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, Advanced Driver Assistance Systems (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

Adil, MD Shahzad, C M, Mithun, Mohammed, Riyazuddin, R, Prasath

Simulation and Experimental: Enhanced Stability Control of Electric Vehicle Based on Phase Plane Boundary Analysis

10-09-02-0017

05/05/2025

To optimize vehicle chassis handling stability and ride safety, a layered joint control algorithm based on phase plane stability domain is proposed to promote chassis performance under complicated driving conditions. First, combining two degrees-of-freedom vehicle dynamics model considering tire nonlinearity with phase plane theory, a yaw rate and side slip angle phase plane stability domain boundary is drew in real time. Then based on the real-time stability domain and hierarchical control theory, an integrated control system with active front steering (AFS) and direct yaw moment control (DYC) is designed, and the stability of the controller is validated by Lyapunov theory. Finally, the lateral stability of the vehicle is validated by Simulink and CarSim simulations, real car data, and driving simulators under moose test and pylon course slalom test. The experimental results confirm that the algorithm can enhance the maneuverability and ride safety for intelligent vehicles.

Liao, Yinsheng, Zhang, Zhijie, Su, Ailin, Zhao, Binggen, Wang, Zhenfeng

Safety, ADAS, and Cybersecurity: Vol. 3

EPRCOMPV06202404/30/2025

Ahmed, Qadeer, Renganathan, Vishnu, O'Neil, Anne, Kemper, Bart

Infrastructure Enablers for Automation: Vol. 3

EPRCOMPV042024

04/30/2025

Coyner, Kelley, Bittner, Jason

Automated Vehicles and Infrastructure Enablers: Dedicated Lanes

EPR2025007

04/24/2025

Dedicated lanes provide a simpler operating environment for ADS-equipped vehicles than those shared with other roadway users including human drivers, pedestrians, and bicycles. This final report in the Automation and Infrastructure series discusses how and when various types of lanes whether general purpose, managed, or specialty lanes might be temporarily or permanently reserved for ADS-equipped vehicles. Though simulations and economic analysis suggest that widespread use of dedicated lanes will not be warranted until market penetration is much higher, some US states and cities are developing such dedicated lanes now for limited use cases and other countries are planning more extensive deployment of dedicated lanes. Automated Vehicles and Infrastructure: Dedicated Lanes includes a review of practices across the US as well as case studies from the EU and UK, the Near East, Japan, Singapore, and Canada. Click here to access the full SAE EDGETM Research Report portfolio.

Coyner, Kelley, Bittner, Jason

SAE TOMORROW TODAY: Advancing the Path to Full Autonomy

1351104/21/2025

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

A Two-Step Approach for Tire Lateral Force Observation for Motorcycles

2024-32-0043

04/18/2025

This study presents a two-step method for estimating motorcycle tire lateral forces, which are critical to the safety of driver assistance systems. In the pre-filtering stage, a partial attitude of the motorcycle is estimated using a Kalman filter and a kinematic model. In the observation stage, the side slip angle and subsequently the tire lateral forces are provided by a sliding mode observer. It extends previous research by incorporating both out-of-plane and in-plane dynamics. The paper also proposes an approach for selecting the Kalman filter parameters. An approach to identify the stochastic sensor errors of the inertial measurement unit is presented. The identified parameters are used as a basis for the selection of the covariances. The overall study provides a practical implementation strategy and demonstrates its applicability in real-world scenarios. The experiments show the results of the lateral force estimation and its relation to the friction ellipse. The effectiveness of

Winkler, Alexander, Grabmair, Gernot, Reger, Johann

Analysis of Lane Departure Caused by Inadequate Motorcycle Driving Maneuvers Due to Road Alignment

2024-32-0034

04/18/2025

There are many riders who drive motorcycles on winding mountain roads and caused single motorcycle traffic accidents on curved roads by lane departure. Driving a motorcycle requires subtle balancing and maneuvering. In this study, in order to clarify the influence of lane departure caused by inadequate driving maneuvers against road alignment, the authors analyzed the required curve initial operation and driving maneuvers in curves depending on the traveling speed using a kinematics simulation for motorcycle dynamics. In addition, it was analyzed how inadequate driving maneuvers for curved roads can easily cause lane departure. As a result, it shows that the steering maneuvers and the lean of motorcycle body during the curves are highly affected by the vehicle speed, and the required maneuvers increases rapidly with increasing speed. The inadequate maneuver in the curves, especially for the lean of motorcycle body and steering torque, even by 10%, may cause failure to follow the

Kuniyuki, Hiroshi, Takechi, So

TOC

99-07-02-0TOC

04/17/2025

TOC

Tobolski, Sue

Learned Human Eye Gaze for Object Tracking: A Preliminary Study

09-13-01-0004

04/15/2025

Visual object tracking technology is the core foundation of intelligent driving, video surveillance, human–computer interaction, and the like. Inspired by the mechanism of human eye gaze, a new correlation filter (CF) tracking algorithm, named human eye gaze (HEG) tracking algorithm, was proposed in this study. The HEG tracking algorithm expanded the tracking detection idea from the traditional detection-tracking to detection-judging-tracking by adding a judging module to check the initial and retrack the unreliable tracking result. In addition, the detection module was further integrated into the edge contour feature on the basis of the HOG (histogram of oriented gradients) extracting feature and the color histogram to reduce the sensitivity of the algorithm to factors such as deformation and illumination changes. The comparison conducted on the OTB-2015 dataset showed that the overall overlap precision, distance precision, and center location error of the HEG tracking algorithm were

Jiang, Yejie, Jiang, Binhui, Chou, Clifford C.

Automatic Emergency Braking (AEB) System Performance Testing

J3087_202504 (Current)

04/14/2025

This document describes an SAE Recommended Practice for Automatic Emergency Braking (AEB) system performance testing which: Establishes uniform vehicle level test procedures Identifies target equipment, test scenarios, and measurement methods Identifies and explains the performance data of interest Does not exclude any particular system or sensor technology Identifies the known limitations of the information contained within (assumptions and “gaps”) Is intended to be a guide toward standard practice and is subject to change on pace with the technology Focuses on “Vehicle Front to Rear, In Lane Scenarios” expanded to include additional offset impacts This document describes the equipment, facilities, methods, and procedures needed to evaluate the ability of Automatic Emergency Braking (AEB) systems to detect and respond to another vehicle, in its forward path, as it is approached from the rear. This document does not specify test conditions (e.g., speeds, decelerations, clearance gaps

Active Safety and Driver Support Systems Standards Committee

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

Systematic Predictive Energy Management Algorithm Testing in MIL/SIL with Realistic Traffic Simulation

2025-01-8583

04/01/2025

Energy management strategy is essential for HEV’s to achieve an optimum of energy consumption. With predictive energy management, taking future vehicle speed predicted from ADAS map information, in-vehicle navigation traffic flow status information, and current speed into account, one could anticipate a considerable improvement in energy-saving. The major validating approach widely adopted for energy management algorithms nowadays is real-world vehicle testing, of which the economic and time costs are relatively high. Moreover, with advanced algorithms featuring AI coming into light, putting forward higher requirement in the richness of test cases, the drawback in coverage of vehicle testing is revealed. This paper proposed a MIL/SIL testing approach for predictive energy management algorithms, providing a partial replacement to, and overcome the limitations of, vehicle testing. In the testing setup, random traffic generated by MATLAB® based on real-time traffic condition will be taken

Yan, Yue, Ma, Xiudan, Wei, Xinli, Xiong, Jie, Deng, Yunfei

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2025-01-8033

04/01/2025

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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