Browse Topic: Active safety systems

Items (1,854)

End-to-End Style Parking Algorithm Development Pipeline Based on Procedural Parking Scenario Synthetic Data Generation

2025-01-8060

04/01/2025

Less costs and higher efficiency may be constant technological pursuit. Despite the great success, data-driven AI development still requires multiple stages such as data collection, cleaning, annotation, training, and deployment to work together. We expect an end-to-end style development process that can integrate these processes, achieving an automatic data production and algorithm development process that can work with just clicks of the mouse. For this purpose, we explore an end-to-end style parking algorithm development pipeline based on procedural parking scenario synthetic data generation. Our approach allows for the automated generation of parking scenarios according to input parameters, such as scene construction, static and dynamic obstacles arrangement, material textures modification, and background changes. It then combines with the ego-vehicle trajectories into the scenarios to render high-quality images and corresponding label data based on Blender software. Utilizing

Li, Jian, Wang, Hanchao, Zhang, Song, Meng, Chao, Rui, Zhang

Advancing Verification and Validation for ADAS and ADS

2025-01-8052

04/01/2025

Emerging automotive technologies like advanced driver assistance systems (ADAS) and automated driving systems (ADS) hold promise for improving safety for the traveling public; however, effective verification and validation (V&V) of these systems has proven to be challenging. Traditional testing methodologies may serve in limited cases for systems exhibiting low levels of automation, but recent studies show that these systems that have been brought to market perform poorly in practice. Further, these traditional methods do not serve for testing systems with high levels of automation where a human driver simply serves as a fallback ready user or is out of the loop altogether. New V&V methods are required to assess whether these systems can perform their intended functions in their intended operating environments, and to assess whether they can do so safely across the expansive and variable operating space. This paper presents an overview of ADAS and ADS challenges and novel approaches to

Thorn, Eric, Knisley, Veronica, Auchter, Joseph

Development of an Advisory System for Parking a Car with Trailer

2025-01-8035

04/01/2025

Trailer parking is a challenging task due to the unstable nature of the vehicle-trailer system in reverse motion and the unintuitive steering actions required at the vehicle to accomplish the parking maneuver. This paper presents a strategy to tackle this kind of maneuver with an advisory graphic aid to help the human driver with the task of manually backing up the vehicle-trailer system. A kinematic vehicle-trailer model is derived to describe the low-speed motion of the vehicle-trailer system, and its inverse kinematics is established by generating an equivalent virtual trailer axle steering command. The advisory system graphics is generated based on the inverse kinematics and displays the expected trailer orientation given the current vehicle steer angle and configuration (hitch angle). Simulation study and animation are set up to test the efficacy of the approach, where the user can select both vehicle speed and vehicle steering angle freely, which allows the user to stop the

Cao, Xincheng, Chen, Haochong, Aksun Guvenc, Bilin, Guvenc, Levent, Link, Brian, Harber, John, Richmond, Peter, Fan, Shihong, Yim, Dokyung

Deriving Architectural Requirements for SAE J3016 ^TM Level 3 System from Functional Safety Perspective

2025-01-8009

04/01/2025

Vehicles with SAE J3016TM Level 3 systems are exposed to road infrastructure, Vulnerable Road Users (VRUs), traffic and other actors on roadways. Hence safe deployment of Level 3 systems is of paramount importance. One aspect of safe deployment of SAE Level 3 systems is the application of functional safety (ISO 26262) to their design, development, integration, and testing. This ensures freedom from unreasonable risk, in the event of a system failure and sufficient provisions to maintain Dynamic Driving Task (DDT) and to initiate Minimum Risk Maneuver (MRM), in the presence of random hardware and systematic failures. This paper explores leveraging ISO 26262 standard to develop architectural requirements for enabling SAE Level 3 systems to maintain DDT and MRM during fault conditions and outlines the importance of fail-operability for Level 3 systems, from a functional safety perspective. At a high-level, UN Regulation No. 157 – Automated Lane Keeping Systems (ALKS) is used as a baseline

Mudunuri, Venkateswara Raju, Jayakumar, Namitha

Advanced Driver Assistance Systems (ADAS): Assessing the Efficacy of Non-Impact Testing for Evaluating the Performance of Frontal Collision Mitigation Technology

2025-01-8056

04/01/2025

As Automatic Emergency Braking (AEB) systems become standard equipment in more light duty vehicles, the ability to evaluate these systems efficiently is becoming critical to regulatory agencies and manufacturers. A key driver of the practicality of evaluating these systems’ performance is the potential collision between the subject vehicle and test target. AEB performance can depend on vehicle-to-vehicle closing speeds, crash scenarios, and nuanced differences between various situational and environmental factors. Consequently, high speed impacts that may occur while evaluating the performance of an AEB system, as a result of partial or incomplete mitigation by an AEB activation, can cause significant damage to both the test vehicle and equipment, which may be impractical. For tests in which impact with the test target is not acceptable, or as a means of increasing test count, an alternative test termination methodology may be used. One such method constitutes the application of a late

Kuykendal, Michelle, Easter, Casey, Koszegi, Giacomo, Alexander, Ross, Paradiso, Marc, Scally, Sean

Role of AI in an Autonomous Vehicle Perception Systems: Use of Convolutional Neural Network Approach to Design the Vehicle Perception System

2025-01-8012

04/01/2025

Artificial Intelligence has gained lot of traction and importance in the 21st century with use cases ranging from speech recognition, learning, planning, problem solving to search engines etc. Artificial Intelligence also has played a key role in the development of autonomous vehicles and robots ranging from perception, localization, decision to controls. Within the big AI umbrella there is machine learning which is all about using your computer to "learn" how to deal with problems without “programming". Deep learning is a branch of machine learning based on a set of algorithms that learn to represent the data directly from the input such as an image, text, Sound, etc. Within deep learning there are Convolutional Neural Networks and Recurrent Neural Networks (CNN/RNN). The study here used convolutional neural network approach to perform image/object recognition. Given that the objective of the autonomous or semi-autonomous vehicle is to promote safety and reduce number of accidents, it

Mansour, Iyad, Singh, Sanjay

Responsible AI System Development in Automotive Applications: A Framework

2025-01-8102

04/01/2025

With the surge in adoption of artificial intelligence (AI) in automotive systems, especially Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AV), comes an increase of AI-related incidents–several of which have ended in injuries and fatalities. These incidents all share a common deficiency: insufficient coverage towards safety, ethical, and/or legal requirements. Responsible AI (RAI) is an approach to developing AI-enabled systems that systematically take such requirements into account. Existing published international standards like ISO 21448:2022 (Safety of the Intended Functionality) and ISO 26262:2018 (Road Vehicles – Functional Safety) do offer some guidance in this regard but are far from being sufficient. Therefore, several technical standards are emerging concurrently to address various RAI-related challenges, including but not limited to ISO 8800 for the integration of AI in automotive systems, ISO/IEC TR 5469:2024 for the integration of AI in functional

Nelson, Jody, Lin, Christopher

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

2025-01-8065

04/01/2025

As longitudinal Automated Driving System (ADS) technologies, such as Adaptive Cruise Control (ACC), become more prevalent, robust testing frameworks that encompass both simulation and vehicle-in-the-loop (VIL) methodologies are essential to ensure system reliability, safety, and performance refinement. Although significant research has focused on ACC algorithm development and simulation testing, existing VIL dynamometer testing frameworks are typically tailored to specific vehicle models and sensor simulation tools. These highly customized approaches often fail to account for broader interoperability while overlooking energy consumption as a key performance metric. This paper presents a novel modular framework for ACC dynamometer testing, designed to enhance interoperability across a diverse range of vehicle platforms, simulation tools, and dynamometer facilities with a focus on evaluating impacts of automated longitudinal control on the overall energy consumption of the vehicle. The

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

Active and Passive Security Generalization Algorithm and Typical Case Analysis

2025-01-8744

04/01/2025

In order to effectively predict the vehicle safety performance and reduce the cost of enterprise safety tests, a generalized simulation model for active and passive vehicle safety was proposed. The frontal driver-side collision model under the intervention of the Autonomous Emergency Braking (AEB) was created by using the MADYMO software. The collision acceleration obtained from the sled test was taken as the original input of the model to conduct simulation for the working conditions under different sitting postures of the human body. The injury values of various parts of the Hybrid III 50th dummy were read. Based on the correlation between the two, an active and passive simulation model was established through the Back Propagation (BP) neural network. The input of the model was the inclination angle centered on the dummy's waist, and the output was the acceleration of the dummy's head. The results showed that the comprehensive prediction accuracy rate exceeded 80%. Therefore, the

Ge, Wang, fengyao, LV

Improved Distance Estimation in Dynamic Environments through Multi-Sensor Fusion with Extended Kalman Filter

2025-01-8034

04/01/2025

The application of multi-sensor fusion for enhanced distance estimation accuracy in dynamic environments is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. The Limitations of single sensors such as cameras or radar in adverse conditions motivate the use of combined camera and radar data to improve reliability, adaptability, and object recognition. A multi-sensor fusion approach using an Extended Kalman Filter (EKF) is proposed to combine sensor measurements with a dynamic system model, achieving robust and accurate distance estimation. The research utilizes the Mississippi State University Autonomous Vehicular Simulator (MAVS), a physics-based simulation platform, to generate realistic synthetic datasets incorporating sensor imperfections such as noise and missed detections to create a controlled environment for data collection. Data analysis is performed using MATLAB. Qualitative metrics such as visualization of fused data vs ground truth and

Ebu, Iffat Ara, Islam, Fahmida, Rafi, Mohammad AbdusShahid, Rahman, Mahfuzur, Iqbal, Umar, Ball, John

Tire–Road Friction Estimation and Classification Based on a CNN using Tire Acoustical Signals for Autonomous Driving Vehicles

2025-01-8761

04/01/2025

Advanced driver assistance systems (ADASs) and driving automation system technologies have significantly increased the demand for research on vehicle-state recognition. However, despite its critical importance in ensuring accurate vehicle-state recognition, research on road-surface classification remains underdeveloped. Accurate road-surface classification and recognition would enable control systems to enhance decision-making robustness by cross-validating data from various sensors. Therefore, road-surface classification is an essential component of autonomous driving technologies. This paper proposes the use of tire–pavement interaction noise (TPIN) as a data source for road-surface classification. Traditional approaches predominantly rely on accelerometers and visual sensors. However, accelerometer signals have inherent limitations because they capture only surface profile properties and are often distorted by the resonant characteristics of the vehicle structure. Similarly, image

Yoon, Youngsam, Kim, Hyungjoo, Lee, Sang Kwon, Lee, Jaekil, Hwang, Sunguk, Ku, Sehwan

High-Speed FCW & AEB Testing of Tesla Model 3's Across Software Updates

2025-01-8695

04/01/2025

A total of 148 tests were conducted to evaluate the Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) systems in five different Tesla Model 3 vehicles between model years 2018 and 2020 across four calendar years. These tests involved stationary vehicle targets, including a foam Stationary Vehicle Target (SVT), a Deformable Stationary Vehicle Target (DSVT), a live vehicle with brake lights, and a SoftCar360 designed for high-speed impact tests. The evaluations were conducted at speeds of 35, 50, 60, 65, 70, 75, and 80 miles per hour (mph) during both daytime and nighttime conditions and utilized early and medium FCW settings. These settings, part of Tesla's Collision Avoidance AssistTM, modify object detection alerts and the timing of visual and auditory warnings issued to drivers. The 2018 to 2020 vehicles initially utilized cameras, radar and ultrasonic sensors (USS) for object detection. Tesla updated their Autoilot software and detection algorithms to a vision

Harrington, Shawn, Nagarajan, Sundar Raman

Synchronizing Real-Time Control in Centralized Automotive E/E Architectures

2025-01-8072

04/01/2025

Automotive technologies have been rapidly evolving with the introduction of electric powertrains, Advanced Driver-Assistance Systems (ADAS) and Over-The-Air (OTA) upgradability. Existing decentralized architectures are not an optimal choice for these applications, due to significant increases in cost and complexity. The transition to centralized architectures enables heavy computation to be delegated to a limited number of powerful Electronic Control Units (ECUs) called domain or zone controllers. The remaining ECUs, known as smart actuators, will perform well defined and specific tasks, receiving new parameters from the dedicated domain/zone controller over a network. Network bandwidth and time synchronization are the two major challenges in this transition. New automotive standards have been developed to address these challenges. Automotive Ethernet and Time Sensitive Networking (TSN) are two standards that are well-suited for centralized architectures. This paper presents a

Ayesh, Mostafa, Bandur, Victor, Pantelic, Vera, Wassyng, Alan, Wasacz, Bryon, Lawford, Mark

Empirical Analysis on Machine Vision Recognition of Green Bike Lanes for Vulnerable Road Users Safety

2025-01-8017

04/01/2025

Deliberate modifications to infrastructure can significantly enhance machine vision recognition of road sections designed for Vulnerable Road Users, such as green bike lanes. This study evaluates how green bike lanes, compared to unpainted lanes, enhance machine vision recognition and vulnerable road users safety by keeping vehicles at a safe distance and preventing encroachment into designated bike lanes. Conducted at the American Center for Mobility, this study utilizes a vehicle equipped with a front-facing camera to assess green bike lane recognition capabilities across various environmental conditions including dry daytime, dry nighttime, rain, fog, and snow. Data collection involved gathering a comprehensive dataset under diverse conditions and generating masks for lane markings to perform comparative analysis for training Advanced Driver Assistance Systems. Quality measurement and statistical analysis are used to evaluate the effectiveness of machine vision recognition using

Ponnuru, Venkata Naga Rithika, Das, Sushanta, Grant, Joseph, Naber, Jeffrey, Bahramgiri, Mojtaba

Effect of Aftermarket Modifications on ADAS Functionality – 2021 Ford F-150 Light Vehicle

2025-01-8057

04/01/2025

Both automotive aftermarket vehicle modifications and Advanced Driver Assistance Systems (ADAS) are growing. However, there is very little information available in the public domain about the effect of aftermarket modifications on ADAS functionality. To address this deficiency, a research study was previously performed in which a 2022 Chevrolet Silverado 1500 light truck was tested in four different hardware configurations. These included stock as well as three typical aftermarket configurations comprised of increased tire diameters, a suspension level kit, and two different suspension lift kits. Physical tests were carried out to investigate ADAS performance of lane keeping, crash imminent braking, traffic jam assist, blind spot detection, and rear cross traffic alert systems. The results of the Silverado study showed that the ADAS functionality of that vehicle was not significantly altered by aftermarket modifications. To determine if the results of the Silverado study were

Bastiaan, Jennifer, Muller, Mike, Morales, Luis

RGB2BEV-Net: A PyTorch-Based End-to-End Pipeline for RGB to BEV Segmentation Using an Extended Dataset for Autonomous Driving

2025-01-8023

04/01/2025

In this study, we introduce RGB2BEV-Net, an end-to-end pipeline that extends traditional BEV segmentation models by utilizing raw RGB images with Bird’s Eye View (BEV) generation. While previous work primarily focused on pre-segmented images to generate corresponding BEV maps, our approach expands this by collecting RGB images alongside their affiliated segmentation masks and BEV representations. This enables direct input of RGB camera sensors into the pipeline, reflecting real-world autonomous driving scenarios where RGB cameras are commonly used as sensors, rather than relying on pre-segmented images. Our model processes four RGB images through a segmentation layer before converting them into a segmented BEV, implemented in the PyTorch framework after being adapted from an original implementation that utilized a different framework. This adaptation was necessary to improve compatibility and ensure better integration of the entire system within autonomous vehicle applications. We

Hossain, Sabir, Lin, Xianke

Real-Time Dynamic Mass Learning Algorithm for Connected and Automated Light-Duty Vehicles

2025-01-8389

04/01/2025

Mass estimation in light-duty vehicles (LDVs) is a crucial aspect of vehicle dynamics, control systems, energy optimization, range prediction, and overall performance. Accurate mass estimation is essential for precise energy predictions, which are used by energy optimization algorithms. It also enhances vehicle safety and the effectiveness of advanced driver assistance systems (ADAS). The mass of a vehicle can vary depending on occupancy and load. This paper presents a comprehensive study on in-situ mass learning in light-duty vehicles under real-world driving conditions. Utilizing simple longitudinal dynamics, road grade calculated from GPS with RTK correction, and the vehicle’s torque model, we developed a robust framework for vehicle mass estimation. A detailed sensitivity analysis was performed to evaluate the impact of uncertainties or errors in various inputs and parameters, identifying optimal regions for learning the mass and ensuring the model's reliability. This method was

Poovalappil, Aman, Robare, Andrew, Apostol, Peter, Bahramgiri, Mojtaba, Chen, Bo, Naber, Jeffrey, Robinette, Darrell

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

2025-01-8063

04/01/2025

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

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

Integrated Bracket Design Optimizations to Resolve Issues in Vehicle Prototype Stage

2025-01-8614

04/01/2025

The integrated bracket is a plastic part that packages functional components such as the ADAS (Advanced Driver Assistance System) camera, rain light sensor, and the mounting provisions of the auto-dimming IRVM (Inner Rear View Mirror). This part is fixed on the windshield of an automobile using double-sided adhesive tapes and glue. ADAS, rain light sensors, and auto-dimming IRVM play an important part in the safety of the driver and everyone present in the automobile. This makes proper functioning of the integrated bracket very integral to occupant safety. Prior to this work, the following literature; Integrated Bracket for Rain Light Sensor/ADAS/Auto-Dimming IRVM with provision of mounting for Aesthetic Cover [1] outlines the design considerations and advantages of mounting several components on the same bracket. It follows the theme where the authors first define the components packaged on the integrated bracket and then the advantages of packaging multiple components on a single

Chandravanshi, Priyansh, Dharmatti, Girish

A Study on Affections of Active Neck Muscle Force on Neck Injury Responses in Frontal Impact with Automatic Emergency Braking Conditions

2025-01-8742

04/01/2025

Neck injury is one of the most common injuries in traffic accidents, and its severity is closely related to the posture of the occupant at the time of impact. In the current era of smart vehicle, the triggered AEB and the occupant's active muscle force will cause the head and neck to be out of position which has significant affections on the occurrence and severity of neck injury responses. Therefore, it is very important to study the influences of active muscle force on neck injury responses in in frontal impact with Automatic Emergency Braking conditions. Based on the geometric characteristics of human neck muscles in the Zygote Body database, the reasonable neck muscle physical parameters were obtained firstly. Then a neck finite element model (FEM) with active muscles was developed and verified its biofidelity under various impact conditions, such as frontal, side and rear-end impacts. Finally, using the neck FEM with or without active muscle force, a comparative study was

Junpeng, Xu, Gan, Qiuyu, Jiang, Binhui, Zhu, Feng

Development of a Computer-Less Robotic Platform for ADAS Testing

2025-01-8064

04/01/2025

Testing collision avoidance systems on vehicles has become increasingly complex. Robotic platforms called Pedestrian Target Carriers (PTC) typically require Global Positioning System (GPS), network communications, tuning, and ever-increasing scope to the user interface to function. As an alternative to these complicated systems, but as an improvement to a pulley system pedestrian target carrier, a simplistic robotic platform was developed. An open-loop user interface was designed and developed, and a series of tests were performed to evaluate the effectiveness of the robot in performing basic, repeatable straight-line tests with a vehicle in the loop. Based on testing outcomes, the development of further control algorithms, user requirements, and the prototype improvements are analyzed for future work.

Bartholomew, Meredith, Muthaiah, Ponaravind, Heydinger, Gary, Zagorski, Scott

Leveraging Traffic-in-the-Loop Simulations to Assess the Impact of Traffic on Vehicle Energy Consumption

2025-01-8054

04/01/2025

The advancements in vehicle connectivity and the increased level of driving automation can be leveraged for the development of Advanced Driver Assistance Systems (ADAS) that improve driver safety and comfort while optimizing the energy consumption of the vehicle. In the development phase of energy-efficient ADAS, modeling and simulation are used to assess the potential benefits of these technologies on energy consumption. However, there is a lack of standardized simulation or test frameworks to quantify the benefits. Moreover, the driving scenario and the traffic conditions are often not explicitly modeled when simulating energy-efficient ADAS, even though they have a major impact on the attainable energy benefits. This paper presents the development and implementation of a closed-loop traffic-in-the-loop simulator designed to evaluate the performance of vehicles under realistic traffic conditions. The primary objective is to qualitatively assess how varying traffic conditions

Grano, Elia, Villani, Manfredi, Ahmed, Qadeer, Carello, Massimiliana

Do Aftermarket and Original Equipment Forward Collision Warning Systems Respond the Same to Vehicles and Pedestrians?

2025-01-8677

04/01/2025

About 32% of registered vehicles in the U.S are equipped with automatic emergency braking or forward collision warning (FCW) systems [1]. Retrofitting vehicles with aftermarket devices can accelerate the adoption of FCW, but it is unclear if aftermarket systems perform similarly to original equipment manufacturer (OEM) systems. The performance of four low-cost, user-installable aftermarket windshield-mounted FCW systems was evaluated in various Insurance Institute for Highway Safety (IIHS) rear-end and pedestrian crash avoidance tests and compared with previously tested OEM systems. The presence and timing of FCWs were measured when vehicles approached a stationary passenger car at 20, 40, 50, 60, and 70 km/h, motorcycle and dry van trailer at 50, 60, and 70 km/h, adult pedestrian at 40 and 60 km/h, and child pedestrian crossing the road at 20 and 40 km/h. Equivalence testing was used to determine if FCW performance was similar for aftermarket and OEM systems. OEM systems provided a

Kidd, David, Floyd, Philip, Aylor, David

Research on Control Algorithm for Active Rear Wheel Steering System of Passenger Cars

2025-01-8755

04/01/2025

With the continuous development of automobile technology, vehicle handling performance and safety have become increasingly critical research areas. The active rear-wheel (ARW) steering system, a technology that significantly enhances vehicle dynamics and driving stability, has garnered widespread attention. By coordinating front-wheel steering with rear-wheel angle adjustments, ARW improves handling flexibility and stability, particularly during high-speed driving and under extreme conditions. Therefore, designing an efficient ARW control algorithm and optimizing its performance are vital to enhancing a vehicle's overall handling capability. This study delves into the control algorithm design and performance optimization of ARW. First, a comprehensive vehicle dynamics model is constructed to provide a solid theoretical basis for developing control algorithms. Next, optimal control theory is applied to regulate the rear-wheel steering angle, and an LQR control strategy with variable

Zhang, Yi, Zheng, Hongyu, Kaku, Chuyo, Zong, Changfu, Zhang, Yuzhou

Compensation Algorithm Development in Platform for Pedestrian ADAS Testing

2025-01-8066

04/01/2025

Test procedures such as EuroNCAP, NHTSA’s FMVSS 127, and UNECE 152 all require specific pedestrian to vehicle overlaps. These overlap variations allow the vehicle differing amounts of time to respond to the pedestrian’s presence. In this work, a compensation algorithm was developed to be used with the STRIDE robot for Pedestrian Automatic Emergency Braking tests. The compensation algorithm uses information about the robot and vehicle speeds and positions determine whether the robot needs to move faster or slower in order to properly overlap the vehicle. In addition to presenting the algorithm, tests were performed which demonstrate the function of the compensation algorithm. These tests include repeatability, overlap testing, vehicle speed variation, and abort logic tests. For these tests of the robot involving vehicle data, a method of replaying vehicle data via UDP was used to provide the same vehicle stimulus to the robot during every trial without a robotic driver in the vehicle.

Bartholomew, Meredith, Nguyen, An, Helber, Nicholas, Heydinger, Gary

Object Detection for City and Highway Driving Scenario with YOLOX and Mask RCNN

2025-01-8015

04/01/2025

This paper explores the integration of two deep learning models that are currently being used for object detection, specifically Mask R-CNN and YOLOX, for two distinct driving environments: urban cityscapes and highway settings. The hypothesis underlying this work is that different methods of object detection will work best in different driving environments, due to the differences in their unique strengths as well as the key differences in those driving environments. Some of these differences in the driving environment include varying traffic densities, diverse object classes, and differing scene complexities, including specific differences such as the types of signs present, the presence or absence of stoplights, and the limited-access nature of highways as compared to city streets. As part of this work, a scene classifier has also been developed to categorize the driving context into the two categories of highway and urban driving, in order to allow the overall object detection

Patel, Krunal, Peters, Diane

A Proactive System to Anticipate and Store the Damage Information on a Parked Car Using Machine Learning Algorithm

2025-01-8211

04/01/2025

Vehicle ADAS Systems majorly comprises of two functions: Driving and Parking. The most common form of damage to the vehicle which goes unnoticed with unidentified cause are parking damages. A vehicle once parked at a certain location may get damaged without knowledge of the user. In this work developed a solution that not only pre-warns the driver but also prepares the vehicle beforehand if it suspects a damage may occur. This eliminates the latency between damage and information capture, detects small damages such as scratches, classifies the type of damage and informs the user beforehand. This is solution is different from our competitors as the existing solutions informs the user about the scratches/damages, but these solutions are expensive, have high response time, and the damage information is captured after the damage has occurred. The solution consists of the following check blocks: Precondition, Sensor Control and Action Module. The Precondition Module observes the vehicle

Debnath, Sarnab, Patil, Prasad, Belur Subramanya, Sheshagiri, Govinda, Shiva Prasad

Study on the Impact of Vehicle Pitch During Braking on Pedestrian Leg Injuries

2025-01-8741

04/01/2025

With the increasing prevalence of Automatic Emergency Braking Systems (AEB) in vehicles, their performance in actual collision accidents has garnered increasing attention. In the context of AEB systems, the pitch angle of a vehicle can significantly alter the nature of collisions with pedestrians. Typically, during such collisions, the pedestrian's legs are the first to come into contact with the vehicle's front structure, leading to a noticeable change in the point of impact. Thus, to investigate the differences in leg injuries to pedestrians under various pitch angles of vehicles when AEB is activated, this study employs the Total Human Model for Safety (THUMS) pedestrian finite element model, sensors were established at the leg location based on the Advanced Pedestrian Legform Impactor (APLI), and a corresponding vehicle finite element model was used for simulation, analyzing the dynamic responses of the pedestrian finite element model at different pitch angles for sedan and Sport

Hong, Cheng, Ye, Bin, Zhan, Zhenfei, Liu, Yu, Wan, Xinming, Hao, Haizhou

Machine Learning-Based Lane Detection and Lateral Offset Estimation Model for Vehicle Following Applications

2025-01-8020

04/01/2025

Precisely understanding the driving environment and determining the vehicle’s accurate position is crucial for a safe automated maneuver. vehicle following systems that offer higher energy efficiency by precisely following a lead vehicle, the relative position of the ego vehicle to lane center is a key measure to a safe automated speed and steering control. This article presents a novel Enhanced Lane Detection technique with centimeter-level accuracy in estimating the vehicle offset from the lane center using the front-facing camera. Leveraging state-of-the-art computer vision models, the Enhanced Lane Detection technique utilizes YOLOv8 image segmentation, trained on a diverse world driving scenarios dataset, to detect the driving lane. To measure the vehicle lateral offset, our model introduces a novel calibration method using nine reference markers aligned with the vehicle perspective and converts the lane offset from image coordinates to world measurements. This design minimizes

Karuppiah Loganathan, Nirmal Raja, Poovalappil, Aman, Naber, Jeffrey, Robinette, Darrell, Bahramgiri, Mojtaba

Fuzzy Sliding Mode Control of Intelligent Electric Vehicles Based on Phase Plane Stability Domain Boundaries

2025-01-8802

04/01/2025

In order to effectively improve the chassis handling stability and driving safety of intelligent electric vehicles (IEVs), especially in combing nonlinear observer and chassis control for improving road handling. Simultaneously, uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IEVs become a hot topic in both academia and industry. To issue the above mentioned, a fuzzy sliding mode control method based on phase plane stability domain is proposed to enhance the vehicle’s chassis performance during complex driving scenarios. Firstly, a two-degree-of-freedom vehicle dynamics model, accounting for tire non-linearity, was established. Secondly, combing with phase plane theory, the stability domain boundary of vehicle yaw rate and side-slip phase plane based

Liao, Yinsheng, Wang, Zhenfeng, Guo, Fenghuan, Deng, Weili, Zhang, Zhijie, Zhao, Binggen, Zhao, Gaoming

An Evaluation of the Performance of the Automatic Emergency Braking and Forward Collision Warning System in a 2020 Jeep Grand Cherokee during Daytime and Nighttime Conditions

2025-01-8698

04/01/2025

Testing was conducted to evaluate the performance of the 2020 Jeep Grand Cherokee’s Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) collision mitigation systems at speeds between 35 and 70 miles per hour (mph). Two different 2020 Jeep Grand Cherokee’s were utilized under varying testing conditions in order to evaluate the performance of their collision mitigation systems. A total of 40 tests were conducted: 29 tests were conducted during daytime and 11 tests were conducted at nighttime. Testing measured the Time to Collision (TTC) values of the visual/audible component of the Forward Collision Warning that was presented to the driver. In addition, the testing quantified the TTC response of the Automatic Emergency Braking (AEB) system including the timing and magnitude of the automatic braking response. The results of the testing add higher speed FCW and AEB testing scenarios to the database of publicly available tests for the 2020 Jeep Grand Cherokee.

Harrington, Shawn, Lieber, Victoria, Nagarajan, Sundar Raman

Analysis of OEM Collision Avoidance Data Outputs during VRU Testing

2025-01-8053

04/01/2025

Using OEM tools to analyze data available from on-vehicle collision avoidance systems can shed light on the data scope that is available to analyze failures in these systems. In this work, an Advanced Driver Assistance System (ADAS)-equipped vehicle was tested to determine the performance in several collision imminent scenarios. Vehicle Event Data Recorder (EDR) data was pulled and compared to data collected from independent instrumentation to determine the vehicle system accuracy in detecting targets, investigate timings, and understand the scope of data available to an agency investigating a failure. Images and data files are presented as an example of scope of output. Tests included several variable overlap tests, along with several tests specifically chosen due to compare performance across the operating design domain.

Bartholomew, Meredith, Heydinger, Gary

Recommendation of Adaptive Safe Speed Limits for Different Driver Profiles Using Deep Learning

2025-01-8106

04/01/2025

Personalization is a growing topic in the automotive space, where Artificial Intelligence can be used to deliver a customized experience in features like seat positioning and climate control. Considering that the leading cause of accidents is driving at an inappropriate speed, personalizing the speed limit for a driver can greatly improve vehicle safety. Current speed limits apply to all drivers, irrespective of skill, including special speed limits when there are adverse weather conditions. As these speed limits do not consider an individual’s skill and capabilities, the limit could still be inappropriate for a given driver in that specific driving context. Therefore, we propose a system that can profile the driver’s style to recommend a personalized speed limit, based on both the environmental context and their skill in that environment. The system uses a neural network to classify the driver’s behavior in specific environments by monitoring the vehicle data and the environmental

Perumal, Rathapriya, Chouhan, Madhvendra, Rangarajan, Rishi

Daytime and Nighttime Performance Evaluations of the Automatic Emergency Braking and Forward Collision Warning Systems on a 2020 and 2022 Kia Telluride

2025-01-8699

04/01/2025

Testing was conducted in daytime and nighttime conditions to evaluate the performance of the Automatic Emergency Braking and Forward Collision Warning systems present on both a 2020 and 2022 Kia Telluride. The 2022 Kia Telluride was tested during the day at speeds between 35 and 70 miles per hour, while the 2020 Kia Telluride was tested both during the day and at night at speeds between 35 and 60 miles per hour (mph). The daytime testing of both the 2020 and 2022 Kia Telluride utilized a foam stationary vehicle target. The nighttime testing of the 2020 Kia Telluride utilized a live 2006 Chevrolet Tahoe as the target with the brake lights on. Testing measured the Time to Collision (TTC) values of the visual/audible component of the Forward Collision Warning (FCW) that was presented to the driver. Further, testing also quantified the timing and magnitude of the two-phase response of the Automatic Emergency Braking (AEB) system. The results of both sets of testing add higher speed FCW and

Harrington, Shawn, Patrick-Moline, Peyton, Nagarajan, Sundar Raman

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

2025-01-8098

04/01/2025

Intelligent transportation systems and connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. Consequently, traditional human-driven vehicles (HDVs), CAVs, and human-driven connected and automated vehicles (HD-CAVs) will coexist on roads for the foreseeable future. Simultaneously, car-following behaviors in equilibrium and discretionary lane-changing behaviors make up the most common highway operations, which seriously affect traffic stability, efficiency and safety. Therefore, it’s necessary to analyze the impact of CAV technologies on both longitudinal and lateral performance of heterogeneous traffic flow. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for different vehicle types, considering human factors and cooperative adaptive cruise control. Then, this paper incorporates CAV penetration rates, shared autonomy rates

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

Research on the Boundary of Pedestrian Leg Impactor Test in Low-Speed Passive and Active Conditions

2025-01-8735

04/01/2025

With the widespread application of the Automatic Emergency Braking System (AEB) in vehicles, its impact on pedestrian safety has received increasing attention. However, after the intervention of AEB, the kinematic characteristics of pedestrian leg collisions and their corresponding biological injury responses also change. At the same time, in order to accurately evaluate the pedestrian protection performance of vehicles, the current assessment regulations generally use advanced pedestrian protection leg impactors (aPLI) and rigid leg impactors (TRL) to simulate the movement and injury conditions of pedestrian legs. Based on this, in order to explore the collision boundary conditions and changes in injury between vehicles and APLI and TRL leg impactors under the action of AEB, this paper first analyzes the current passive and active assessment conditions. Secondly, the simulation software LS-DYNA is used to build a finite element model of APLI and TRL impactor-vehicle collisions to

Ye, Bin, Hong, Cheng, Wan, Xinming, Liu, Yu, Cheng, James, Long, Yongchen, Hao, Haizhou

Collision Avoidance System at Urban Intersections Using V2X Communication

2025-01-8030

04/01/2025

Driving safely through urban intersections is quite challenging for self-driving vehicles due to complicated road geometries and the highly dynamic maneuvers of oncoming traffic, which can cause a high risk of collisions. Traditional onboard sensors like cameras, radar, and lidar give limited visibility in this environment. To overcome these limitations, this paper explores implementing a collision avoidance system at urban intersections utilizing vehicle-to-everything (V2X) communication. The system leverages V2X map data to identify warning zones and uses vehicle-to-vehicle (V2V) communication to estimate the maneuver types and paths of approaching vehicles. The system assesses collision risk by calculating the intersection points of predicted paths for the ego vehicle and oncoming traffic. Depending on the level of collision risk, the system generates a collision warning signal to the driver or activates emergency braking when necessary to prevent accidents. We implement the system

Park, Seo-Wook, Suresh, Raynier, Ailuri, Anusha

Development of a Rule-Based Lidar Lane-Keeping Assistance System for Autonomous Vehicles Applications

2025-01-8025

04/01/2025

Lane-keeping is critical for SAE Level 3+ autonomous vehicles, requiring rigorous validation and end-to-end interpretability. All recently U.S.-approved level 3 vehicles are equipped with lidar, likely for accelerating active safety. Lidar offers direct distance measurements, allowing rule-based algorithms compared to camera-based methods, which rely on statistical methods for perception. Furthermore, lidar can support a more comprehensive and detailed approach to studying lane-keeping. This paper proposes a module perceiving oncoming vehicle behavior, as part of a larger behavior-tree structure for adaptive lane-keeping using data from a lidar sensor. The complete behavior tree would include road curvature, speed limits, road types (rural, urban, interstate), and the proximity of objects or humans to lane markings. It also accounts for the lane-keeping behavior, type of adjacent and opposing vehicles, lane occlusion, and weather conditions. The algorithm was evaluated using

Soloiu, Valentin, Mehrzed, Shaen, Kroeger, Luke, Pierce, Kody, Sutton, Timothy, Lange, Robin

Different Methods-Based Curvature Estimation and its Effect on the Lane Centering Performance

2025-01-8055

04/01/2025

Lateral driving features used in Advanced Driver Assistance Systems (ADAS) rely heavily on inputs from the vehicle's surroundings and state information. A critical component of this state information is the curvature of the Ego Vehicle, which significantly influences performance. Curvature is often utilized in lateral trajectory generation and serves as a key element of the lateral motion controller. However, obtaining accurate curvature data is challenging due to the scarcity of sensors that directly measure this parameter. Instead, curvature is typically derived from various vehicle signals and additional sensor data, often employing sophisticated estimation techniques. This paper discusses several methods for estimating vehicle curvature using diverse information sources, evaluates their effectiveness, and investigates their impact on lateral feature performance, while analyzing the associated challenges and advantages.

Awathe, Arpit, Varunjikar, Tejas, Jain, Arihant

Numerical Investigation of Fuel Injection Strategies for Ammonia-Diesel Dual-Fuel Engine

2025-01-8368

04/01/2025

This study numerically investigates ammonia-diesel dual fuel combustion in a heavy-duty engine. Detailed and reduced reaction mechanisms are validated against experimental data to develop injection timing maps aimed at maximizing indicated thermal efficiency (ITE) while mitigating environmental impacts using stochastic reactor model (SRM). The equivalence ratio, ammonia energy share (AES), injection timing, and engine load are varied to optimize combustion efficiency and minimize emissions. The results demonstrate that advancing injection timing reduces ITE due to heightened in-cylinder temperatures, resulting in increased heat losses through walls and exhaust gases. Maximum chemical efficiency is observed at an equivalence ratio near 0.9 but decreases thereafter, influenced by ammonia’s narrow flammability range. Emission analysis highlights significant reductions in Global Warming Potential (GWP) and Eutrophication Potential (EP) with higher AES, driven by decreased CO2 and nitrogen

Karenawar, Shivraj Anand, Yadav, Neeraj Kumar, Maurya, Rakesh Kumar

The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics

EPR2025003

03/31/2025

Advanced driver assistance systems (ADAS) and automated driving systems (ADS) continue to expand into the market at a rapid pace. As improved (i.e., next generation) versions of these systems become available, they will continue to face many challenges in their implementation and benefits for safety and driving operations. The solution will involve many parties, including road safety professionals and researchers who see the potential in these systems but may have difficulties keeping up with them, and safety advocates who are calling for these systems to achieve higher levels of safety now. The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics explores these challenges that will fall on the National Highway Traffic Safety Administration (NHTSA) and automakers as they balance costs and benefits; establish reasonable regulations and standards; and determine how to improve, test, deliver, and use these systems successfully. Perhaps the most formidable challenge will

Chalmers, Seth

Smart Clusters: Functional Safety Concepts and Requirements

12-08-02-0018

03/28/2025

This study presents a detailed review of a contemporary safety concept for a smart cluster, comprising a multipurpose display and a head unit. It focuses on elucidating the fundamental regulatory requirements for smart clusters within the frameworks of the United States and the European Union, and draws connections to their functional safety requirements and concepts. The article explores a range of safety mechanisms and architectures designed to implement these proposed functional safety requirements. For each mechanism, we provide an in-depth analysis of its benefits and drawbacks, as well as a thorough explanation of its operational logic. This comprehensive evaluation offers valuable insights into developing safer and more efficient smart clusters in line with international regulatory standards.

Anisimov, Valentin, Babaev, Islam, Shinde, Chaitanya

Improving Safety of Autonomous Vehicles: A Verifiable Method for Graceful Degradation of Decision and Control Responsibilities

12-08-02-0021

03/24/2025

Developing safe and reliable autonomous vehicles is crucial for addressing contemporary mobility challenges. While the goal of autonomous vehicle development is full autonomy, up to SAE Level 4 and beyond, human intervention remains necessary in critical or unfamiliar driving scenarios. This article introduces a method for gracefully degrading system functionality and seamlessly transferring decision-making and control between the autonomous system and a remote safety operator when needed. This transfer is enabled by an onboard dependability cage, which continuously monitors the vehicle’s performance during its operation. The cage communicates with a remote command control center, allowing for remote supervision and intervention by a safety driver. We assess this methodology in both lab and test field settings in a case study of last-mile parcel delivery logistics and discuss the insights and results obtained from these evaluations.

Aniculaesei, Adina, Aslam, Iqra, Zhang, Meng, Buragohain, Abhishek, Vorwald, Andreas, Rausch, Andreas

Advancing Road Sensing Technology: Functional Observer for Vehicle Road Profile Estimation

12-08-04-0038

03/24/2025

Having an in-depth comprehension of the variables that impact traffic is essential for guaranteeing the safety of all drivers and their automobiles. This means avoiding multiple types of accidents, particularly rollover accidents, that may have the capacity of causing terrible repercussions. The non-measured factors in the system state can be estimated employing a vehicle model incorporating an unknown input functional observer, this gives an accurate estimation of the unknown inputs such as the road profile. The goal of the proposed functional observer design constraints is to reduce the error of estimation converging to a value of zero, which results in an improved calculation of the observer parameters. This is accomplished by resolving linear matrix inequalities (LMIs) and employing Lyapunov–Krasovskii stability theory with convergence conditions. A simulator that enables a precise evaluation of environmental factors and fluctuating road conditions was additionally utilized. This

Saber, Mohamed, Ouahi, Mohamed, Naami, Ghali, El Akchioui, Nabil

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

2025-01-7150

03/19/2025

With the development and maturity of new generation digital technologies such as artificial intelligence, Internet of Things, and 5G mobile communication, their integration with physical products is becoming increasingly seamless. Automobiles serve as a prime example in this regard. In recent years, automated vehicle (AV) technologies have emerged as a prominent focal point, witnessing an escalating acceptance in the market and a growing number of self-driving vehicles on the roads, existing roads are primarily designed for traditional human-driven vehicles (HVs). Due to the differences in perception between automated systems and human drivers, it is essential to assess AVs' feasibility to current road infrastructure. This paper analyzes the safety and comfort of automated vehicles equipped with adaptive cruise control systems (ACC-AVs) on longitudinal road profiles from the perspective of vehicle dynamics. Firstly, a co-simulation platform integrating PreScan, CarSim, and Simulink

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

Research on Automatic Emergency Braking Pedestrian System Considering Road Adhesion Coefficient

10-09-03-0022

03/08/2025

To further optimize the automatic emergency braking for pedestrian (AEB-P) control algorithm, this study proposes an AEB-P hierarchical control strategy considering road adhesion coefficient. First, the extended Kalman filter is used to estimate the road adhesion coefficient, and the recursive least square method is used to predict the pedestrian trajectory. Then, a safety distance model considering the influence factor of road adhesion coefficient is proposed to adapt to different road conditions. Finally, the desired deceleration is converted into the desired pressure and desired current to the requirements of the electric power-assisted braking system. The strategy is verified through the hardware-in-the-loop (HIL) platform; the simulation results show that the control algorithm proposed in this article can effectively avoid collision in typical scenarios, the safe distance of parking is between 0.61 m and 2.34 m, and the stop speed is in the range of 1.85 km/h–27.64 km/h.

Wang, Zijun, Wang, Liang, Ma, Liang, Sun, Yong, Li, Chenghao, Yang, Xinglong

Chinese Ride-Hailing Drivers’ Adoption Intention of New Risk Visualization System: A Questionnaire Analysis Based on the Improved UTAUT2 Model

2025-01-7122

02/21/2025

Advanced Driver Assistance Systems (ADAS) have achieved significant progress worldwide, with the primary goals of enhancing driving safety, improving operational efficiency, and supporting vehicle automation. These systems are increasingly dependent on intelligent connected technologies, which enhance drivers' awareness and capacity to recognize and respond to potential road hazards in real-time. Within ADAS, risk visualization systems have become especially crucial, as they provide immediate alerts, thereby promoting safer driving behavior and enabling drivers to make more informed decisions on the road. This study expands upon existing frameworks by investigating the adoption of advanced risk visualization systems among Chinese ride-hailing drivers through an improved Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The improved model introduces two novel constructs: Technology Trust and Perceived Risk, addressing critical gaps in understanding safety-critical

Zhang, Jiayan, Li, Linheng, Pan, Yan, Qu, Xu, Ran, Bin

Identifying High-Risk Situations in Right-Turn Phase of Heavy-Duty Vehicles Using Naturalistic Driving Data

2025-01-7209

02/21/2025

The interaction between heavy-duty vehicles turning right and non-motor vehicles going straight has led to severe traffic crashes. It is essential to evaluate the driving risk of heavy-duty vehicles in the right-turn phase. Increasingly, studies have explored some indicators associated with driving risk. Based on naturalistic driving data of 121 heavy-duty vehicles in Nanjing, this research combined factor analysis and K-means cluster algorithm to assess the driving risk of two scenarios, one without a blind spot warning and another with a blind spot warning during the right-turn phase. The results have concluded the driving characteristics of heavy-duty vehicles under different risk levels. It formed a set of driving risk level assessment methods for heavy-duty vehicles in the right-turn phase. This evaluation method is expected to identify high-risk right-turn behaviors of heavy-duty vehicles and provide some insights to practitioners for traffic management.

Zhang, Hedi, Fu, Yuanhang, Ma, Yongfeng, Chen, Shuyan

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

02-18-02-0009

02/19/2025

This study presents the development and integration of a vehicle mass estimator into the ZF’s Adaptive Cruise Control (ACC) system. The aim is to improve the accuracy of the ACC system’s torque control for achieving desired speed and acceleration. Accurate mass estimation is critical for optimal control performance, particularly in commercial vehicles with variable loads. The incorporation of such mass estimation algorithm into the ACC system leads to significant reductions in the error between requested and measured acceleration during both flat and uphill driving conditions, with or without a preceding vehicle. The article details the estimator’s development, integration, and validation through comprehensive experimental testing. An electric front-wheel drive van was used. The vehicle’s longitudinal dynamics were modeled using D’Alembert’s principle to develop the mass estimation algorithm. This algorithm updates the mass estimate based on specific conditions: zero brake torque, high

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

Leveraging Triggering Conditions for Efficient Scenario-Based Testing of Automated Vehicles

12-08-04-0035

02/19/2025

Scenario-based testing has become a central approach of safety verification and validation (V&V) of automated driving. The standard ISO 21448: Safety of the intended functionality (SOTIF) [1] proposes triggering conditions (e.g., an occluded traffic sign) as a new aspect to be considered to organize scenario-based testing. In this contribution, we discuss the requirements and the strategy of testing triggering conditions in an iterative, SOTIF-oriented V&V process. Accordingly, we illustrate a method for generating test scenarios for evaluating potential triggering conditions. We apply the proposed method in a two-fold case study: We demonstrate how to derive test scenarios and test these with a virtual automated driving system in simulation. We provide an analysis of the testing result to show how triggering condition-based testing facilitates spotting the weakness of the system. Besides, we exhibit the applicability of the method based on multiple triggering conditions and nominal

Zhu, Zhijing, Philipp, Robin, Howar, Falk

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