Browse Topic: Advanced driver assistance systems (ADAS)

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Optimization of Active–Passive Integrated Evaluation Weighting for Pedestrian Protection2026-01-0577To be published on 04/07/2026
Pedestrians, as the most vulnerable road users, largely determine the severity of traffic accidents through the extent of their injuries. Traditional vehicle safety evaluations have primarily emphasized passive safety. However, with the widespread implementation of active safety technologies such as Automatic Emergency Braking (AEB), passive-only assessments no longer adequately reflect a vehicle’s true pedestrian protection performance.This study employed finite element models of a sedan and an SUV to analyze changes in pedestrian head and lower limb injury metrics before and after AEB intervention, thereby quantifying the associated injury-mitigation benefits. The results show that AEB reduced the maximum head injury value (HIC15) by about 50%, lowering the proportion of severe head injuries from 0.217 to 0.118. Conversely, braking-induced pitch effects increased the proportions of severe femur and tibia bending moment injuries to 0.235 and 0.294, respectively, making them higher
shi, Xinxin, YE, BIN, Liu, Yu, Zhan, Zhenfei, Sun, Bowen, Zhao, yuanyi, zhu, lei
Prediction of Pressure Pulsation in Brake Line Considering Wave Propagation Characteristics of Fluid Contained in Flexible Hose for Automotive Hydraulic Brake System2026-01-0225To 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
Adaptive Driver-Centric ADAS Using Machine Learning for Personalized Assistance2026-01-0063To be published on 04/07/2026
Advanced Driver Assistance Systems (ADAS) are becoming increasingly integral to vehicle safety; however, most current implementations rely on static algorithms with fixed thresholds that do not account for individual driver behavior. This can lead to false positives or unnecessary alerts, thereby reducing user trust and system effectiveness. This paper presents a Driver-Adaptive ADAS framework that leverages machine learning to dynamically tailor assistance logic based on real-time driver behavior and driving style. Instead of treating all drivers uniformly, the system learns patterns such as reaction time, maneuver preferences, and braking tendencies. It adjusts key parameters like Time-To-Collision (TTC) thresholds, Forward Collision Warning (FCW) timings, and Automatic Emergency Braking (AEB) triggers accordingly. A software-based proof of concept is developed using Python and MATLAB, where simulated vehicle and driver behavior data are processed to classify driver types and adapt
Bhargav, Matavalam
A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements2026-01-0013To 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
Holistic Solution of Unified Framework for Integration of Safety Process into Automotive Software Development Process2026-01-0068To be published on 04/07/2026
The rapid advancement of advanced driver assistance systems (ADAS) and automated driving 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) focuses on evaluating and improving software development processes, while ISO 26262-2018 standard ensures the establishment of functional safety in road vehicles. 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
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-0047To be published on 04/07/2026
This paper presents a radar-based dynamic imager pixel activation method designed to optimize image processing efficiency, power consumption, and thermal management in automotive In-cabin monitoring systems (DMS and OMS). By integrating radar sensing with high-resolution cameras, the proposed approach selectively activates image sensor pixels only within radar-identified regions of interest, such as moving occupants or key cabin areas. This selective pixel activation substantially reduces the computational workload and data bandwidth required, as the camera transmits high-resolution data solely for relevant zones while maintaining lower resolution or deactivating pixels elsewhere. The fusion of radar and camera data enables adaptive sensor control that caters to distinct scenarios, including driver gaze tracking and passenger monitoring, thereby enhancing DMS and OMS functionality. The architecture supports mounting configurations such as headliner or rearview mirror installations
Kasarla, Nagender Reddy, Patel, Krunal, Balaraman, Nirmal Kumar
Game-Changing Flow-Centric Traffic Orchestration - A Novel Framework for Stabilizing Mixed-Traffic Dynamics2026-01-0099To be published on 04/07/2026
The recent rise in traffic flow instability and fatal accidents has become a central concern for transportation researchers and policy makers. Hansa Tek Netics (HTN) observes that technology-enabled vehicles - equipped with Advanced Driver Assistance Systems (ADAS) and Adaptive Cruise Control (ACC) - exhibit driving dynamics that diverge from those of human-driven vehicles. This divergence creates interaction conflicts that may amplify disturbances in mixed traffic, thereby contributing to the traffic’s metastability on freeways. To investigate this hypothesis, we developed a flow-centric analytical framework and analyzed the metastability phenomenon. Using both simulation and field observations, we examined how mismatched control philosophies between human and automated vehicles propagate disturbances across traffic streams. Results show that even small differences in dynamics cause positive feedback and trigger instabilities that spread nonlinearly through the flow. We observe that
Schlueter, Georg J.
Translating Crowdsourced Scenarios for use in Simulation Testing for Autonomous Driving Systems2026-01-0057To be published on 04/07/2026
Scenario-based testing is an industry-standard method for building safety cases for Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS). Safety Pool™ Studio is a crowd sourcing platform that enables users to create and submit highly abstract, functional scenarios. Users compose scenarios using a tile-based editor, whereby each tile represents a road segment. Road users can be placed on these tiles and assigned behaviours via a simplified and intuitive simple interface. Prioritising accessibility is key to ensuring broad public participation on the platform. Crowdsourcing scenarios is a novel way of scenario identification, as it focuses on linking public experience into the engineering process and can help identify scenarios which may have been overlooked by engineers. This approach has the potential to increase public trust in the autonomous technology, and ensure that real-world, relevant cases are captured in the testing and training datasets. This paper explores
Dodoiu, Tudor, Serry, Hamid, Alakkad, Fadi, Khastgir, Siddartha, Jennings, Paul
A Study of Tire Grip Performance Evolution Under Dry, Wet and Snow Roads with Tire Wear2026-01-0589To be published on 04/07/2026
This study focuses on investigating how tire grip performance on dry, wet, and snowy road surfaces varies with the different level of tire wear. The new, 50% worn and the end-of-life tires are prepared following the worn tire preparation standards. Additionally, worn tires obtained by real driving conditions in the market are used. Tire grip performances on dry, wet and snowy roads are characterized respectively by using an indoor flat belt machine, an outdoor trailer, and a specially designed snow spin truck. The results demonstrate a grip performance evolution according to the tire wear. The different impact of worn tire preparation whether real driving or artificial is identified especially on snowy road surface. Furthermore, this study investigates the effects of tire reduced tread depth and tread surface roughness of worn tires on each type of road surface. The objective of this study is to enhance the understanding of tire behavior throughout its lifecycle to enable more
Kim, Changsu, Saito, Yoshinori
Optical Performance of ADAS Camera under Thermal-Induced Structural Deformation of Windshield2026-01-0061To be published on 04/07/2026
This study investigates the combined use of optical and mechanical simulation tools to assess the impact of thermally induced structural deformation of automotive windshields on the optical performance of Advanced Driver Assistance Systems (ADAS) cameras. ADAS technologies rely heavily on accurate and high-quality visual data captured through cameras mounted behind the windshield. Any distortion in the optical path, especially due to environmental factors such as heat generated by defrosting systems, can compromise the reliability of these systems. The simulation workflow begins with an ideal-case optical analysis, where the camera’s imaging performance is evaluated assuming a nominal windshield geometry. This serves as a baseline for image clarity, contrast, and overall system functionality. Next, thermal loads originating from the embedded defrost wires are modeled using steady-state thermal analysis. These thermal inputs result in non-uniform temperature distribution across the
Patke, Akshay, Loya, Victor
Impact of Instantaneous Center of Rotation Variability on Path Planning and Performance of Park Assist Systems2026-01-0053To 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
Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control2026-01-0622To be published on 04/07/2026
This paper presents a testing platform for torque vectoring controls utilizing a 10 degree of freedom (DOF) vehicle simulation and a ⅕ scale radio control test vehicle. These vehicle simulations or “Digital Twins” have been widely adopted throughout the automotive industry for 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 Advanced Driver Assist Systems (ADAS). As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a 1/5 scale radio controlled vehicle with torque vectoring capability is designed and assembled. A high fidelity vehicle model is then constructed in the Matlab/Simulink environment to model test vehicle behavior. The test vehicle features independent suspension for
Petersen, Nicholas Conner, Robinette, Darrell
Enhancing LiDAR Performance under Fog with Adaptive Multi-Echo and Feature-Based Filtering2026-01-0011To 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 reflection intensity and relative echo positions. Based on predefined rules, the algorithm identifies the
Kaito, Seiya, Zheng, Shengchao, Fujioka, Ibuki, Beppu, Taro
Real-Time End-to-end Stop Sign and Traffic Light Detection Development and Vehicle Testing2026-01-0014To be published on 04/07/2026
With the rise of end-to-end autonomous driving, visual perception for environmental understanding has become one of the hot topics in advanced driver assistance system development. Most existing end-to-end models only generate executable control command or a planned path, which makes the prediction process hard to interpret. In this research, an end-to-end method for stop-sign and traffic light detection along with depth estimation is built on top of the openpilot open source end-to-end model. Instead of running separate object detection models, we extend the existing openpilot backbone with two lightweight multi-task heads: traffic light detection and classification head and stop sign detection head with confidence and distance output. The modified architecture preserves openpilot’s function by reusing shared features and adding lightweighted residual blocks and feedforward layers. The new outputs are appended to the original openpilot model output, so it will not affect the model
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
The Balance of Illumination and Glare Prevention of Upward Directed Lighting2026-01-0516To be published on 04/07/2026
This study investigates the ongoing challenge of balancing sufficient headlight illumination with the need to prevent glare for other drivers. It focuses on the upper portion of a headlight's beam pattern, which is particularly relevant for lighting overhead signs and the upper portions of vulnerable road users. This study investigates the influence of LED headlamp technology on upward directed lighting and compares illumination of systems with varying mounting heights. This is especially important when it comes to the performance of camera-based pedestrian detection and Automatic Emergency Braking (AEB) systems. Data was collected by testing multiple vehicles over time with pedestrians positioned at two distinct locations relative to the vehicle as well as some discrete points relative to the headlamp and independent of mounting height. The resulting analysis reveals trends, providing valuable insights for predicting optimal lighting strategies in future systems. These strategies may
Allen, Jodi Mary Jean
Personalized Driver Model for On-Ramp Merging Using LSTM2026-01-0525To be published on 04/07/2026
This study develops a personalized driver model for freeway merging, embedding individual driving characteristics into automated longitudinal and lateral control through Long Short-Term Memory (LSTM) networks. Uniform assistance strategies such as adaptive cruise control (ACC) can feel uncomfortable when misaligned with a driver's style. To address this, we focus on the merging maneuver, a safety-critical task requiring anticipation and timing, and examine how roadway and environmental information contributes to model fidelity. Driving data were collected in a high-fidelity motion-base simulator replicating merging sections. In Scenario 1 (Tokyo-Nagoya Expressway, three lanes), seven licensed male drivers in their twenties each completed 15 trials. In Scenario 2 (Metropolitan Expressway, two lanes, lower speed), six drivers followed the same protocol. Model inputs included ego speed, relative distance and speed to the lead vehicle, distances to the end of the acceleration lane and to
Shen, Shuncong, Hirose, Toshiya
Predictive AEB Activation for Secondary Collision Risk Reduction2026-01-0518To 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 indicate that in real-world traffic environments, particularly at intersections with multi-lane arterial roads, complex situations involving multiple vehicles are common, and there exists a significant risk of secondary side collisions occurring immediately after Autonomous Emergency Braking (AEB) activation. This study aims to predict and mitigate such secondary collision risks by proposing a control strategy that adjusts braking timing and stopping position based on the motion information of multiple crossing vehicles. A control algorithm was developed to estimate the likelihood of secondary collisions and determine optimal intervention parameters. The proposed system was evaluated using a sensor-equipped test vehicle and numerical simulations. Results demonstrated a tendency to reduce secondary
Kobayashi, Fumiya, Fukuda, Kentaro, TANI, HIROAKI
Quantifying the Accuracy of Detecting Lateral Movement Onset for a Selection of Bicycle GPS Computers2026-01-0531To be published on 04/07/2026
Bicycle computers and apps record, at minimum, positional data over time, and this data is commonly used in accident reconstruction to inform the behavior of the bicycle and rider prior to an incident in question. This positional data is primarily calculated using the Global Positioning System (GPS) and thus specific accuracy and precision is not well quantified. To improve this understanding when it comes to relative accuracy of lateral movements, a series of controlled tests were performed and recorded with several commercially available bicycle GPS computers and apps and compared against a RaceLogic VBox 3i ADAS with Real-Time Kinematic corrections from a RaceLogic Base Station. The testing consisted of 5 sets of 15 runs: 15 runs riding down the center of a lane, 15 making a 6 ft lateral left movement, 15 making 12 ft lateral left movements, 15 making 6 ft lateral right movements, and 15 making 12 ft lateral right movements. Each test run was ended with hard braking to a stop to
Sweet, David Michael, Bretting, Gerald, O'Brien, Nathan, Wilhelm, Christopher
Comparative Analysis of FCW and AEB Performance: 2020 Jeep Gladiator vs. 2020 Tesla Model 3 Against Slow-Moving Target in Varying Light Conditions.2026-01-0547To be published on 04/07/2026
A total of 67 high speed tests (70 mph and 75 mph) were performed on a 2020 Jeep Gladiator and a 2020 Tesla Model 3 to evaluate and compare the performance of Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) in daytime and nighttime conditions. These tests involves high closing speeds of 60 mph and 65 mph against a slow moving live vehicle target (moving at 10 mph and 15 mph). Consistent and repeatable FCW was observed across all the tests. At a closing speed of 60 mph, the average TTC at the onset of FCW for the Jeep was 2.21 seconds and it was 2.55 seconds for the Tesla. At the same closing speed, the average TTC at the onset of AEB for the Jeep was 1.89 seconds and it was 1.56 seconds for the Tesla. At a closing speed of 65 mph, the average TTC at the onset of FCW for the Jeep was 2.29 seconds and it was 2.37 seconds for the Tesla. At the same closing speed, the average TTC at the onset of AEB was 1.88 seconds for the Jeep and 1.39 seconds for the Tesla. The
Nagarajan, Sundar Raman, Harrington, Shawn
Comparative Analysis of Rear Collision Prevention Systems2026-01-0535To be published on 04/07/2026
Rear Collision Warning (RCW) and Rear Emergency Braking (REB) are key Advanced Driver Assistance Systems (ADAS) designed to prevent collisions with Vulnerable Road Users (VRUs) when a vehicle is reversing. These systems typically rely on a combination of radar, ultrasonic, and camera sensors. This study provides a comparative evaluation of Rear Collision Prevention systems across different vehicle manufacturers. Testing was performed on the 2022 Cadillac Escalade, 2017 Subaru Outback, and 2021 Ford F-150, using both static and moving pedestrian targets. Data collection was carried out with a VBOX data acquisition system, focusing on time-to-collision (TTC) as the primary performance metric. The analysis highlights variations in system effectiveness and provides a brief overview of OEMs' approach to pedestrian safety.
Harrington, Shawn, Takbhate, Shubham Ramraja, Nagarajan, Sundar Raman
Time-of- Flight (ToF) estimation using the Discrete Logarithmic Frequency (DLF) method2026-01-0019To be published on 04/07/2026
When incorporated into the small, curved, and enclosed spaces of contemporary automotive lighting systems, conventional measurement tools like photodetectors, optical sensors, and laser-based systems frequently encounter difficulties. Physical space restrictions, light attenuation along curved surfaces, and the high energy consumption of conventional sensors—particularly in intelligent headlight modules and advanced driver-assistance systems (ADAS)—are some of these limitations. To address these issues, this work presents a novel method for Time-of-Flight (ToF) estimation based on the Discrete Logarithmic Frequency (DLF) approach. The suggested method improves the accuracy and dependability of ToF-based measurements by examining the time-frequency correlations of modulated light signals sent through optical channels in automotive systems. Applications like adaptive beam shaping, vehicle-to-vehicle (V2V) optical communication, and in-lens diagnostics benefit greatly from this. The
Chinni, Venkata Sai Sandeep
An Enhanced Canny-Based Vision System for Vanishing Point Detection in Camera Calibration and Sun Glare Mitigation for ADAS2026-01-0016To be published on 04/07/2026
Abstract— 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
Uppala, Rohit Raj, Kaye, Murali, Zadeh, Mehrdad, TAN, Teik-Khoon
Integrated Longitudinal and Lateral Control for Autonomous Truck Platooning in Curved Scenarios with 5G-V2X Uncertainties2026-01-0168To be published on 04/07/2026
This paper presents a practical feedback control strategy for high-fidelity autonomous truck platooning, addressing the critical challenge of negotiating curved roads under 5G-V2X communication uncertainties. A nonlinear dynamic model is developed, integrating both longitudinal and lateral vehicle dynamics to simulate a five-truck platoon. The proposed hierarchical control architecture consists of two key components: (1) an Adaptive Cruise Control (ACC) system for maintaining safe inter-vehicle distances using a PD controller, and (2) a path-following lateral controller that ensures accurate lane-keeping on curved sections by tracking a predefined road centerline profile. The 5G-V2X sensor uncertainty, a major source of measurement noise in vehicle-to-everytyhing communication, is modeled as additive Gaussian noise. Extensive simulations under a scenario involving a transition from a straight road to a constant-radius curve (R=500m) demonstrate the efficacy of the proposed method
zhou, junyu, Zeng, Dequan, Petitti, Antonio, xiong, min, GAO, LETIAN, Jiang, Xiaomeng, lu, jun, Yu, Yinquan, CARBONE, GIUSEPPE, Hu, Yiming, Nawaz, Aamir, Linh, Nguyen Vu
Mathematical Modeling of Trust Calibration for Human–Automation Safety2026-01-0530To be published on 04/07/2026
Trust between humans and automated vehicles is increasingly recognized as a pivotal factor in achieving safety in advanced vehicles and mobility environments. Poorly calibrated trust—either over-trust leading to complacency or under-trust leading to disengagement—has been identified in numerous incidents involving advanced driver assistance systems and autonomous functions. Yet, most current approaches to managing trust rely on surveys, heuristics, or qualitative assessments, providing little predictive capability for safety engineers and system designers. This paper presents a formal mathematical framework for trust calibration, modeling trust as a dynamic, evolving construct grounded in probabilistic reasoning. We represent trust as a Beta-distributed belief that updates over time through Bayesian inference, with parameters adjusted by observed successes and failures, contextual cues, and system feedback signals. The model also incorporates forgetting and drift functions, allowing
Wen, He, Mounir, Adil
Driving with Influence: Exploring Crash Factors of Automated Systems in Different Roadway Contexts2026-01-0526To 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, the 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 2,263 crash records were extracted—472 for ADAS systems and 1,791 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
Securing the AI-Driven Vehicle: A Hardware-Based Security Framework for Scalable, Trustworthy Autonomy2026-01-0085To 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
Detection of Potholes and Road surface hazards for ADAS/AD/AVS systems in Indian conditions using CARLA2026-01-0024To be published on 04/07/2026
Modern Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD) solutions depend heavily on strong perception capabilities. These systems must not only identify moving objects like vehicles and pedestrians but also detect static road hazards that can impact how safely and comfortably a vehicle can operate. In India, poor road infrastructure—such as potholes, uneven patches, and worn surfaces—presents a serious safety concern. Unlike the more structured and well-maintained roads in developed countries, Indian roads often lack lane markings, have chaotic traffic mixes, and suffer seasonal damage from monsoons. Because of this, detecting road surface issues becomes a crucial part of the perception system, yet it remains underexplored. This research introduces a simulation-driven framework to tackle this problem. Using the CARLA simulator, the framework generates multi-sensor datasets combining camera, LiDAR, and radar data. Potholes and damaged road surfaces are added into
Janarthanan, Shiva Subramanian
Integration and Validation of Adaptive Cruise Control Algorithm Across Different Modes2026-01-0076To 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 Level 3 autonomous features including Adaptive Cruise Control (ACC), Automatic Intersection Navigation (AIN), Lane Centering Control, and Automatic Parking. 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. In the team’s approach, 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
Gupta, Ishika, Midlam-Mohler, Shawn, Estrada, Tyler, Tambolkar, Pooja
Energy-Efficient Predictive Cruise Control in Heavy-Duty Electric Trucks with Optimization of the Penalty Factor as a Function of Payload2026-01-0042To be published on 04/07/2026
Heavy-duty Class 8 battery electric trucks not only offer a significant reduction in greenhouse gas (GHG) emissions compared to conventional diesel trucks but can also provide significant savings in fuel costs. To further enhance the 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 trade-off 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
Safder, Ahmad Hussain, Villani, Manfredi, Wang, Eric, Khuntia, Satvik, Nelson, James, Meijer, Maarten, Ahmed, Qadeer
A Study on TSC(Technical Safety Concept) design and verification in EMB(Brake By Wire) Systems2026-01-0095To be published on 04/07/2026
The Electro-Mechanical Brake (EMB) system, a dry-type Brake-by-Wire technology, represents a significant advancement in eco-friendly and intelligent vehicle braking systems. Unlike conventional hydraulic brakes, EMB eliminates fluid-based components by directly controlling friction braking through electrical actuators installed at each wheel. This not only reduces environmental impact but also enhances system responsiveness and integration with advanced driver assistance systems (ADAS). The EMB architecture comprises six controllers: a Main Center Control Unit, a Backup Center Control Unit for redundancy, and four Wheel Control Units. These controllers communicate seamlessly using multiple vehicle communication protocols, including CAN, Ethernet, and FlexRay. This distributed control structure is essential for achieving compliance with ISO 26262, the international standard for functional safety in road vehicles. As EMB systems become more complex, they introduce new safety risks that
KIM, Dokun
AI-Enhanced Functional Safety in ADAS Controllers: Predictive Fault Management under ISO 262622026-01-0036To be published on 04/07/2026
Ensuring ISO 26262 functional safety in advanced driver assistance systems (ADAS) is increasingly complex as these platforms adopt artificial intelligence for perception and control. Traditional safety mechanisms are deterministic, but AI introduces non-determinism that challenges verification and validation. This paper presents a predictive fault management framework that enhances functional safety in ADAS controllers using AI-driven models. Real-time vehicle and sensor data are processed through machine learning algorithms that forecast hardware and software faults before they escalate into hazardous conditions. These predictive insights are coupled with ISO 26262 safety mechanisms to enable adaptive diagnostics, fault isolation, and recovery strategies. Hardware-in-the-loop (HIL) validation demonstrates up to a 25% improvement in diagnostic coverage compared to conventional monitoring, while maintaining ASIL-D compliance and real-time performance. By showing how AI can complement
Abdul Karim, Abdul Salam
Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation.2026-01-0050To 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
Evaluation of PAEB Performance of a GM vehicle using EDR and Ground Truth Data2026-01-0054To 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
Enhancing Traditional Adaptive Cruise Control through Map-Based Feature Integration: A Performance evaluation Study on Stationary Vehicle Response at High Speeds2026-01-0056To 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
Navigation Integrated End-to-end Path Planning Development and Vehicle Testing2026-01-0032To 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 and lighting conditions, and it shows improved route adherence in vehicle testing. The proposed model is validated in a comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
Study of Automatic Emergency Braking Effects on Belted Human and Surrogate Passengers2026-01-0556To be published on 04/07/2026
The influence of modern Automatic Emergency Braking on the head and neck behavior of the occupants in a vehicle is an emerging area of study. Occupant kinematics and kinetics were evaluated using a vehicle equipped with a pedestrian Automatic Emergency Braking (AEB) system. The vehicle was tested in several different scenarios from speeds of nominally 15 through 50 mph. Two instrumented 50th percentile male Hybrid-III Anthropomorphic Test Devices (ATD) were positioned in the right front and right rear seats of the SUV, while minimally instrumented human volunteers were seated in the left front and left rear seats. Displacement transducers and video analysis were utilized to capture the kinematics of each occupant. The results of this study indicate that AEB only events with the subject vehicle did not result in any ATD or volunteer motion that would have placed the occupants out of position (OOP) had an impact event occurred immediately following the AEB event. This means that when
Bartholomew, Meredith, Dahiya, Akshay, Russell, Caleb, Morr, Douglas, Castro, Elaine, Nguyen, An
Steering Automation for Lane Keep Assistance2026-28-0117To be published on 02/12/2026
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 Vehicles2026-28-0103To be published on 02/12/2026
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
Accidental Dooring Warning and Assist System2026-28-0053To be published on 02/12/2026
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
Development of Advanced Rider Assistance System (ARAS) for Electric Motorcycles: A Level-1 Collision Avoidance and Emergency Braking Prototype Using Ultrasonic Sensors2026-28-0118To be published on 02/12/2026
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 Mobility2026-28-0113To be published on 02/12/2026
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
Automotive Engineering: February 202626AUTP02To be published on 02/05/2026
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 Operation10-10-02-00152/2/2026
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
SAE International’s Dictionary of ADAS and Connected VehiclesR-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
Algorithm Development for AI Driven Path Planning Using Augmented Reality for Navigating in Indian Complex Road Scenarios2026-26-00381/16/2026
The road infrastructure in India has complex navigational challenges with most of the road unstructured especially in rural areas. Decision-making becomes a challenge for drivers in unpredictable environments such as narrow roads, flooded roads and heavy traffic. In this paper, an Augmented Reality based ML-Algorithm for Driver Assistance (ARMADA) has been proposed that improves awareness to safely maneuver in these conditions. The methodology for development and validation of this Augmented Reality (AR) based algorithm contains multiple steps. Firstly, extensive data collection is conducted using real time recording and benchmark datasets like Berkeley Deep Drive (BDD) and Indian Driving Dataset (IDD). Secondly, collected data are annotated and trained using an optimal machine learning (ML) model to accurately identify the complex scenario. In third step, an ARMADA algorithm is developed, integrating these models to estimate road widths, detect floods and provide seamless driver
Anandaraj, Prem Raj, Sivakumar, Vishnu, Thanikachalam, Ganesh, L, Radhakrishnan, Motoki, Yaginuma, Selvam, Dinesh Kumar
Market Research on Actual User Experience and Efficacy of ADAS Features in Indian Driving Conditions2026-26-00351/16/2026
Advanced Driver Assistance Systems (ADAS) have become increasingly prevalent in modern vehicles, promising improved safety and reducing accidents. However, their implementation comes with several challenges and limitations. The efficacy of these systems in diverse and challenging road conditions of India, remains as a concern. For deeper understanding of the ADAS feature related concerns in Indian market due to the factors such as unique road conditions, traffic situations, driving patterns, an extensive study was done throughout Indian terrain. The functionality and performance of different ADAS features were evaluated in the real-world scenarios. The objective data of the observations and occurrence conditions were captured with help of data loggers & camera setups inside the vehicle. This research paper represents a comprehensive study on the challenges faced by user while using ADAS enabled cars in Indian road conditions. We captured the performance data of various ADAS features
Kumbhar, Prasad Uttam, Pyasi, Praveen
A Centralized Framework for Sustaining Safety-Critical Vehicle Systems Through Periodic Servicing2026-26-00031/16/2026
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
HN, Sufiyan Ahmed, Khan, Furqan, Srinivas, Dheeraj
Dynamic Integration of Bilateral and Adaptive Cruise Control for Intuitive Vehicle Behavior and Congestion Mitigations2026-26-00371/16/2026
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
A, Arya, A, Aishwarya, D, Vishal Mitaran, M, Senthil Vel, Kumar, Vimal
Unlocking the Potential of In-Vehicle Edge AI2026-26-06861/16/2026
Edge Artificial Intelligence (AI) is poised to usher in a new era of innovations in automotive and mobility. In concert with the transition towards software-defined vehicle (SDV) architectures, the application of in-vehicle edge AI has the potential to extend well beyond ADAS and AV. Applications such as adaptive energy management, real-time powertrain calibration, predictive diagnostics, and tailored user experiences. By moving AI model execution right into edge, i.e. the vehicle, automakers can significantly reduce data transmission and processing costs, ensure privacy of user data, and ensure timely decision-making, even when connectivity is limited. However, achieving such use of edge AI will require essential cloud and in-vehicle infrastructure, such as automotive-specific MLOps toolchains, along with the proper SDV infrastructure. Elements such as flexible compute environments, deterministic and high-speed networks, seamless access to vehicle-wide data and control functions. This
Khatri, Sanjay, Sah, Mohamadali
Crowdsourcing Indian Traffic Scenarios for ADAS Development and Testing2026-26-00461/16/2026
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
Serry, Hamid, Dodoiu, Tudor, Alakkad, Fadi, Zhang, Xizhe, Khastgir, Siddartha, Jennings, Paul
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