Browse Topic: Advanced driver assistance systems (ADAS)

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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
Dodoiu, Tudor, Serry, Hamid, Alakkad, Fadi, Zhang, Xizhe, Jennings, Paul, Khastgir, Siddartha
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
Experimental Correlation of Virtual and Physical Testing for Regulatory ADAS Features: A Roadmap towards Fully Virtual ADAS Validation”2026-26-00481/16/2026
Validation of Advanced Driver Assistance Systems (ADAS), under global regulations is primarily anchored to vehicle tests. While possessing accuracy, validation through physical means is costly, slow, and not scalable in the variety of situations. The development of high-fidelity simulation environments and advanced sensor modelling presents the possibility of shifting regulatory testing for ADAS to virtual platforms. This paper describes an experimental analysis conducted with the aim of full virtual homologation by correlating real world scenarios, test scenarios and virtual vehicle simulated results for the most important ADAS regulatory features. A calibrated vehicle simulator with embedded sensor and actuator models was used to create standard ADAS test scenarios, which were then validated against data collected from a proving ground. Comparison benchmarks included response time for brake application, inter-vehicle spacing, lateral travel deviation, activation of the system, and
Saraswat, Shubham, Vishe, Prashant, Singh, Jitender
Optimizing Collision Warnings: A Shift from Time to Collide to Time to Brake2026-26-00471/16/2026
In automotive safety systems, Time to Collide (TTC) is traditionally used to trigger warnings in auto-emergency braking systems. However, TTC can lead to premature or inaccurate warnings as it is calculated based on the relative speed and distance between the ego and an obstacle. TTC does not consider the vehicle's braking dynamics, such as brake prefill lag which varies across different vehicles, maximum deceleration, and the effectiveness of braking systems and assumes constant speed which may not always be realistic. We propose Time to Brake (TTB) as a more effective parameter for driver warnings. TTB directly relates to the action a driver needs to take—braking. It provides a clear indication of when braking should begin to avoid a collision, whereas TTC only tells us about the possibility of a collision. To calculate TTB we utilize the brake profile, which incorporates both deceleration and system jerk for improved accuracy. The proposed warning time is the sum of variable brake
Singh, Ashutosh Prakash, Kumawat, Himanshu, Gupta, Sara
Bridging Real and Virtual: A Review of Advanced Testing Approaches for ADAS Validation2026-26-05661/16/2026
The rapid advancement and deployment of Advanced Driver Assistance Systems (ADAS) has driven a significant demand for robust and reliable testing methodologies to ensure their safety, functionality, and readiness for real-world implementation. This paper provides a comprehensive review of current practices and emerging techniques in the verification and validation of ADAS, with particular emphasis on X-in the-Loop (XiL) testing frameworks. These methods enable early-stage evaluation by integrating real hardware components with high-fidelity simulation environments, supporting the detection of design flaws under controlled and repeatable conditions. Challenges in sensor integration, including signal injection for radar, lidar, camera, and ultrasonic systems, are discussed, alongside strategies for simulating complex driving scenarios through GPU-based environmental rendering and virtual object generation. Furthermore, we explore the role of virtual test environments in overcoming
W, Arckja
Ocular-Behavioral Metrics for Driver State Classification for Indian Driving Contexts: KSS-Based Evaluation, Conformance and implementation challenges2026-26-06681/16/2026
With the increasing integration of Advanced Driver Assistance Systems (ADAS) in Indian automotive markets, robust Driver Monitoring Systems (DMS) have become critical for road safety. This study focuses on the development and validation of a Driver Distraction and Attention Warning System (DDAWS), tailored to the unique challenges of Indian driving conditions. The DDAWS module, a core component of DMS, leverages a driver-facing high-resolution camera to continuously evaluate driver behaviour. The system architecture is bifurcated into two primary subsystems: the Driver Attention Monitoring module and the Drowsiness Detection module. DMS primarily is divided into two parts, first being the Driver Attention monitoring system, which ensures the driver maintain a proper focus on the road by not being distracted either by the inner and the external environment of the vehicle. This is generally achieved using the head pose monitoring of the driver, post processing which results in an
Verma, Harshal, Kale, Jyoti Ganesh, Karle, Ujjwala
Predicting cut-in events using LSTM based techniques2026-26-06361/16/2026
Highway accidents caused by sudden cut-in and braking events pose critical challenges for driver safety, particularly when traditional Advanced Driver Assistance Systems (ADAS) only react after a cut-in is complete and time-to-collision (TTC) is critically low. This research proposes a predictive system that uses Long Short-Term Memory (LSTM) networks to forecast the probability of cut-in events and potential collisions in advance. By doing so, it enables earlier driver alerts and proactive responses, particularly in scenarios where ADAS may fail to intervene—such as when a slow-moving vehicle in an adjacent lane initiates an abrupt lane change without signaling. The model is trained using a combination of real-world driving data and synthetically generated high-risk scenarios to address data imbalance. Predictive features include relative velocity, lateral velocity, and lane overlap—key indicators of imminent risk. Results indicate an average early warning time of approximately 1.35
Srivastava, Rohan, NAYAK, APOORVA S., Suvvari, Sai Dileep, Satwik, Rahul, Bhattacharya, Abhinov
Dynamic Integration of Bilateral and Adaptive Cruise Control for Intuitive Vehicle Behavior and Congestion Mitigation2026-26-00371/16/2026
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 dampening 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
A, Arya, A, Aishwarya, D, Vishal Mitaran, M, Senthil Vel, Kumar, Vimal
Digital Rack Load Estimation Methodology for Electric Power Steering to Optimize Motor Sizing2026-26-03831/16/2026
Electric Power Assisted Steering (EPAS) systems are integral to modern passenger vehicles, contributing to improved steering performance, enhanced safety, and enabling the integration of advanced driver assistance systems (ADAS). A critical aspect of EPAS design is precise motor sizing, which has a direct impact on steering dynamics, driver input effort, and overall system efficiency. The selection of an appropriate motor is governed by several parameters, including peak rack load, maximum steering speed, electrical supply constraints, thermal limitations, and spatial packaging considerations. This study specifically addresses the accurate estimation of rack force, a primary parameter guiding motor selection and assist control calibration. Previous researchers have explored multiple EPAS motor sizing methodologies such as iterative physical testing and simplified analytical modelling. Out of these, iterative physical testing incurs high resource costs and may hamper the development
Adsul, Sourabh, Iqbal, Shoaib
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
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 82% roads being unstructured according to Ministry of Roads Transports and Highways (MoRTH). Decision-making becomes a challenge for drivers in unpredictable environments such as narrow roads or sometimes flooded roads and heavy traffic. In this paper, an augmented reality (AR) driver assistance algorithm has been proposed that improves awareness to safely maneuver in these conditions.The methodology for development and validation of this AR algorithm contains multiple steps. Firstly, extensive data collection is conducted using real time capture and benchmark datasets like Berkeley Deep Drive (BDD) and Indian Driving Dataset (IDD). Secondly, A machine learning model consisting of the collected data are labelled and trained to rightly identify the complex scenario. In third step, an AR navigation algorithm is developed, integrating these models to accurately detect floods and estimate road widths and provide
Anandaraj, Prem Raj, Sivakumar, Vishnu, Thanikachalam, Ganesh, L, Radhakrishnan, Motoki, Yaginuma, Selvam, Dinesh Kumar
Virtual Validation of ADAS Algorithms Using Driver-in-the-Loop Simulation2026-26-00501/16/2026
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. A comprehensive review of current research trends reveals key areas of focus such as realism of simulated experiences through improved vehicle dynamics models and advanced sensory feedback. The integration of virtual and augmented reality technologies help to create more immersive and informative simulations to improve the cost-effectiveness and accessibility of DIL simulation platforms. By examining the current landscape of DIL simulator use cases, this paper critically focuses on
Sharma, Chinmaya, Bhagat, Ajinkya, Kale, Jyoti Ganesh, Karle, Ujjwala
Microcontroller-based modular mirror for Heavy and Mid commercial vehicles2026-26-00181/16/2026
Existing ICE Mid and Heavy commercial vehicles in the Indian and international market are recording a large number of mishaps due to blind spots and non accessibility of the driver to the opposite side mirror in real-time driving. Non-driver side rear view mirror adjustment creates the need for the driver to get down and adjust the mirror manually/ get support from the co-passenger. The paper proposes a solution for a Microcontroller-based compact mirror adjustment system, which will run with minimal economy and highest efficiency. This will assist drivers in aesthetically and safely monitoring of mirror to check on specific blind spots in day conditions. Mirror will be actuated via a Modular control panel, a Dashboard with audio input or button-operated system based on vehicle type and customer need, along with a quick response mechanism to operate the mirror. This will reduce the prone accidents due to non-visibility by approximately 30%, ensuring enhanced road safety and driver
Jambagi, Vaibhavi Vyankatesh, Gangvekar, Onkar, Bhandari, Kiran Kamlakar
Application framework with multi-tiered approach in validating UI based animation in automotive product.2026-26-05861/16/2026
As part of their market segmentation strategy, each OEM is using UX (user experience) as a crucial aspect for product differentiation. Though there are many parts to UX, the one which would have a profound effect on the user is through animation of real-world aspects on the instrument panel, like falling snowflakes when it’s snowing outside, real time traffic conditions as part of Advanced Driver Assistance Systems (ADAS) or even a unique welcome or farewell message. The unique and realistic nature of such implementations and customizing it to the needs of market segments introduces a lot of complexity in evaluating the correctness of implementation with respect to design. In this paper, we would extensively evaluate the current practices in analyzing the test basis, test environment needs, test data and test methods used in testing animation. However, the primary focus of this paper is to introduce a novel multi-tiered approach towards evaluating animations - where we would introduce
Kandrattha A, Mohammed Azharuddin, Kulkarni, Apoorva
Integrated Quality Assurance Frameworks for Testing Advanced Automotive Systems in the Era of ADAS and E-Mobility2026-26-05681/16/2026
As the automotive industry rapidly transitions toward intelligent, electrified, and connected mobility, ensuring the safety and performance of advanced vehicle systems has become increasingly complex. Technologies such as Advanced Driver Assistance Systems (ADAS), electric powertrains, and software-defined architectures demand robust quality assurance (QA) frameworks capable of addressing the multi-domain nature of modern vehicle platforms. This paper presents an integrated QA methodology that combines traditional testing protocols with model-based validation, digital twin environments, and real-time system monitoring to comprehensively evaluate the reliability and functionality of next-generation automotive systems. The proposed framework focuses on end-to-end traceability from requirements to validation, encompassing hardware-in-the-loop (HIL), software-in-the-loop (SIL), and over-the-air (OTA) testing strategies. Emphasis is placed on simulating complex scenarios for ADAS, battery
KOMANDURI, ARUN SRINIVAS, Srivastava, Anuj
Estimating the Road Friction Coefficient Dynamically using Machine Learning2026-26-06301/16/2026
In autonomous vehicles, it is vital for the vehicle to drive in a manner that ensures the driver is comfortable and does not feel compelled to intervene. Hence, the system must consider important environmental factors, such as the road friction coefficient, which quantifies the grip experienced by the tire on a road. An inaccurate estimation of road friction can lead to safety risks or discomfort for the driver, as the system would be overestimating or underestimating the tire’s grip on the road and the vehicle’s response to control inputs. Therefore, in the context of Advanced Driver Assistance Systems (ADAS), dynamically estimating the road friction coefficient can significantly improve the safety and comfort of autonomous driving functions. However, estimating the friction coefficient dynamically requires complex mathematical modelling of nonlinear relationships that are challenging to solve numerically or analytically. This, coupled with the need for real-time estimation presents a
Rangarajan, Rishi, Sukumar Rajammal, Prem kumar, Singh, Akshay Pratap, Kumaravel, Sujeeth, Kop, Anand, Bharadwaj, Pavan
Methodical Approach to preparedness of India specific automotive database matrix with example of AEB scenarios.2026-26-06511/16/2026
The purpose of this report is to identify systematic approach of formation of India specific automotive database matrix. Section 2 reviews the practices used to prepare automotive dataset catalogue with established pattern to showcase automotive dataset from which appropriate data clusters can be picked up judiciously in order to train ADAS algorithms. Section 3 applies this framework which helps to establish strategy to build a grid in which Indian automotive dataset can be contoured and selection of serviceable data bunches can be picked. This would make sure prompt selection of database aiming model training with valid input. This serves the purpose of implementation and evaluation of varied ADAS levels in India which insist upon good quality of distinguished dataset pertaining to Indian scenarios. Section 4 explains the described approach with the example of AEB scenarios and present appropriate matrix readiness comprising of relevant data objects excluding unnecessary junk data
Behere, Sayali Rajendra, Karle, Manish, Karle, Ujjwala
Establishing a Scalable Virtual Framework for ADAS Validation: Traffic Jam Pilot with Real-World Scene Integration2026-26-05061/16/2026
Advanced Driver Assistance Systems (ADAS) are instrumental in improving road safety and minimizing traffic-related incidents. However, their development and validation processes are resource-intensive, requiring substantial time, cost, and domain-specific expertise. Moreover, real-world testing introduces significant safety challenges. To address these issues, virtual simulation platforms offer high-fidelity environments for the secure and efficient testing of ADAS functions. This research presents a virtual validation framework for a Traffic Jam Pilot (TJP) algorithm utilizing such simulators. The framework features detailed models of camera and radar sensors, capturing essential parameters like detection range and field of view, alongside a vehicle plant model and road infrastructure modeling that includes elements such as curvature, slope, banking angles, and varying lane widths. A perception stack is developed using synthetic sensor data and is integrated with the TJP control
Agrawal, Mridul, Ithape, Avinash, Sharma, Prashant, Trivedi, Abhishek
Integrated Evaluation of Pre-Crash Braking and Crash Injury Outcomes Using Human Body Models and ATDs2026-26-00191/16/2026
Traditionally, vehicle occupant safety research has focused on passive safety aspects like crashworthiness and occupant restraint systems. However, with the advent of Active Safety technologies such as Forward Collision Warning (FCW) and Autonomous Emergency Braking (AEB), research has expanded to include occupant kinematics during the pre-crash phase. AEB, by inducing occupant movement, often leads to out-of-position (OOP) behavior, compromising available space during the crash phase. Current Anthropometric Test Devices (ATDs) are limited in simulating occupant kinematics during the pre-crash phase, especially in the neck and torso regions. These devices are rigid and unable to replicate human body dynamics during low-acceleration events like AEB. To address this gap, Human Body Models (HBMs) such as THUMS, GHBMC, and SAFER are used to provide more biofidelic simulations. This study begins with physical vehicle trials conducted with human volunteers at various speeds to measure
Pendurthi, Chaitanya Sagar, THANIGAIVEL RAJA, T, Kondala, Hareesh, Sudarshan, B.Sudarshan, Nehe, Vaibhav, Rao, Guruprakash
Mileage Accumulation Target Identification For AEBS Validation for Medium & Heavy Trucks Considering Real-World Accidents In India2026-26-00361/16/2026
Robust validation of Advanced Driver Assistance Systems (ADAS) considering real-world conditions is a vital for ensuring safety. Mileage accumulation is a one of the validation method for ensuring ADAS system robustness. By subjecting systems to diverse real-world driving environments and edge-case scenarios, engineers can evaluate performance, reliability, and safety under realistic conditions. In accordance with ISO 21448 (SOTIF), known hazardous scenarios are explicitly tested during robustness validation in combination of virtual and physical testing at component, sub system and vehicle level, while unknown hazards may emerge through extended mileage by running vehicles on roads, allowing them to be identified and classified. However, defining a mileage target that ensures comprehensive safety remains a significant engineering challenge. This paper proposes a data-driven approach to define mileage accumulation targets for validating Autonomous Emergency Braking Systems (AEBS
Koralla, Sivaprasad, Ravjani, Amin, Tatikonda, Vijay, GADEKAR, GANESH
Advancing Safe and Sustainable Mobility: A Review of Emerging Trends and Challenges in ADAS2026-26-00421/16/2026
India’s severe road safety challenges, marked by high accident rates and fatalities, necessitate innovative solutions like Advanced Driver Assistance Systems (ADAS) to align with SIAT 2026’s theme, “Innovative Pathways for Safe and Sustainable Mobility. This review synthesizes recent studies to explore ADAS’s role in enhancing safety and sustainability in India’s unique traffic environment. Technologies such as automatic emergency braking, lane departure warnings, and driver monitoring systems show promise in reducing crashes caused by human error, a leading factor in road incidents. However, India’s complex road conditions unmarked lanes, dense urban traffic, and prevalent two-wheelers pose significant challenges to ADAS effectiveness. Recent surveys highlight strong public support for ADAS, with many Indian road users recognizing its safety benefits and advocating for its integration into vehicles. Despite growing adoption by automakers like Tata and Mahindra, barriers such as high
Neelakanthu, Karra, Sreenivasulu, T, Kumar, Om, Haregaonkar, Rushikesh Sambhaji, Kumar, Rajiv
Vehicle Twin Connectivity and Computing2026-26-04191/16/2026
The evolution of future mobility is predicated on the seamless integration of interconnectivity and high-performance computational frameworks to support advanced functionalities such as Advanced Driver Assistance Systems (ADAS) and autonomous driving. These capabilities necessitate next-generation Electronic Control Units (ECUs) that exhibit both high processing throughput and robust data management capabilities. However, vehicle-embedded computation is constrained by the limitations of on-board perception systems—namely, camera, radar, and LiDAR sensors—which are inherently dependent on line-of-sight (LoS) conditions. These constraints limit environmental awareness and introduce risks in edge-case scenarios, particularly where the probability of false positives in decision-making must be minimized due to real-time safety-critical implications. To address these challenges, we propose a paradigm shift towards cloud-integrated digital twin architectures. Each vehicle would be mirrored by
Rathi, Gopal
Leveraging road accident database to enhance ADAS features and testing protocols in context of Indian driving conditions2026-26-00141/16/2026
Advanced Driver Assistance Systems (ADAS) are pivotal in enhancing road safety by reducing human error and assisting drivers in critical situations. Since, major ADAS technologies are developed and validated using data and test scenarios that are predominantly based on driving conditions and road environments of developed countries. But for country like India where driving behaviour, traffic dynamics, road infrastructure and the accident characteristics differ significantly, the ADAS technologies and test scenarios validated by different forums create a critical gap in deploying such systems on vehicles to work on Indian roads. The objective of this research is to identify and generate India specific ADAS test scenarios from the Road Accident Sampling system India (RASSI) database, available MoRTH reports, data available from previously executed ADAS test cases, etc. Through this research we propose a methodology to identify, extract and analyse accident scenarios pertaining to Indian
Adhikari, Mayur, Bhagat, Ajinkya, Verma, Harshal, Kale, Jyoti Ganesh, Sharma, Chinmaya, Karle, Ujjwala
Single Radar: A Robust and Cost-Effective Solution for ADAS and Parking functionality2026-26-00491/16/2026
With rapid advancements in Autonomous Driving (AD) & Advanced Driver Assistance Systems (ADAS), numerous sensors are integrated in vehicles to achieve higher and reliable level of autonomy. Due to the growing number of sensors and its fusion creates complex architecture which causes challenges in calibration, cost, and system reliability. Considering the need for further ADAS advancements and addressing the challenges, this paper evaluates a novel solution called One Radar - a single radar system with a wide field of view enabled by advanced antenna design. Placing the One Radar at the rear of the vehicle eliminates the need for corner radars and ultrasonic sensors used for parking assistance. With rigorous real-world testing in different urban and low-speed scenarios, the One Radar solution showed comparable accuracy in object detection with warning and parking assistance to the conventional combination of corner radars and ultrasonic sensors. The simple single sensor-based
Anandan, Ram, Sharma, Akash
Adapting ADAS to Indian Driving and User Needs2026-26-00451/16/2026
To enhance ADAS usability, safety, and localization in India, this study aims to identify gaps in ADAS system performance and Indian drivers’ expectations by capturing customer’s voice, underlining the need for context-aware ADAS development tailored to emerging Indian markets. The penetration of ADAS in automotive markets is increasing rapidly. However, their effectiveness and acceptance are significantly influenced by regional driving behavior and infrastructure. This study explores the interaction between naturalistic driver behaviour in India and the operational characteristics of ADAS Level 2 systems (FCW, ACC, LCF and BSD) with focus on cars. Using real-world driving data collected from Indian roads, the research aims to highlight the divergence between ADAS design assumptions often based on structured Western traffic environments and the complex, dynamic nature of Indian traffic, characterized by frequent human negotiation, informal road practices, and different vehicle types
SANKPAL, KRISHNATH NAMDEV, Magar, AKSHAY, KHOT, ANKUSH, KULKARNI, ALOK, Perez, Marc
Advancing Automotive Radar Characterization: OTA HIL Testing for Enhanced ADAS Safety2026-26-00431/16/2026
The increasing demand for reliable Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies necessitates the precise validation of automotive radar sensors. Traditional on-road testing, while effective, presents challenges such as limited repeatability, high costs, and safety concerns. To address these limitations, this paper introduces an Over-the-Air (OTA) Hardware-in-the-Loop (HIL) methodology for radar characterization, providing a controlled and repeatable lab-based validation approach. This methodology enables dynamic testing of key radar parameters, including range estimation, angle of arrival measurement, Doppler velocity analysis, and object detection accuracy. The proposed setup supports diverse testing scenarios, such as variable target distances, relative speeds, multiple object simulation, and environmental effects, ensuring a comprehensive evaluation of radar behaviour under complex and safety-critical conditions. Through OTA testing, developers can
Jadhav, Tejas, Karle, Ujjwala, Paul, Harshit, SNV, Karthik
Artificially Intelligent Rapid Rescue Vehicle System2026-26-06421/16/2026
human lifestyle. The automotive industry plays a crucial role as travel is an integral part of human lifestyle. This indeed has increased the need and demand for automotive domain to step ahead with technology and innovations. Especially, related to ADAS features and AI/ML based algorithms to provide comfort, safety, and many other factors for the consumers. The busy life of human beings has shown increased rate of many health-related issues like stress, anxiety, heart attacks, blood pressure, accidents etc. The existing system in vehicles detects health emergencies and triggers SOS to the emergency service center. However, several catastrophic events occur due to delayed information, thus there is a need for a proactive solution that combines technology and human safety. In this work, we have investigated the different methods which detect the health issues of occupants in a vehicle by monitoring their stress level, heart rate, blood pressure etc., We propose a solution which helps to
Eswarappa, Asha, Nagaraj, Chaitra, Mudassir, Syed
Vision Driven Testing Framework for Human-Machine Interfaces: An Automated Approach Using Computer Vision / Machine Learning2026-26-05711/16/2026
Modern automotive infotainment systems and digital instrument clusters require rigorous testing to ensure reliability and compliance with industry standards. Traditional testing methods are labor-intensive and lack scalability. This research proposes a phased, vision-based approach using image processing, machine learning (ML), and computer vision (CV) to enhance testing automation and accuracy. Phase 1 employs open-loop testing using an NVIDIA Jetson Nano with camera modules to capture visual data from infotainment and instrument cluster displays. This phase focuses on the static analysis of visual elements, screen transitions, animations, and error messages. Feature extraction and pattern recognition techniques identify interface behaviors and detect anomalies without a feedback loop. This research will focus in depth on Phase 1, providing a detailed evaluation of its implementation and effectiveness. Phase 2 extends the testing environment with a hardware-in-the-loop (HIL) system
Lad, Rakesh Pramod, Mehrotra, Soumya, Mishra, Arvind
Real-time Intrusion Detection System (IDS) for Vehicle Networks Using Machine Learning for Predictive Threat Analysis2026-26-06171/16/2026
The rapid adoption of connected vehicle technologies and advanced driver assistance systems (ADAS) necessitates robust security mechanisms capable of identifying and mitigating sophisticated cyber threats in real-time. Traditional signature-based intrusion detection systems (IDS) are often inadequate in addressing the dynamic and evolving nature of automotive cybersecurity threats, particularly in modern vehicle networks like Controller Area Network (CAN), CAN with Flexible Data-Rate (CAN-FD), and Automotive Ethernet. This research introduces a novel Real-time Intrusion Detection System utilizing advanced Machine Learning (ML) techniques designed specifically for automotive network environments. The proposed IDS framework employs supervised and unsupervised ML algorithms, including anomaly detection, behavioral analytics, and predictive threat modeling, to achieve high accuracy and rapid threat identification capabilities. Through extensive testing in simulated and actual vehicle
chaudhary lng, Vikash, Desai, Manoj, Chatterjee, Avik
Guidelines for the Integration of Electronic Engine Control Systems for Transport Category (Part 25) and General Aviation (Part 23) AircraftAIR5924B (Current)11/26/2025
This SAE Aerospace Information Report (AIR) provides methodologies and approaches that have been used to install and integrate full-authority-digital-engine-control (FADEC) systems on transport category aircraft. Although most of the information provided is based on turbofan engines installed on large commercial transports, many of the issues raised are equally applicable to corporate, general aviation, regional and commuter aircraft, and to military installations, particularly when commercial aircraft are employed by military users. The word “engine” is used to designate the aircraft propulsion system. The engine station designations used in this report are shown in Figure 1. Most of the material concerns an Electronic Engine Control (EEC) with its associated software, and its functional integration with the aircraft. However, the report also addresses the physical environment associated with the EEC and its associated wiring and sensors. Since most of today’s transport category
E-36 Electronic Engine Controls Committee
Detect and Avoid function for UAVs using ACAS Xu for Non-Cooperative TargetsF-0081-2025-02745/20/2025
Advanced Air Mobility (AAM) is an innovative concept that aims to revolutionize air transportation through electric and unmanned aircraft, enabling applications such as urban air taxis and medical transport. However, one of the key challenges to its widespread adoption is ensuring safety, particularly in collision avoidance. This study focuses on the development of a perception and guidance system for avoiding collisions with non-cooperative targets, which do not share their position or trajectory. To achieve this, a Frequency-Modulated Continuous Wave (FMCW) radar and an InfraRed(IR) camera are used. Compared to traditional pulsed or panel radars, FMCW radars offer higher resolution, better detection of small and slow-moving objects, and improved performance in cluttered environments. The IR camera enhances situational awareness by providing visual confirmation and additional tracking capability, making this sensor fusion approach particularly suitable for AAM applications. Our
Brivio, Riccardo, Crippa, Anna, Baiguera, Matteo, Portanti, Samuele, Bertolo, Mattia
SAE TOMORROW TODAY: Advancing the Path to Full Autonomy135114/18/2025
From advancements in ADAS (Advanced Driver Assistance Systems) to the importance of AI and redundancy, the path to autonomy is paved with delivering valuable intelligence that optimizes safety. Mobileye is a leader in autonomous driving and driver-assistance technologies, harnessing world-renowned expertise in computer vision, artificial intelligence, mapping, and data analysis. The company's diverse product portfolio ranges from driver assist systems to fully autonomous vehicles, all designed to enhance safety, comfort, and efficiency on the road. To learn more, we sat down with Nimrod Nehushtan, Executive Vice President of Business Development Strategy, to discuss the innovative AI technologies powering Mobileye's products, the importance of redundancy in ensuring reliability, and the company's strategic partnerships with leading OEMs. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to
Hineman, Marcie
SAE TOMORROW TODAY: How AI Alignment Makes AVs Safer134951/10/2025
In order for AVs to perform safely and reliably, we need to teach them the language of human preference and expectations--and accelerating AI alignment can do just that. Enter Kognic, the industry-leading annotation platform for sensor-fusion datasets (e.g., camera, radar, and LIDAR data). By helping companies gather, organize, and refine massive datasets used for training AI models, Kognic is helping to ensure that AD/ADAS perform reliably and meet safety standards--all while minimizing costs and optimizing teams. To learn more, we sat down with Daniel Langkilde, Co-Founder and CEO, to discuss why the future of autonomous driving depends on effectively managing AI-driven datasets and how Kognic is leading dataset management for safety-critical AI. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in
Hineman, Marcie
EVOLUTION OF 360 DEGREE LOCAL SITUATIONAL AWARENESS (LSA) TO AID THE WARFIGHTER2024-01-345111/15/2024
ABSTRACT Northrop Grumman has developed a software and hardware solution to provide enhanced 360 degree local situational awareness (LSA) to enable the warfighter with an overmatch capability on today’s modern battlefield. The architecture exploits technological gains in cameras, video processing, and video compression. The approach allows rapid comprehension of local and remote situational views presented with operational relevance for a ground combat platform or tactical wheeled platform crew. The 360 Degree LSA approach provides direct visualization of relative positioning of targets, threats, and lines of fire; and additionally offers common situational understanding / operational picture from the dismounted soldier to higher echelon commands. The approach provides prioritized information through LSA software to provide an enhanced view to the warfighter whereas the squad leader becomes an integral part of the crew with a view of the common operating picture (mounted) and
Viscovich, Christopher, Geoghegan, Susan, Worthy, David
SAE TOMORROW TODAY: Improving Road Safety With ADAS and ADS134718/1/2024
Thousands of people die or are injured in automobile crashes each year. By leveraging in-vehicle advanced driver assistance systems (ADAS) and automated driving systems (ADS) more proactively, errors caused by human drivers can be reduced, resulting in less fatal and serious car crashes. To learn more about the future of road safety, we sat down with Seth Chalmers, Director of Traffic Engineering for Dibble, to discuss new NHTSA developments, the intersection of ADAS and autonomy, and his experience with Waymo in Phoenix.
Hineman, Marcie
SAE TOMORROW TODAY: Unsupervised Learning Makes Scalable Autonomy a Reality134596/12/2024
Artificial intelligence that teaches itself? That's the gist of unsupervised learning. And it offers far-reaching implications for the future autonomous driving--and much more. As a pioneer in unsupervised learning, Helm.ai employs an AI-first approach to building autonomous driving software using an in-house unsupervised learning technology called Deep Teaching which leverages insights from applied mathematics and compressive sensing. Deep Teaching enables Helm.ai to train on vast volumes of data more efficiently without the need for large scale fleets or human annotation, moving the world closer to fully autonomous self-driving systems. The company licenses their software to automakers for high-end ADAS L2+/L3 products and uses the same scalable AI approach for L2/L3 ADAS through L4. To learn more about this exciting breakthrough, we sat down with Vlad Voroninski, CEO & Co-Founder of Helm.ai, to discuss unsupervised learning, Deep Teaching, and the future of autonomous driving.
Hineman, Marcie
EDITORIAL: It's not nothing, man24AUTP06_046/1/2024
Mathematicians, hold your ire. There's a hidden message in this issue that there's no difference between zero and infinity. Let me explain. In mid-May, I attended VI-Grade's Zero Prototype Summit (ZPS). As the name suggests, the company - like so many others working in the test simulation space - is trying to provide OEMs and suppliers with the tools to reduce the number of physical prototypes that have to be developed and built before those vehicles or components reach production-ready status. We haven't yet entered the zero-prototype automotive world, but we're getting closer. You can find detailed ZPS coverage starting on page 22.
Blanco, Sebastian
Vehicle Experience and Human Interaction: Vol. 1EPRCOMPV1720235/3/2024
Connected and autonomous vehicles (CAVs) and their productization are a major focus of the automotive and mobility industries as a whole. However, despite significant investments in this technology, CAVs are still at risk of collisions, particularly in unforeseen circumstances or “edge cases.” It is also critical to ensure that redundant environmental data are available to provide additional information for the autonomous driving software stack in case of emergencies. Additionally, vehicle-to-everything (V2X) technologies can be included in discussions on safer autonomous driving design. Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including CAVs. It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The
Abdul Hamid, Umar Zakir, Roth, Christian, Nickerson, Jeffrey, Lyytinen, Kalle, King, John Leslie
Evaluation of Automated Emergency braking systems for collision avoidance2024-26-01851/16/2024
From the past few years, there is a pressing need for implementation of automatic in-vehicle safety systems to avoid vehicle crashes and fatalities. Development of autonomous emergency braking systems (AEB) to detect and avoid collisions in such critical moments is of paramount importance. In this paper, AEB control systems are developed for a four-wheeler system that aims to detect pedestrians and/or vehicles and halts the ego vehicle based on the time to collision (TTC). This control system aims to react based on the real-time relative distance & speed of the ego vehicle to actuate appropriate braking force. Control systems developed in Altair ActivateTM are co-simulated with CARLA, a virtual reality simulator for autonomous driving research. Various scenarios including sudden pedestrian entry, low and high-speed car to car motion, urban high traffic density environment, changing weather condition etc. are simulated to study the robustness of the control system. Further, studies are
K V, Manoj, Kamikkiya, P, Urs, Hriday V.
Synthetic scenario generation from real road data for Indian specific ADAS function verification and validation2024-26-00201/16/2024
Advanced Driver Assistance Systems (ADAS) play a crucial role in enhancing road safety by providing intelligent assistance to drivers. To ensure the reliability and effectiveness of ADAS functions, rigorous verification and validation processes are necessary. One critical aspect of this process is scenario generation, which involves creating diverse and representative driving scenarios for testing and evaluating ADAS functions. This paper proposes a novel approach for synthetic scenario generation specifically tailored for Indian road conditions. The approach leverages real-time road data collected from various sources, including camera sensors, Lidar sensor, GPS devices, and traffic monitoring systems. The collected data is processed and analyzed to extract relevant information, such as road geometries, traffic patterns, and environmental conditions. Based on the extracted data, a synthetic scenario generation algorithm is developed, which takes into account the unique characteristics
Pachhapurkar, Ninad, R, Manish, Kale, Jyoti Ganesh, Karle, Manish, Karle, Ujjwala, Gupta, Rishu, saurav, Saket
Active-Learning method : An effective way to generate ground truth data to test & validate ADAS functions2024-26-03641/16/2024
In today’s autonomous industry, Machine learning plays an important role in development of various functions for self-driving vehicles. The learning models are trained using data, and their performance is determined by the quality of data they get trained on. For autonomous driving, sometimes, we don't have valid data and our model doesn't know what to do. As a result, the models must be trained on as much as diverse data as possible to make them efficient to deal with unusual situations in the actual world. Huge amount of unlabeled data is collected by cars moving around, equipped with multiple sensors to capture the surrounding environment in a frame. It becomes difficult to annotate each data manually and find data frame with more information. Therefore, automated methods are needed to identify and extract the most informative data from collected ones. Active learning is a powerful data selection technique which improves data efficiency for learning models. In Active Learning, the
Katariya, Rashmi, Kumari, Anita
Anomaly detection and Quality Indicators for Digital maps used in ADAS applications2024-26-00261/16/2024
With the evolution of ADAS and AD technology, digital maps have become one of the critical sources along with other ADAS sensors. Up-to-date map data is crucial for proper functioning of ADAS functions. The need for map update is driven by (i) ADAS technology progressively using map attributes in risk of getting outdated, and (ii) by rapid changes in road infrastructure. This demands the need to evaluate the correctness and quality of the map data on a regular basis and in an efficient way. In this work, we propose a framework to quantify the map data quantity and quality in a systematic way. The framework algorithmically detects error locations in a map database and then derives KPIs from these error locations. This helps in identifying issues in the map data related to the internal data consistency or heuristic rules. The framework consists of a process automation written in Python and map database checks written in SQL. The proposed framework defines validation methodology that
Veber, Christophe, Bai N, Niha, Bhat, Goutam, Kumar, Vikas, Nair, Priyanka, Li, Yu
Framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems2024-26-00221/16/2024
Autonomous Emergency Braking (AEB) systems play a critical role in ensuring vehicle safety by detecting potential rear-end collisions and automatically applying brakes to mitigate or prevent accidents. This paper focuses on establishing a framework for the Verification & Validation (V&V) of Advanced Driver Assistance Systems (ADAS) by testing & verifying the functionality of a RADAR-based AEB ECU. A comprehensive V&V approach was adopted, incorporating both virtual and physical testing. For virtual testing, closed-loop Hardware-in-Loop (HIL) simulation technique was employed. The AEB ECU was interfaced with the real-time hardware via CAN. Data for the relevant target such as the target position, velocity etc. was calculated using an ideal RADAR sensor model running on the real-time hardware. The methodology involved conducting a series of test scenarios, including various driving speeds, obstacle types, and braking distances. Automation was leveraged to perform automated testing and
Bhagat, Ajinkya, Kale, Jyoti Ganesh, Pachhapurkar, Ninad, Karle, Manish, R, Manish, Karle, Ujjwala
Evaluating forward collision warning and autonomous emergency braking systems in India using dashboard cameras2024-26-00311/16/2024
Road traffic fatalities in India have been increasing, reaching around 150,000 fatalities a year. To reduce fatalities, some prospective studies suggested using active safety technologies such as Forward Collision Warning (FCW), and Autonomous Emergency Braking (AEB). However, the effectiveness of FCW and AEB on Indian roads using retrospective studies is not known. Vehicle data such as radar, and controller area network signals could be used for the evaluation of the systems (FCW and AEB). However, these data are not readily accessible. This exploratory study aims to explore the opportunities and limitations of using simple dashboard cameras for a Field Operational Test. One European car with state-of-the-art FCW and AEB systems was rented. Fifteen drivers shared the vehicle, driving almost 10,000 km over 29 days. The vehicle was mounted with a set of dashboard cameras. The navigator noted the “system activated” events and “no activation” events in the logbook during the drive. Post
Shaikh, Junaid, Lubbe, Nils
A Novel Method to Select Hyperparameters of The DBSCAN Algorithm for RADAR Applications2024-26-00301/16/2024
Abstract- As the automotive industry is coming up with various ADAS solutions, RADAR is playing an important role. There are many parameters concerning RADAR detections to acknowledge. Unsupervised Clustering methods are used for RADAR applications. DBSCAN clustering method which is widely used for RADAR applications. The existing clustering DBSCAN is not aligned very well with its hyperparameters such as epsilon (the radius within which each data point checks the density) and minimum points (minimum data points required within a circle to check for core point) for which a calibration is needed. In this paper, different methods to choose the hyperparameters of DBSCAN are compared and verified with different clustering evaluation criteria. A novel method to select hyperparameters of the DBSCAN algorithm is presented with the paper. For testing the given algorithm, ground truth data is collected, and the results are verified with MATLAB-Simulink. Keywords- (clustering, DBSCAN, DBSCAN
Payghan, Vaibhav Santosh, Prajapati, Miit, Chauhan, Abhisha
Regulatory trends for enhancement of Road Safety2024-26-01651/16/2024
Various Advanced Driver Assistance systems are being introduced to the Indian Market with the aim of reducing the road fatalities and injuries leading to severe trauma. India being one of the largest markets for the automobile sector always stands committed towards providing safe and sustainable transportation to its citizens. Advanced Driver Systems such as Anti-lock Braking Systems (ABS), Autonomous Emergency Braking systems (AEBS), Lane Departure Warning Systems, Auto Lane Correction Systems, Driver Drowsiness Monitoring Systems, Speed Limiting and Assisting Systems, Automated Crusing Control Systems, Automated Commanded Steering Functions, Traffic Jam assist systems etc., assist the driver during driving. They tend to reduce road accidents and related fatalities by their advanced and artificial intelligent fed programs. This paper will share an insight on the past, recent and the upcoming developments in the regulations with respect to driver assist systems, that are being enforced
Nayak, Pratik, Rawal, Vishal, Patil, Kamalesh, Tandon, Vikram, Badusha, Akbar
A Multiple-Input Multiple-Output Radar-Based Rider Assistance System for Personal Light Electric Vehicles2023-01-50407/6/2023
The use of personal light electric vehicles (PLEVs), such as electric scooters, has rapidly increased in recent years. However, their widespread use has raised concerns about rider safety due to their vulnerability in shared traffic spaces. To address this issue, this paper presents a radar-based rider assistance system aimed at enhancing the safety of PLEV riders. The system consists of an adaptive feedback system and a single-channel anti-lock braking system (ABS). The adaptive feedback system uses multiple-input multiple-output (MIMO) radar sensors to detect nearby objects and provide real-time warnings to the rider through haptic, visual, and acoustic signals. The system takes into account traffic density and uses online data to warn about obscured objects, thereby improving the rider’s situational awareness. Results from testing the feedback system show that it effectively detects potential collisions and provides warning signals, reducing the risk of accidents. The ABS is
Pyschny, Jan, Berger, Felix, Rothen, Samuel, Denker, Joachim, Frantzen, Michael, Roder, Felix, Kneiphof, Simon
"Objectifying Visual Perception for Automotive Screens through Vibration Targets"2025-01-00435/5/2023
New mobility concepts with smart infrastructure have led to enhanced customer driving experience. The potential to develop safe cars with minimal driver intervention is a great need of the future. The cusp for fully autonomous driving has produced much technical talk, which has led to faster transition and adoption. One of the features that global OEMs have tried to focus on, is Human Machine Interface (HMI) solutions, popularly called display screens. The touchscreen HMIs are common in all mid-range budget cars. They offer driver support beyond just streaming music, including inputs for navigation, parking assistance, in-car technologies, ADAS, and infotainment. Poor display screen visibility is a phenomenon observed when a vehicle is driven over different road surfaces. This paper presents a user-centric approach for the right design & development of the HMI for a vibration free driving experience. The mounting strategies for the display screens, from the existing projects were
ADIL, MD SHAHZAD, C M, MITHUN, Mohammed, Riyazuddin, R, Prasath
test 11/01/2022 10:40 amauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testauto testSAE-PP-0778011/1/2022
test 11/01/2022 10:40 am
Mutagaana, Festo
Design and Development of a Virtual Autonomous System2022-01-507210/3/2022
An autonomous vehicle or a driverless vehicle is a ground vehicle that is capable of sensing its environment and moving safely with little to no human input. The present study aims to validate existing literature on vehicle following, emergency braking, lane changing, overtaking, and jerk trajectory with regard to autonomous vehicles. By using the jerk trajectory equation, we are able to optimize it by implementing a complete overtaking procedure. We are also able to compare the variation in both the dynamic and kinematic bicycle model for the same vehicle and traveling the same distance. A new kinematic bicycle model is built, which is more simple to construct. This project is performed using MATLAB and Simulink for creating and modifying the system blocks. There has also been an integration of 3D visualization software such as VR sink and Unreal Engine. We construct the vehicles by simple transfer functions, ACC and PID as the main system blocks for vehicles 1, 2, and 3, respectively
Narain, C.M., Patil, Sharanbassapa S.
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