehicle: A Hardware-Based Security Framework for Scalable, Trustworthy Autonomy

2026-01-0085To be published on 04/07/2026

Autonomous driving technology in modern vehicles marks a pivotal evolution in transportation, but it may also introduce system-level vulnerabilities that span from tampering with sensors and interfaces to compromising compute units and communication links. This paper proposes a unified, layered hardware security architecture for AI-equipped automated vehicles. Based on current automotive Ethernet and zonal architectures, it provides end-to-end trust using hardware interface security, accelerated-cryptography, and SRAM PUF-based key provisioning. All security primitives are anchored to a hardware root of trust, delivering cryptographic identity, secure boot enforcement, and trusted key storage across the entire vehicle lifecycle. Our architectural assumptions follow the direction ISO 26262, ISO/SAE 21434 and AI development mandates for next-generation SAE Level 3+ vehicles using zonal Ethernet backbones, Time-Sensitive Networking (TSN), and heterogeneous compute islands (e.g., vision

C Suriyanarayanan, Pav, Iacob, Radu

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

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

Automotive Engineering: April 2026

26AUTP044/2/2026

WCX 2026: Honda's crowdsourced PRMS can improve roads, drivers Proactive Roadway Maintenance System could tap into humanity's altruistic mode. Sue Bai's done the research and will deliver a keynote speech. Precise fit Why precision engineering is defining confidence in next-generation internal combustion engines. How precise GNSS unlocks scalable, cost-effective mapping for ADAS and autonomy The benefits of using the Global Navigation Satellite System and SD maps to keep ADAS/AVs up-to-date. Editorial It's war o'clock, again Supplier Eye Goodbye: A look at the past and thoughts on the future Caterham Project V EV on track for 2027 Joby looks to Toyota for investment and manufacturing Tensor goes with 433 Arms in upcoming Robocar Scout CEO: 160K reservations, no unsolvable Problems Ford announces 48-volt architecture for future electric truck BorgWarner to supply integrated drive and generator for EREV trucks The electric Mercedes G-Wagon: A pricey joy 2026 Audi A6: A sensible but fun

Characterization of Fatal Cyclist Collisions Involving Nine Vehicle Types Traveling at Low and High Speeds in Japan

09-14-01-0005

4/1/2026

To reduce traffic fatalities through vehicle safety measures, particular attention must be given to cyclist-related fatalities. Clarifying the characteristics of hazardous events leading to cyclist fatalities, not only by vehicle speed range but also by vehicle type, is essential and should be based on analyses of real-world accident data. Accordingly, this study aimed to characterize fatal cyclist accidents involving vehicles traveling at low and high speeds in Japan. We used macro accident data from the Japanese Institute for Traffic Accident Research and Data Analysis covering the period from 2013 to 2022. Based on nine vehicle types, we investigated the effects of road type, vehicle behavior, and accident type on cyclist fatalities. Additionally, we identified the five most frequent accident scenarios separately for each low- and high-speed category. At signalized intersections, the proportions of cyclist fatalities involving vehicles traveling at low speeds were higher than those

Matsui, Yasuhiro, Oikawa, Shoko

Characterization of Pedestrian Automatic Emergency Braking Acceleration in Modern Light Vehicles: Potential Implications for Occupant Safety

09-14-01-0007

3/27/2026

The objective of this study was to characterize and compare pedestrian automatic emergency braking (PAEB) pulses in modern light vehicles to understand the loading environment that vehicle occupants are being exposed to during PAEB maneuvers. PAEB tests (n = 8008) conducted using 2018–2023 vehicle model years were analyzed. Pulse, vehicle, and impact characteristics (e.g., jerk, peak acceleration, pedestrian scenario, etc.) were derived from each PAEB test. Two k-means clustering analyses were used to group PAEB pulses with and without target collisions based on their similarity between characteristics. One-way ANOVA and Kruskal–Wallis tests were performed on the PAEB pulse characteristics to examine differences between clusters (p < 0.05). Two non-collision clusters (NC1 and NC2) were identified for PAEB pulses without collisions: NC1 had a statistically significant lower jerk (0.8 ± 0.4 g/s) and peak acceleration (1.0 ± 0.1 g) compared to NC2 (1.6 ± 0.8 g/s and 0.9 ± 0.1 g

Witmer, Maitland, Kidd, David, Graci, Valentina

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

09-14-01-0022

3/20/2026

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

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

Driver Understanding of Yellow Indication Laws and Preferences for Configuration of In-Vehicle Alerts for Dilemma Zones

09-14-01-0006

3/20/2026

Drivers frequently encounter Type II dilemma zones at signalized intersections, where the decision to stop or proceed during the onset of a yellow indication can be ambiguous. Decision-making relies on drivers’ expectations of the yellow change interval duration and behavioral factors. While boundaries of these zones are well studied, less is known about how familiar drivers are with their local yellow indication laws, which vary from state to state, and whether their typical reactions to yellow indications align with the laws. Existing interventions like signal timing adjustments, improved vehicle detection, and advance warning signs reduce the number of drivers caught in dilemma zones but may not reach distracted drivers. In-vehicle alerts tailored to dilemma zone scenarios are a potential solution not yet implemented widely in North America. This study addresses how drivers may interpret these alerts. A web-based survey of 640 licensed drivers in Michigan and Washington (ages 18–85

Anderson, Erika, Jashami, Hisham, Ahmed, Ananna, Hurwitz, David

Accuracy Requirements for the Road Friction Coefficient Estimation of a Friction-Adaptive Automatic Emergency Braking System as a Software Solution for Integrated Chassis Control Systems

10-10-02-0016

3/10/2026

As part of this work, the accuracy requirements for the road friction coefficient estimation of a friction-adaptive automatic emergency braking (AEB) system are determined using a complex, nonlinear vehicle model. The AEB system varies its trigger distance depending on an estimated value of the road friction coefficient. The accuracy requirements are determined at a driving speed of 40 km/h depending on the severity classification of ISO 26262 in the statistically relevant Euro NCAP test scenario with a stationary target vehicle. MATLAB/Simulink is used as simulation software. The permissible estimation error (difference between estimated value and road friction coefficient) is determined by the severity classification S1 (light and moderate injuries). The results show that the positive permissible estimation error (road friction coefficient is overestimated) must not exceed about 30% of the road friction coefficient to comply with the severity classification S1 of ISO 26262.

Ahrenhold, Tim, Wielitzka, Mark, Binnewies, Tomas, Henze, Roman

Human-Like Automatic Braking Algorithm Based on Kinematic Cue: Yield Behavior Analysis and Fuzzy Controller Design

10-10-03-0019

2/25/2026

Pedestrians are among the most vulnerable participants in traffic, particularly when crossing the road. Extensive research has been conducted globally on the yielding behavior analysis of vehicle–pedestrian interaction and the design of automatic vehicle braking systems to mitigate pedestrian casualties. However, few studies have comprehensively addressed lateral risks using implicit kinematic cues in pedestrian–vehicle interactions. Moreover, the design of collision avoidance systems has rarely taken into account driving behavior, along with the pedestrian’s kinematics and crossing behavior. This article presents a human-like automatic braking fuzzy control strategy for pedestrian–vehicle collision avoidance, combining the advantages of professional driver emergency braking behavior and kinematic interaction cues. First, a high-fidelity driving simulator is used to investigate the yielding behavior of pedestrian–vehicle interaction when pedestrians cross the road. Second, the

Zhang, Wenyan, Huang, Xiaorong, Sun, Shulei, Fu, Kairong, Xiong, Qing, Huang, Haibo

Chinese Drivers’ Preference for In-Vehicle Information Display: Insights from a Large-Scale Online Survey

12-09-03-0020

2/20/2026

Integrating intelligent and connected technologies in vehicles has significantly enriched the information environment for drivers, aiding them in making comprehensive driving decisions. However, inadequate information display may lead drivers to miss crucial information or increase their cognitive load, thereby affecting driving safety and user experience. It is essential to study drivers’ preferences for in-vehicle information display, the factors influencing these preferences, and to present information through appropriate modalities and carriers. Drawing on 695 valid questionnaire responses, this study investigates drivers’ preferences for recommendatory, explanatory, alerting, and warning information across three display modalities and six display carriers. A multivariate ordered probability model was further developed to examine the influence of user characteristics on these preferences. The results showed that drivers preferred visual cues over auditory ones, with a selection

He, Gang, Diao, Kai, Luo, Longfei, Xie, Bingjun, Zhong, Yixin, Qi, Jianping

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

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

Intelligent Steering System to Improve Fuel Efficiency in Heavy Duty Vehicles

2026-28-0103

2/12/2026

In commercial vehicles, conventional engine-driven hydraulic steering systems result in continuous energy consumption, contributing to parasitic losses and reduced overall powertrain efficiency. This study introduces an Electric Powered Hydraulic Steering (EPHS) system that decouples steering actuation from the engine and operates only on demand, thereby optimizing energy usage. Field trials conducted under loaded conditions demonstrated a 3–6% improvement in fuel economy, confirming the system’s effectiveness in real-world applications. A MATLAB-based simulation model was developed to replicate dynamic steering loads and vehicle operating conditions, with results closely aligning with field data, thereby validating the model’s predictive accuracy. The reduction in fuel consumption directly translates to lower CO₂ emissions, supporting regulatory compliance and sustainability goals, particularly in the context of tightening emission norms for commercial fleets. These findings position

T, Aravind Muthu Suthan, Mani, Kishore, Ayyappan, Rakshna, D, Senthil Kumar, S, Mathankumar

Development of Advanced Rider Assistance System (ARAS) for Electric Motorcycles: A Level-1 Collision Avoidance and Emergency Braking Prototype Using Ultrasonic Sensors

2026-28-0118

2/12/2026

This paper presents the design, development, and validation of an Advanced Rider Assistance System (ARAS) tailored for electric motorcycles, with a specific focus on a Level-1 collision-avoidance and emergency-braking prototype employing ultrasonic sensing. The study is motivated by the disproportionately high accident exposure of two-wheeler riders and the slow adoption of ARAS technologies relative to the well-established Advanced Driver Assistance Systems (ADAS) in passenger vehicles. The proposed system utilizes front and rear ultrasonic sensors operating at 40 kHz, offering a measurement range of 2 cm to 4 m with ±1% accuracy, and maintaining reliable performance at motorcycle lean angles of up to 30°. Sensor data are processed using an STM32-series microcontroller running a real-time collision-risk estimation algorithm based on obstacle distance and relative velocity. A configurable safety threshold (typically 3 m) initiates a hierarchical warning strategy comprising visual

Deepan Kumar, Sadhasivam, Karu, Ragupathy, Karthick, K N, R, Vishnu Ramesh Kumar, Kumar, V, Manojkumar, R, M, Karthick, M, Rishab

IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility

2026-28-0113

2/12/2026

This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless

Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan

Accidental Dooring Warning and Assist System

2026-28-0053

2/12/2026

Dooring accidents occur when a vehicle door is opened into the path of an approaching cyclist, motorcyclist, or other road user, often causing serious collisions and injuries. These incidents are a major road safety concern, particularly in densely populated urban areas where heavy traffic, narrow roads, and inattentive behavior increase the likelihood of such events. To address this challenge, this project presents an intelligent computer vision based warning system designed to detect approaching vehicles and alert occupants before they open a door. The system can operate using either the existing rear parking camera in a vehicle or a USB webcam in vehicles without such a feature. The captured live video stream is processed by a Raspberry Pi 4 microprocessor, chosen for its compact size, low power consumption, and ability to support machine learning frameworks. The video feed is analyzed in real time using MobileNetSSD, a lightweight deep learning object detection model optimized

C, Jegadheesan, T, Karthi, Gurusamy, Varun Sankar, Balraj, Tharun, Murugaiya, Tamilselvan

Automotive Engineering: February 2026

26AUTP022/5/2026

Qualcomm expands partnerships for more Snapdragons Bosch is ready to bring AI to your vehicle, likely to still be ICE-powered in 2035 Etching for a greener future How chemical etching is helping enable next-gen automotive technologies. Rewriting the engineer's playbook: What OEMs must do to spin the AI flywheel The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. From redundancy to resilience: building smarter safety systems through sensor collaboration ADAS sensor fusion can provide improved and required safety technologies by rethinking the best strategy for allowing a car to sense the world. Open Safety is the shortcut to safer ADAS/AD An open safety stack, shared scenarios, benchmarks, and core validation tools can speed certification, reduce duplicated V&V and build public trust while preserving vendor differentiation. Editorial Robots, physical AI shift the focus at

Dynamic Stability Enhancement during Emergency Obstacle Avoidance under Inappropriate Driver Operation

10-10-02-0015

2/2/2026

In response to the decline in vehicle stability and the resulting safety risks caused by inappropriate driver operations during high-speed emergency obstacle avoidance, a human–machine cooperative control strategy based on driver operation recognition is proposed. The strategy establishes a vehicle controllability boundary by integrating real-time driver inputs with tire adhesion limits, enabling dynamic evaluation of the influence of operations on system controllability and identification of potential inappropriate operations. On this basis, a control authority allocation mechanism is developed, capable of adaptively adjusting to vehicle states and driver operations. By combining road boundary constraints with vehicle stability envelope constraints, the strategy dynamically regulates the steering angle, ensuring vehicle stability while retaining the driver’s effective intentions as much as possible. Unlike conventional path-tracking or single-envelope control approaches, the proposed

Liu, Yangyi, Zhou, Bing, Wu, Xiaojian, Jiang, Xiaokun, Cui, Qingjia

OPEN SAFETY is the shortcut to safer ADAS/AD

26AUTP02_06

2/1/2026

Treat foundational AV safety like seatbelts - make it non-proprietary and universal. An open safety stack, shared scenarios, benchmarks, and core validation tools can speed certification, reduce duplicated V&V and build public trust while preserving vendor differentiation. The bottleneck isn't compute - it's verification. Autonomous features are shipping in more vehicles and markets, but the gating factor is no longer raw compute. It's whether developers and regulators can verify systems against requirements and validate them against real-world operating design domains (ODDs) with confidence and repeatability. Today, many safety-critical components, from scenario libraries to pass/fail criteria, live in proprietary silos. That fragmentation slows regression testing, complicates regulator audits across regions, and duplicates effort across the industry. The result is an expensive, bespoke path to certification for every program and geography.

Musa, Mohammad, Khawaja, Muhammad Zain

PRODUCT BRIEFS

26TOFHP02_10

2/1/2026

SAE International’s Dictionary of ADAS and Connected Vehicles

R-5591/20/2026

The convergence of Advanced Driver Assistance Systems (ADAS) and connected vehicle technologies is ushering in a transformative era of automotive innovation—one that is fundamentally enhancing vehicle safety, efficiency, reliability, and the overall driving experience. SAE International’s Dictionary of ADAS and Connected Vehicles stands as the definitive reference for this rapidly evolving domain, meticulously compiled to clarify and standardize the language shaping modern mobility. Inside, readers will find clear, authoritative definitions encompassing the full spectrum of technologies that enable connected and automated driving—including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communication systems. This comprehensive resource translates complex engineering terminology into accessible language, enabling engineers, researchers, policymakers, educators, and enthusiasts to share a common technical foundation. Reflecting the latest

Quigley, Jon M., Gulve, Amol, Krishnamoorthy, Jayalekshmi

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