Browse Topic: Pedestrian safety

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Cooperative Edge Extension for Automated Driving Level 4 and Beyond2026-01-07767/1/2026
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
Lüntzel, Vitus, Lukezic, Nikola, Kraus, David, Seidel, Luca, Beck, Maximilian, Schindewolf, Marc, Sax, Eric
Traumatic Brain Injury in Vulnerable Road Users: Analysis of German In-Depth Crash Data to Inform Targeted Prevention2026-22-00035/28/2026
Road traffic crashes are a major cause of traumatic brain injury (TBI), particularly among vulnerable road users (VRUs). However, current injury prevention strategies often overlook the heterogeneity of TBI—which include various injury types and severities—leading to an oversimplified approach to evaluating helmets and safety systems in regulations and ratings. To identify priority TBI types and severities in VRUs and to inform targeted prevention strategies, the German In-Depth Accident Study database was analyzed and a pathoanatomic classification system, i.e., Abbreviated Injury Scale, was employed. AIS 2 (moderate) TBIs account for 70-80% of all brain injuries across VRU groups, nearly half of which are concussions. For helmeted cyclists, milder TBIs are at a greater percentage than for unhelmeted cyclists. These findings highlight the need for expanding prevention efforts to include AIS 2+ injuries. Key injury types observed include concussion (with and without loss of consciousness), skull base fracture, subdural hemorrhage, contusion and laceration. New mechanism-specific injury criteria may be needed to address these injuries. The strong similarity in injury type ranking among different road users (the Kendall’s tau values ranged from 0.90 to 0.93) suggests similar needs for injury prevention. A new brain injury assessment criterion may serve all road user types.
Meng, Shiyang, Schindler, Ron, Kleiven, Svein, Lubbe, Nils
Characterization of Pedestrian Automatic Emergency Braking Acceleration in Modern Light Vehicles: Potential Implications for Occupant Safety09-14-01-00075/4/2026
The objective of this study was to characterize and compare pedestrian automatic emergency braking (PAEB) pulses in modern light vehicles to understand the loading environment that vehicle occupants are being exposed to during PAEB maneuvers. PAEB tests (n = 8008) conducted using 2018–2023 vehicle model years were analyzed. Pulse, vehicle, and impact characteristics (e.g., jerk, peak acceleration, pedestrian scenario, etc.) were derived from each PAEB test. Two k-means clustering analyses were used to group PAEB pulses with and without target collisions based on their similarity between characteristics. One-way ANOVA and Kruskal–Wallis tests were performed on the PAEB pulse characteristics to examine differences between clusters (p < 0.05). Two non-collision clusters (NC1 and NC2) were identified for PAEB pulses without collisions: NC1 had a statistically significant lower jerk (0.8 ± 0.4 g/s) and peak acceleration (1.0 ± 0.1 g) compared to NC2 (1.6 ± 0.8 g/s and 0.9 ± 0.1 g, respectively, p < 0.001). NC1 was mostly represented by stationary adult (88.6%), 60 km/h (99.5%), and 40 km/h (62.2%) tests. NC2 was mostly represented by crossing scenarios (child: 92.3%; adult: 70.5%) and 20 km/h (96.2%) tests. Three collision clusters (C1, C2, and C3) were identified for PAEB pulses with collisions. C3 showed a greater jerk (1.5 ± 0.8 g/s) compared to C1 (0.9 ± 0.6 g/s) and C2 (1.1 ± 0.9 g/s, p < 0.001). These results suggest that with successful avoidance, deceleration begins earlier with higher speeds and a stationary pedestrian, resulting in potentially milder loading conditions for vehicle occupants (i.e., lower jerk in NC1 vs NC2). With unsuccessful avoidance, in daytime pedestrian crossing scenarios, lower impact speeds were observed, resulting in potentially non-optimal loading conditions (i.e., higher jerk and peak acceleration in C3) for vehicle occupants. At night with low beams, C2 may result in advantageous loading conditions for vehicle occupants (i.e., lower jerk and peak acceleration), but it may lead to the worst outcome for pedestrians (i.e., greatest impact speed).
Witmer, Maitland, Kidd, David, Graci, Valentina
Modification and Validation of a Finite Element Model of a Pedestrian Dummy09-14-01-00125/4/2026
Aims of the research This study aims to modify the lower body (the pelvis, thigh, and leg) of the mid-sized male pedestrian dummy FE model by considering the latest version of the physical dummy and to evaluate both the accuracy by comparing test results of the past studies and the biofidelity specified in SAE J2782 in both component and full-scale validations. Methods 1 Component validation The validation of the modified pelvis model was performed in dynamic lateral compression simulations. The sacrum and the pubis force-deflection responses of the iliac or the acetabulum impact were measured. The modified thigh and leg models were evaluated in a dynamic 3-point lateral bending simulation, measuring the force-deflection responses. The results from the simulations were compared with test results and the biofidelity requirements. 2 Full-scale validation The whole-body model was updated by incorporating these modified component models. The model of the generic buck developed for the assessment of pedestrian whole-body impact response and specified in SAE J3093 was used in this study. The buck model was made to collide with the full-scale dummy model at 40 km/h laterally. The trajectories of the head, upper spine, mid-thorax, and pelvis were measured and compared with those of the test results and the biofidelity requirements. Results The force-deflection responses from the pelvis, thigh, and leg models were similar to those of the test results, indicating they almost fell within the biofidelity requirements. As the results of the full-scale simulation, the trajectories of the head, upper spine, mid-thorax, and pelvis showed a strong agreement with those of the test results, indicating almost the same tendency as the biofidelity corridors, except for that of the pelvis. Conclusions As the results of component and full-scale validations, the equivalences of the modified pedestrian dummy model to test results and the biofidelity were confirmed in most cases.
Asanuma, Hiroyuki, Gunji, Yasuaki, Mori, Fumie, Nagashima, Akiko
Characterization of Fatal Cyclist Collisions Involving Nine Vehicle Types Traveling at Low and High Speeds in Japan09-14-01-00055/4/2026
To reduce traffic fatalities through vehicle safety measures, particular attention must be given to cyclist-related fatalities. Clarifying the characteristics of hazardous events leading to cyclist fatalities, not only by vehicle speed range but also by vehicle type, is essential and should be based on analyses of real-world accident data. Accordingly, this study aimed to characterize fatal cyclist accidents involving vehicles traveling at low and high speeds in Japan. We used macro accident data from the Japanese Institute for Traffic Accident Research and Data Analysis covering the period from 2013 to 2022. Based on nine vehicle types, we investigated the effects of road type, vehicle behavior, and accident type on cyclist fatalities. Additionally, we identified the five most frequent accident scenarios separately for each low- and high-speed category. At signalized intersections, the proportions of cyclist fatalities involving vehicles traveling at low speeds were higher than those involving vehicles traveling at high speeds across all vehicle types. In contrast, on straight roads, the proportions at low speeds were lower than those at high speeds for all vehicle types. In the low-speed range, cyclist fatalities within the top five scenarios accounted for 65% of all fatalities, with the most frequent scenario occurring at signalized intersections during left-turn maneuvers, where heavy-duty trucks accounted for 86% of the fatalities. In the high-speed range, cyclist fatalities within the top five scenarios accounted for 71% of all fatalities. The most frequent high-speed scenario involved crossing collisions at unsignalized intersections when vehicles traveled straight, with light passenger cars and sedans accounting for 29% and 24% of the fatalities, respectively. These findings provide valuable insights for the development of targeted traffic safety regulations and vehicle technologies aimed at reducing vehicle–cyclist collisions across different speed ranges.
Matsui, Yasuhiro, Oikawa, Shoko
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis2026-01-01704/7/2026
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decision-making, yet their robustness to physical adversarial attacks, especially whether such attacks transfer across different VLM architectures, is not well understood and poses a practical risk when attackers do not know which model a vehicle uses. We address this gap with a systematic cross-architecture study of adversarial transferability in VLM-based driving, evaluating three representative architectures (Dolphins, OmniDrive, and LeapVAD) using physically realizable patches placed on roadside infrastructure in both crosswalk and highway scenarios. Our transfer-matrix evaluation shows high cross-architecture effectiveness, with transfer rates of 73–91% (mean TR = 0.815 for crosswalk and 0.833 for highway) and sustained frame-level manipulation over 64.7–79.4% of the critical decision window even when patches are not optimized for the target model. We further find asymmetric architecture-level risk, with Dolphins most vulnerable to incoming transfer attacks (VS = 0.82) and LeapVAD producing the most transferable patches (TO = 0.882), while models sharing CLIP-based vision encoders exhibit stronger bidirectional transfer. Overall, these results indicate that current VLM-based autonomous driving systems share systematic cross-architecture weaknesses that architectural diversity alone does not resolve, underscoring the need for defenses and design principles that explicitly account for transferability in safety-critical deployment.
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.
The Balance of Illumination and Glare Prevention of Upward Directed Lighting2026-01-05164/7/2026
This study examines the ongoing challenge of balancing sufficient forward illumination for vehicle operation with the need to limit glare experienced by other road users. This analysis specifically focuses on the portion of a headlight's beam pattern intensity distribution located above the horizontal plane, which is particularly relevant for lighting overhead signs and the upper portions of vulnerable road users but is also a potential contributor to glare. In particular, the study investigates how the adoption of LED headlamp technology has influenced upward-directed lighting relative to historical halogen beam intensity distributions. Two different comparative analyses were performed within this study. The first analysis was the calculation of intensity on targets positioned at multiple locations relative to the headlamps considering vehicle conditions. The second analysis was performed as at selected discrete points referenced directly to the headlamp and independent of vehicle height. The results of the study show that the changes in upward directed lighting due to transition to LED technology were inconsequential relative to historical variability of the halogen headlamp systems. Analysis of this data reveals trends in light distributions and can provide information for considerations of future headlamp strategies balancing the needs of drivers to illuminate potential vulnerable road users and the glare experienced by other road users.
Allen, Jodi Mary Jean
Safety-Centered Scenario Generation for Autonomous Vehicles2026-01-05294/7/2026
This paper presents a scenario generation framework that creates diverse, parametrized, and safety-critical driving situations to validate the safety features of autonomous vehicles in simulation [1]. By modeling factors such as road geometry, traffic participants, environmental conditions, and perception uncertainties, the framework enables repeatable and scalable testing of safety mechanisms, including emergency braking, evasive maneuvers, and vulnerable road user protection. The framework supports both regulatory and edge case scenarios, mapped to hazards and safety goals derived from Hazard Analysis and Risk Assessment (HARA), ensuring traceability to ISO 26262 functional safety requirements and performance limitations. The output from these simulations provides quantitative safety metrics such as time-to-collision, minimum distance, braking and steering performance, and residual collision severity. These metrics enable the systematic evaluation of evasive maneuvering as a safety feature, while highlighting system limitations and edgecase vulnerabilities. Integration of scenario-based simulation with safety engineering principles offers accelerated validation cycles, improved test coverage at reduced cost, and stronger evidence for regulatory and stakeholder confidence.
Chandra Shekar, Kiruthiga, Arab, Aliasghar
High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety2026-01-00344/7/2026
Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. Through this framework, CLFs constraints guarantee system stability so that the vehicle can track its reference trajectory, whereas CBFs constraints ensure system safety by letting vehicle avoiding potential collisions region with surrounding obstacles. Then by solving a QP problem, an optimal control command that simultaneously satisfies stability and safety requirements can be calculated. Three key bicyclist crash scenarios recorded in the Fatality Analysis Reporting System (FARS) are recreated and used to comprehensively evaluate the proposed autonomous driving bicyclist safety control strategy in a simulation study. Simulation results demonstrate that the HOCLF-HOCBF-QP controller can help the vehicle perform robust, and collision-free maneuvers, highlighting its potential for improving bicyclist safety in complex traffic environments.
Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin
Evaluation of PAEB Performance of a GM Vehicle Using EDR and Ground Truth Data2026-01-00544/7/2026
In order to determine the on-board EDR data recording characteristics of a GM vehicle, a 2023 GMC Sierra Denali was tested in several Pedestrian Automatic Emergency Braking (P-AEB) scenarios. Using a variety of test tools, including the STRIDE robotic platform and its onboard data systems, a GPS/IMU installed in the vehicle, and several camera units, the vehicle was put into collision imminent scenarios in which the crash avoidance systems were actuated. The flags in the EDR data, the order in which EDR events were written, and the correlation between the EDR and data recorded by the aforementioned external acquisition systems were examined for each test case. Testing was done in both forward and reverse scenarios and at low speeds only. These results provide a picture of the current state of the additional data available in current EDRs installed on GM vehicles equipped with P-AEB capability, as well as an insight into the accuracy and meaning of that data which should prove beneficial to the accident reconstruction community, among others.
Bartholomew, Meredith, Arnett, Michael, Guenther, Dennis
Development of Automotive Hood Using Multi-Material Double Injection Molding – An Overview2026-01-04764/7/2026
This paper presents the multidisciplinary development of a hybrid automotive hood manufactured using double-shot injection molding with overmolded brackets. Conventional steel and aluminum hoods, while structurally reliable, pose challenges in terms of weight reduction, pedestrian head protection, and manufacturing cost. Composite and thermoplastic alternatives supported by computational analysis and advanced molding processes provide opportunities to address these challenges. Finite element analysis (FEA) was employed to evaluate torsional and bending stiffness, locking load, and crashworthiness, while pedestrian headform simulations following ECE R127 and EEVC WG17 guidelines were conducted to assess compliance with safety regulations. Adhesion and bonding strength of overmolded polymer–polymer interfaces were studied to validate manufacturing feasibility. Results confirm that hybrid hoods fabricated using multi-material double-shot molding can achieve weight reductions of up to 30% compared with steel, maintain structural stiffness equivalent to aluminum, meet head injury criterion (HIC) thresholds, and reduce assembly complexity by consolidating brackets and reinforcements into a single integrated structure.
Ganesan, Karthikeyan, Seok, Sang Ho, Jo, Hyoung Han
From Stoplights to On-Ramps: A Comprehensive Set of Crash Rate Benchmarks for Freeway and Surface Street ADS Evaluation09-14-02-00033/31/2026
This article presents crash rate benchmarks for evaluating US-based automated driving systems (ADSs) for multiple urban areas, distinguishing between freeway and surface street crash rates, and breaking them down by crash severity and type. The purpose of this study was to extend prior benchmarks focused only on surface streets to additionally capture freeway crash risk for future ADS safety performance assessments. Using publicly available police-reported crash and vehicle miles traveled (VMT) data from Arizona, California, Georgia, and Texas, the methodology details the isolation of in-transport passenger vehicles, road type classification, and crash typology. Key findings revealed that freeway crash rates exhibit large geographic dependence variations with any-injury-reported crash rates being approximately three times higher in Atlanta (2.3 IPMM; the highest) when compared to San Diego (0.7 IPMM; the lowest). The results show the critical need for location-specific benchmarks to avoid biased safety evaluations and provide insights into the VMT required to achieve statistical significance for various safety impact levels. The distribution of crash types depended on the outcome severity level. Higher severity outcomes (e.g., fatal crashes) had a larger proportion of single-vehicle, vulnerable road users (VRUs) and opposite-direction collisions compared to lower severity (police-reported) crashes. Given heterogeneity in crash types by severity, performance in low-severity scenarios may not be predictive of high-severity outcomes. These benchmarks are additionally used to quantify at the required mileage to show statistically significant deviations from human performance. Future work investigating the underlying factors influencing crash rates in each geographical area will further enhance future benchmarking efforts (by identifying potential confounders to account for when matching exposure between baseline and ADS data). This is the first article to generate freeway-specific benchmarks for ADS evaluation and provides a foundational framework for future ADS benchmarking by evaluators and developers.
Scanlon, John M., McMurry, Timothy L., Chen, Yin-Hsiu, Kusano, Kristofer D., Victor, Trent
Human-Like Automatic Braking Algorithm Based on Kinematic Cue: Yield Behavior Analysis and Fuzzy Controller Design10-10-03-00192/25/2026
Pedestrians are among the most vulnerable participants in traffic, particularly when crossing the road. Extensive research has been conducted globally on the yielding behavior analysis of vehicle–pedestrian interaction and the design of automatic vehicle braking systems to mitigate pedestrian casualties. However, few studies have comprehensively addressed lateral risks using implicit kinematic cues in pedestrian–vehicle interactions. Moreover, the design of collision avoidance systems has rarely taken into account driving behavior, along with the pedestrian’s kinematics and crossing behavior. This article presents a human-like automatic braking fuzzy control strategy for pedestrian–vehicle collision avoidance, combining the advantages of professional driver emergency braking behavior and kinematic interaction cues. First, a high-fidelity driving simulator is used to investigate the yielding behavior of pedestrian–vehicle interaction when pedestrians cross the road. Second, the intrusion position (XP), as a new lateral risk index, is designed to overcome the limitation of lateral distance in complex pedestrian–vehicle interaction scenarios. Various metrics are considered to analyze driver emergency braking behavior using statistical methods from both lateral and longitudinal aspects. Subsequently, based on driver braking behavior, the human-like automatic braking fuzzy control strategy is proposed. Finally, simulation examples verify the reliability of the analysis results and the proposed controller’s effectiveness. Compared with a conventional automatic braking system, the timing of interventions of the proposed system is on average 2.9 s earlier, and the braking deceleration is reduced by 3.59 m/s2.
Zhang, Wenyan, Huang, Xiaorong, Sun, Shulei, Fu, Kairong, Xiong, Qing, Huang, Haibo
Accidental Dooring Warning and Assist System2026-28-00532/12/2026
Dooring accidents occur when a vehicle door is opened into the path of an approaching cyclist, motorcyclist, or other road user, often causing serious collisions and injuries. These incidents are a major road safety concern, particularly in densely populated urban areas where heavy traffic, narrow roads, and inattentive behavior increase the likelihood of such events. To address this challenge, this project presents an intelligent computer vision based warning system designed to detect approaching vehicles and alert occupants before they open a door. The system can operate using either the existing rear parking camera in a vehicle or a USB webcam in vehicles without such a feature. The captured live video stream is processed by a Raspberry Pi 4 microprocessor, chosen for its compact size, low power consumption, and ability to support machine learning frameworks. The video feed is analyzed in real time using MobileNetSSD, a lightweight deep learning object detection model optimized through TensorFlow Lite to ensure smooth and efficient processing even on resource- constrained hardware. Detected objects are classified, and the relative distance of approaching vehicles is estimated based on bounding box dimensions and simple geometric calculations. If a vehicle is detected within a predefined safety distance, the system immediately displays a clear visual warning on an in-vehicle screen, giving occupants enough time to delay opening the door and avoid a potential collision. The system was successfully implemented and tested on both a laptop and Raspberry Pi, demonstrating high accuracy, low latency, and minimal hardware requirements, making it cost effective and scalable. Looking forward, the design allows for future upgrades such as automatic door locking when a hazard is detected, audio and haptic alerts for greater situational awareness, integration with other vehicle sensors for improved detection accuracy, and seamless incorporation into commercial advanced driver assistance systems, providing a practical, affordable, and effective solution to enhance road safety and protect vulnerable road users.
C, Jegadheesan, T, Karthi, Gurusamy, Varun Sankar, Balraj, Tharun, Murugaiya, Tamilselvan
Method and System of Detecting Obstacle around Vehicle Door2026-26-00261/16/2026
Vehicle door-related accidents, especially in urban environments, pose a significant safety risk to pedestrians, infrastructure and vehicle occupants. Conventional rear view systems fails to detect obstacles in blind spots directly below the Outside Rear View Mirror (ORVM), leading to unintended collisions during door opening. This paper presents a novel vision-based obstacle detection system integrated into the ORVM assembly. It utilizes the monocular camera and a projection-based reference image technique. The system captures real-time images of the ground surface near the door and compares them with calibrated reference projections to detect deviations caused by obstacles such as pavements, potholes or curbs. Once such an obstacle is detected the vehicle user is alerted in the form of a chime.
Bhuyan, Anurag, Khandekar, Dhiraj, Jahagirdar, Shweta
Analysis of Pedestrian Accidents in India2026-26-06931/16/2026
Pedestrian safety is a critical concern in India, where rapid urbanization, increased vehicular traffic, and inadequate infrastructure pose significant risks to pedestrians. This study aims to analyze pedestrian accidents across various regions in India, drawing insights from comprehensive accident data. By examining accident patterns, risk factors, and contributing variables, we seek to inform policy recommendations and enhance pedestrian safety measures.
Howlader, Ashim, Mehta, Pooja
Leveraging Road Accident Database to Enhance ADAS Features and Testing Protocols in Context of Indian Driving Conditions2026-26-00141/16/2026
ADAS i.e. Advanced Driver Assistance Systems are pivotal towards amplifying road safety by reducing human error and assisting drivers in critical situations. Most major ADAS technologies are developed and validated using data and test scenarios that are predominantly based on the driving conditions and road environments of developed countries. However, in a country like India, where driving behavior, traffic dynamics, road infrastructure, and accident characteristics differ significantly, the ADAS technologies and test scenarios validated by different forums create a critical gap in deploying such systems on vehicles to work on Indian roads. The major aim of this study was to determine and generate India-specific ADAS test scenarios from the Road Accident Sampling System India (RASSI) database, available MoRTH reports, and data from previously executed ADAS test cases. Through this research, we propose a methodology to identify, extract, and analyze accident scenarios pertaining to the Indian driving environment and their usefulness in developing ADAS technologies to achieve the best results when deployed on vehicles in India. The RASSI database provides exposure to various data factors related to accidents from accident reconstructions, such as velocities at different timestamps, positions of vehicles with respect to time, and pre-crash maneuvers. In addition, the data from MoRTH reports regarding different categories of vehicle collisions will be analyzed extensively for scenario identification and generation for the evaluation of different ADAS functionalities, like Forward Collision Warning, Lane departure warning (LDW), Automatic emergency braking (AEB), and Blind spot detection System. Furthermore, the data generated from the available ADAS Test validations were examined to verify the different parameters related to specific ADAS features. For the purpose of authentication of the pinpointed scenarios, they will be simulated in the simulation software named IPG Carmaker for affirmation of the effectiveness of ADAS functionality if present in the India specific identified accident test scenarios. With the purpose of minimizing the localization gap in ADAS testament, this study provides a data driven, standardized approach towards generation of test scenarios adapted to Indian driving environment.
Adhikari, Mayur, Bhagat, Ajinkya, Verma, Harshal, Kale, Jyoti Ganesh, Karle, Ujjwala, Sharma, Chinmaya
Enhancing Pedestrian Safety: LIDAR-Based Deployment of Front LID/Bonnet and Airbag Systems in Heavy Commercial Vehicles During Low-Speed Collisions2026-26-00061/16/2026
As urban population continues to grow, the safety of Vulnerable Road Users (VRUs) particularly in the presence of Heavy Good Vehicles (HGVs) has emerged as a critical concern. Research indicates that VRUs are at a 50% higher risk of fatal injury in collisions involving HGVs compared to passenger cars. To address this issue, this study proposes a novel pedestrian protection system that integrates LiDAR (Light Detection and Ranging) technology with a reusable airbag system to mitigate the severity of collisions. The proposed solution adopts a twofold approach for enhancing VRU protection in scenarios involving HGVs. In both approaches, LiDAR sensors are used to generate a real-time 3D model of the vehicle’s surroundings, enabling accurate VRU detection and predictive collision analysis. Scenario 1: When vehicle speed exceeds the first threshold and a collision is unavoidable, the onboard ECU activates front lid actuators, extending the vehicle's front lid which can be retracted back to its original position manually. Scenario 2: If vehicle speed exceeds a second, higher threshold, the system not only actuates the front lid but also deploys a non-pyrotechnic reusable airbag using compressed air and turbojet canisters. The extended front lid creates a space between the VRU’s head and the vehicle's hard structures, managing energies and reducing the risk of severe body injuries. The reusable airbag, covering the front lid’s corners, adds an additional cushioning effect at higher speeds. Headform impact simulations conducted on the truck front demonstrated the effectiveness of the proposed active front-lid mechanism. Six impact locations were evaluated using an LSTC® pedestrian headform, and the deployed front-lid configuration consistently showed notable reductions in Head Injury Criterion (HIC) values compared to the undeployed state. In particular, the additional deformation space created during deployment significantly reduced the severity of head impact with the underlying hard structures showing improvement in the performance. By combining advanced sensing with active impact mitigation technologies, this system offers a proactive, sustainable solution to enhance VRU safety in urban environment. The integration of LiDAR-equipped HGVs with reusable airbags represents a significant step forward in reducing traffic-related VRU injuries and fatalities.
Patil, Uday, Sriharsha, Viswanath, Pillai, Rajiv
Intelligent Child-Safety Inhibition Framework for Preventing Accidental Vehicle Activation2026-26-00131/16/2026
Modern vehicle technologies such as keyless entry, push-button start, digital switches have made it easier and more convenient to operate cars. However, this ease of operation has also introduced new safety concerns, particularly the increased risk of accidental operations by children. This can lead to unintentional vehicle movement, injuries, and even fatalities. Existing safety features (e.g., unattended child presence alarms) mitigate entrapment risks but do not prevent children from unintentionally starting or shifting while inside. This paper proposes implementation of a solution for child-safety system which inhibits certain functionalities to prevent accidental operations by underage occupants. The proposed system combines multiple existing technologies like weight sensors, seat position detection, facial recognition, in vehicle camera tracking to determine the child presence. With this, certain operations can be temporarily inhibited, or the vehicle can ask for secondary confirmation on the Infotainment unit. Unlike existing safety mechanisms that focus primarily on the protection of children in vehicles, this mechanism adds an intelligent safeguard against unintended vehicle operation by minors providing an extra layer of safety. The paper discusses the technical implementation and integrating of such a system into current vehicle architectures, examining potential benefits for occupant and pedestrian safety. Thus, the solution tries to fill the under-addressed safety risk in modern cars.
Mote, Vaishal, Garg, Mudit, Pasupuleti, Raju
Jerk Evaluation Criteria for Pedestrian Head Impact on Windshield and Technological Requirement to Meet the Criteria2026-26-00231/16/2026
The safety of vulnerable road users, particularly pedestrians, cyclists, and motorcyclists, is a paramount concern in automotive design and regulation. In India, the situation is particularly alarming, with pedestrians being the second highest victims of road accidents, as evidenced by over 32,825 reported pedestrian accidents and 4,836 cyclist fatalities in 2022, excluding two-wheeler motorcyclists. On a global scale, the prevalence of such incidents has prompted European countries to introduce new regulatory requirements, such as ECE R127.03. This regulation encompasses the evaluation of pedestrian head form impacts on windshields, assessing the typical behavior of glass through jerk criteria following initial contact, in conjunction with the existing Head Injury Criterion (HIC) evaluation for pedestrian head forms. These criteria’s are meticulously designed to ensure that both acceleration and jerk remain within safe limits to reduce the severe risk of severe injury to head of vulnerable road users, thereby providing a more comprehensive approach to pedestrian protection. This Paper focuses into a detailed understanding of the new evaluation criteria for head impact on windshield by describing its benefits and principal design requirements for vehicles to meet these criteria. To comprehend these fundamental design requirements, an initial study was conducted on existing vehicles through physical testing, simulation, and correlation. Detailed simulations were performed, and the correlated model was subsequently utilized to identify the design modifications necessary to meet these criteria. Through an exhaustive analysis of test data and regulatory standards, this paper explores the effect of head impact on windshield of vehicle. The findings underscore the importance of optimizing windshield materials and structures to achieve the desired behavior. This research contributes to the ongoing efforts to enhance the safety of vulnerable road users and provides valuable insights for automotive manufacturers in their approach to meeting windshield requirements and developing safer vehicles in collaboration with regulatory bodies.
Kumar, Ritik, A, Rajesh
Pre-Crash Characteristics of Motorized Two-Wheeler Crashes on Indian Roads2026-26-00341/16/2026
India has emerged as the world’s largest market for motorized two-wheelers (M2Ws) in 2024, reflecting their deep integration into the country’s transportation fabric. However, M2Ws are also a highly vulnerable road user category as according to the Ministry of Road Transport and Highways (MoRTH), the fatality share of M2W riders rose alarmingly from 27% in 2011 to 44% in 2022, underlining the urgency of understanding the circumstances that lead to such crashes. This study aims to investigate the pre-crash behavior and crash-phase characteristics of M2Ws using data from the Road Accident Sampling System – India (RASSI), the country’s only in-depth crash investigation database. The analysis covers 3,632 M2Ws involved in 3,307 crash samples from 2011 to 2022, representing approximately 5 million M2Ws nationally. Key variables examined include crash configuration, collision partner, road type, pre-event movement, travel speed, and human contributing factors. The study finds that straight-line travel, overtaking, and negotiating curves are among the most common pre-event movements preceding fatal crashes. Head-on and object-related collisions as well as crashes involving heavy vehicles show higher fatality rates. Human behaviors such as abrupt turns, over-speeding, unsafe overtaking, and riding under the influence of alcohol are major contributors to crash causation. The findings emphasize the need for targeted enforcement, rider training, and improved road infrastructure to reduce the burden of two-wheeler-related fatalities in India.
Govardhan, Rohan, Padmanaban, Jeya, Jethwa, Vaishnav
Determining Absence of Unreasonable Risk: Proposed Criteria for the Evaluation of an Automated Driving System Deployment12-09-04-00261/12/2026
This article provides an overview of how the determination of absence of unreasonable risk can be operationalized. It complements previous theoretical work published by existing developers of automated driving systems (ADS) on the overall engineering practices and methodologies for readiness determination. Readiness determination is, at its core, a risk assessment process. It is aimed at evaluating the residual risk associated with a new ADS deployment. The article proposes methodological criteria to ground the readiness review process for an ADS release. Specifically, it lists 12 readiness criteria connected with system safety, cybersecurity, verification and validation, collision avoidance testing, predicted collision risks, impeded progress, rules of the road compliance, vulnerable road users interactions, high-severity assessment, conservative estimate of severity, risk management, and field safety. The criteria presented are agnostic of any specific ADS technological solution and/or architectural choice, to support broad implementation by others in the industry. While intended to support the readiness evaluation for the deployment of an SAE Level 4 ADS, their use can also be generalized for lower levels of automation and combined with the unique human interaction challenges applicable to those levels. Following the presentation of the proposed criteria, the article continues with a discussion on governance and decision-making toward approval of a new release candidate for the ADS, inclusive of a discussion on factors that affect residual risk and risk management practices. The implementation of the presented criteria requires the existence of appropriate safety management practices in addition to many other cultural, procedural, and operational considerations. As such, the article is concluded by a statement of limitations for those wishing to replicate part or all of its content. The content presented here serves to inform important ongoing conversations on the topic of ADS certification and the standardization of approval guidelines in international regulatory contexts.
Favaro, Francesca Margherita, Schnelle, Scott, Fraade-Blanar, Laura, Victor, Trent, Peña, Mauricio, Webb, Nick, Broce, Holland, Paterson, Craig, Smith, Daniel
Arbe chipset improves automated driving decisions25AUTP12_0312/1/2025
Perception radar company Arbe was at IAA Mobility in Munich this year to press the case that customers can and should trust automated vehicles. One reason is the global trend of stricter regulations from the NHTSA, Euro NCAP, and in China, which now require automated vehicles to safely meet demanding use cases that are not covered by current sensors, according to Arbe co-founder and CTO Noam Arkind. Arkind told SAE Media that one such category is detecting vulnerable road users (VRU) in poor weather and lighting conditions. “We know from recent tests that a lot of Chinese cars, for example, failed VRU detections in the dark,” he said. “Camera alone doesn't really have reliable pedestrian detection in a dark situation. Radar is a great sensor. It's very sensitive. It's not dependent on weather conditions or lighting conditions, but it's noisy, it's low resolution, and it's hard to use.”
Blanco, Sebastian
Pedestrian Injury Case Reconstruction through Data Fusion and Machine Learning2025-22-000711/10/2025
The proportion of pedestrian injuries in motor-vehicle-crash-induced injuries in the U.S. has been increasing in recent years. Although extensive police-reported data on pedestrian injuries is available, the incomplete nature of the crash and injury information in these datasets presents a significant challenge for statistical injury analysis and pedestrian protection research. This study aims to address this issue by combining simulation data and field data to impute critical missing crash information in pedestrian crash cases through machine learning techniques. A total of 9,000 MADYMO simulations were generated using maximal projection design, incorporating variables such as pedestrian demographics, crash conditions, and vehicle impact parameters. Gaussian process (GP) surrogate models were trained to predict injury risks with simulation parameters calibrated using the complete crash information in the Pedestrian Crash Data Study (PCDS) dataset. Maximum likelihood estimations were then employed to impute the missing vehicle speed in Linked Michigan Trauma dataset. Validation involved comparing the imputed vehicle speed distribution with that of the PCDS dataset and verifying four CIREN cases reconstructed by both the proposed method and a physics-based approach. The histogram of the reconstructed vehicle speeds in Linked Michigan Trauma dataset highly correlated with that from the PCDS dataset. In the four CIREN cases, the absolute deviation between the reconstruction vehicle speeds from the proposed method and physics-based approach was 9 kph on average, with the predicted injury risks matched the observed AIS levels. These results support the use of machine learning for reconstructing missing crash data and enhancing pedestrian injury risk modeling.
Song, Xiaoyang, Sun, Wenbo, Hu, Jingwen, Flannagan, Carol, Karlow, Jared, Bowman, Patrick, Farooq, Iskander, Kalra, Anil
Pedestrian Accident ReconstructionR-59010/30/2025
As technology advances, so too must the methods we use to investigate and understand pedestrian collisions. Pedestrian Accident Reconstruction provides a much-needed update to the field—one that reflects the dramatic rise in digital evidence and the growing role of simulation in modern reconstructions. While earlier texts laid the foundation for understanding pedestrian impacts, they were written before the widespread use of video footage, onboard vehicle sensors, and powerful simulation software. This book addresses that shift. It introduces updated methods that incorporate event data recorders, surveillance video, dash cams, and advanced simulation tools like the PC-Crash multibody pedestrian model. These technologies allow for more precise, repeatable, and illustrative reconstructions than ever before. The book highlights when and how simulation can enhance an investigation—especially in complex scenarios such as roof vaults, sideswipes, or collisions that defy conventional equations. Though PC-Crash is the primary tool used throughout, the insights and principles are broadly applicable across simulation platforms. Written by experienced practitioners, this is not just a guide to software—it’s a thoughtful, practical resource for applying modern tools to real-world cases. Whether you're new to the field or looking to refine your practice, this book offers an essential perspective for today’s reconstruction professional.
Rose, Nathan, Carter, Neal
Customized Signalized Intersection Control for Pedestrian Safety Enhancement through Computer Vision Technologies09-13-02-00086/26/2025
The escalating complexity at intersections challenges the safety of the interaction between vehicles and pedestrians, especially for those with mobility impairments. Traditional traffic control systems detect pedestrians through costly technologies such as LiDAR and radar, limiting their adoption due to high costs and static programming. Therefore, the article proposes a customized signalized intersection control (CSIC) algorithm for pedestrian safety enhancement. This algorithm integrates advanced computer vision (CV) algorithms to detect, track, and predict pedestrian movements in real time, enhancing safety at a signalized intersection while remaining economically viable and easily integrated into existing infrastructure. Implemented at a key intersection in Bellevue, the CSIC system achieves a 100% pedestrian passing rate while simultaneously minimizing the average remaining walk time after crossings. The algorithm used in this study demonstrates the potential of combining CV with traditional traffic control mechanisms to create safer urban traffic environments for all road users.
Xia, Rongjing, Fang, Hongchao, Zhang, Chenyang
V2P-Based Speed Guidance Method for Avoiding Vehicle–Pedestrian Collisions at Intersections12-09-01-00065/14/2025
Conflicts between vehicles and pedestrians at unsignalized intersections occur frequently and often result in serious consequences. In order to alleviate traffic flow congestion at unsignalized intersections caused by accidents, reduce vehicle congestion time and waiting time, and improve intersection safety as well as intersection access efficiency, a speed guidance algorithm based on pedestrian-to-vehicle (P2V) and vehicle-to-pedestrian (V2P) communication technologies is proposed. The method considers the heading angle (direction of motion) of vehicles and pedestrians and combines the post encroachment time (PET) and time to collision (TTC) to determine whether there is a risk of collision, so as to guide the speed of vehicles. Network simulator NS3 and traffic flow simulation software SUMO are used to verify the effectiveness of the speed guidance strategy proposed in this article. The experimental findings demonstrate that the speed guidance strategy introduced in this article exhibits significant efficacy in mitigating conflicts, enhancing safety, and augmenting the throughput of unsignalized intersections. Specifically, when the penetration rate of intelligent connected vehicles attains 100%, there is a complete elimination (100% reduction) in the number of vehicles requiring emergency braking while traversing the intersection, and the queuing time is reduced by 16.1%.
Sun, Yuanyuan, Wang, Kan, Liu, Weizhen, Li, Wenli
Global Vehicle Vibration Enhancement Using Circular Force Technology2025-01-00155/5/2025
In addition to providing safety advantages, sound and vibration are being utilized to enhance the driver experience in Battery Electric Vehicles (BEVs). There's growing interest and investment in using both interior and exterior sounds for pedestrian safety, driver awareness, and unique brand recognition. Several automakers are also using audio to simulate virtual gear shifting of automatic and manual transmissions in BEVs. According to several automotive industry articles and market research, the audio enhancements alone, without the vibration that drivers are accustomed to when operating combustion engine vehicles, are not sufficient to meet the engagement, excitement, and emotion that driving enthusiasts expect. In this paper, we introduce the use of new automotive, high-force, compact, light-weight circular force generators for providing the vibration element that is lacking in BEVs. The technology was developed originally for vibration reduction/control in aerospace applications, has been recently tested in various vehicles, and demonstrates the effectiveness for providing a real haptic feel across the entire vehicle. Shaking the vehicle globally provides a unique capability for BEVs, including Hybrid Electric Vehicles and for helping to create a smooth transition between Gas and Electric power, for example. The technology can be used to generate and emulate high-performance, high power, combustion engine feel, including idle, engine run-up/acceleration, simulated gear shifts, and Advanced Driving Assistance and Systems (ADAS) haptic indicators. The optional and customizable vibration can also mask road vibration which becomes very noticeable in otherwise smooth BEVs and can provide the perfect supplement to existing audio enhancements and gear shifting features. Additionally, the paper describes how the force generating device can be packaged in a light weight, compact, low-power manner. The technology will be compared to other force generating methods, and discuss its pros and cons.
Norris, Mark A., Orzechowski, Jeffrey, Sanderson, Brad, Swanson, Douglas, Vantimmeren, Andrew
Validation of Pedestrian Collision Reconstruction Using the PC-Crash Multibody Pedestrian Model2025-01-86814/1/2025
This study validates the use of the pedestrian multibody model in the simulation software PC-Crash. If reasonable inputs are used, the pedestrian model will yield accurate simulations of pedestrian collisions, particularly in terms of accurately simulating the contact points between the pedestrian and the vehicle and in predicting the throw distance of the pedestrian. This study extends prior studies of the PC-Crash pedestrian multibody model by simulating additional staged collisions, by comparing the results of the model to widely utilized throw distance equations, by providing guidance on inputs for the pedestrian multibody, and by providing documentation of the characteristics of the multibody pedestrian. In addition, two new staged pedestrian collisions are discussed and simulated. This study demonstrates the following: (1) The center of gravity height of the PC-Crash pedestrian model is comparable to the center of gravity height reported for pedestrians in anthropometric data. (2) The pedestrian-to-ground friction coefficient should be set using values representative of simple sliding, not values that include the impact with the ground or the airborne trajectory. (3) Based on the simulations presented in this study, a reasonable range for this pedestrian-to-ground friction coefficient for dry roadways is 0.5 to 0.65. For wet roadways, a reasonable range is 0.3 to 0.4 (4) For forward projection and wrap trajectories, the default coefficient of restitution for the multibody of 0.316 is reasonable for vehicle impact speeds below 40 km/h. (5) For speeds above 40 km/h with these trajectory types, a restitution coefficient in the range of 0.1 to 0.2 yields more accurate throw distances. (6) For fender vaults, the pedestrian-to-vehicle coefficient of friction and the coefficient of restitution are influential in the lateral throw distance, and these parameters can be treated as optimizing parameters for simulations of this trajectory type.
Rose, Nathan, Smith, Connor, Carter, Neal, Metanias, Andrew
Research on the Boundary of Pedestrian Leg Impactor Test in Low-Speed Passive and Active Conditions2025-01-87354/1/2025
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 analyze the changes in collision boundary conditions between leg impactors and vehicles. Finally, based on the simulation model, the changes in injury of leg impactors with and without AEB are further analyzed. The research results show that after the intervention of AEB, the impact positions of aPLI and TRL on the front of the vehicle will change, and the change of sedan is more significant than that of SUV. At the same time, under the action of AEB, the damage of aPLI and TRL will change significantly, and the front edge position of the vehicle is more sensitive. This study provides important theoretical support for the subsequent integrated safety assessment of pedestrian protection, provides design references for future pedestrian protection regulation assessments, and has important guiding significance for the optimization of vehicle front-end structures.
Ye, Bin, Hong, Cheng, Wan, Xinming, Liu, Yu, Cheng, James, Long, Yongchen, Hao, Haizhou
Lattice Based Localized Energy Absorber for Improved Vulnerable Road User Performance for a Vehicle2025-01-87234/1/2025
A passenger vehicle hood is designed to meet Vulnerable Road User (VRU) regulatory requirements and consumer metric targets. Generally, hood inner design and its reinforcements, along with deformable space available under the hood are the main enablers to meet the Head Impact performance targets. However, cross functional balancing requirements, such as hood stiffness and packaging space constraints, can lead to higher Head Injury Criteria (HIC15) scores, particularly when secondary impacts are present. In such cases, a localized energy absorber is utilized to absorb the impact energy to reduce HIC within the target value. The current localized energy absorber solutions include the usage of flexible metal brackets, plastic absorbers etc. which have limited energy absorbing capacity and tuning capability. This paper focuses on usage of a novel 3D printed energy absorbers, based on various kinds of lattice structures. These absorbers are either sandwiched between the inner and the outer hood or are integrated directly with under hood parts such as shock tower or headlamp bolt locations to mitigate high HIC values. The work focuses on virtual evaluation of Nylon-12 3D printed lattice designs, based on Body Centered Cubic (BCC) and Kelvin Lattice, for VRU energy management. These optimized lattice-based designs are shown to provide significant performance improvement over conventional energy absorbers. As 3D printing can support manufacturing of complex shapes, the lattice-based energy absorbers can be tuned easily to achieve optimal performance. Technological advancements in 3D printing make these solutions cost effective and easier to implement.
Kinila, Vivekananda, Agarwal, Varun, V S, Rajamanickam, Tripathy, Biswajit, Gupta, Vishal
Dynamic Driving Task Assessment Scores for Scenarios Navigated by an OEM ADS-Equipped Vehicle2025-01-86724/1/2025
One of the major issues facing the automated driving system (ADS)-equipped vehicle (AV) industry is how to evaluate the performance of an AV as it navigates a given scenario. The development and validation of a sound, consistent, and transparent dynamic driving task (DDT) assessment (DA) methodology is a key component of the safety case framework (SCF) of the Automated Vehicle – Test and Evaluation Process (AV-TEP) Mission, a collaboration between Science Foundation Arizona and Arizona State University. The DA methodology was presented in earlier work and includes the DA metrics from the recently published SAE J3237 Recommended Practice. This work extends and implements the methodology with an AV developed by OEM May Mobility in four diverse, real-world scenarios: (1) an oncoming vehicle entering the AV’s lane, (2) vulnerable road user (VRU) crossing in front of the AV’s path, (3) a vehicle executing a three-point turn encroaches into the AV’s path, and (4) the AV exhibiting aggressive acceleration through an intersection. The assessment of each scenario navigation by the May Mobility AV is provided by two versions of the DA Score: (1) a simple, single grade that incorporates the applicable DA metrics violations and severity of the violations, (2) the DA Score modified by weighting factors of the scenario complexity and relevance to the AV’s ODD, along with the test method fidelity. The objective of the work is to demonstrate the DA methodology in an actual OEM AV application in a variety of scenarios. The aggregation of DA Scores (unweighted and weighted) is a key input to the AV-TEP SCF that provides safety assurance of the AV under development.
Wishart, Jeffrey, Rahimi, Shujauddin, Swaminathan, Sunder, Zhao, Junfeng, Frantz, Matt, Singh, Satvir, Como, Steven Gerard
Developing a Vehicle-in-Virtual-Environment (VVE) Based Autonomous Driving Function Development and Evaluation Pipeline for Vulnerable Road User Safety2025-01-80614/1/2025
Traditional methods for developing and evaluating autonomous driving functions, such as model-in-the-loop (MIL) and hardware-in-the-loop (HIL) simulations, heavily depend on the accuracy of simulated vehicle models and human factors, especially for vulnerable road user safety systems. Continuation of development during public road deployment forces other road users including vulnerable ones to involuntarily participate in the development process, leading to safety risks, inefficiencies, and a decline in public trust. To address these deficiencies, the Vehicle-in-Virtual-Environment (VVE) method was proposed as a safer, more efficient, and cost-effective solution for developing and testing connected and autonomous driving technologies by operating the real vehicle and multiple other actors like vulnerable road users in different test areas while being immersed within the same highly realistic virtual environment. This VVE approach synchronizes real-world vehicle and vulnerable road user motion within the same virtual scenario, enabling the safe and realistic testing of various traffic situations in a safe and repeatable manner. In this paper, we propose a new testing pipeline that sequentially integrates MIL, HIL, and VVE methods to comprehensively develop and evaluate autonomous driving functions. The effectiveness of this testing pipeline will be demonstrated using an autonomous driving path-tracking algorithm with local deep reinforcement learning modification for vulnerable road user collision avoidance.
Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin
Deriving Architectural Requirements for SAE J3016 TM Level 3 System from Functional Safety Perspective2025-01-80094/1/2025
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 for deriving safety goals for SAE Level 3 systems, to ensure that the operation of Level 3 systems with failure conditions are free from unreasonable risk. This paper discusses the process by which these safety goals are manifested into architectural requirements for safely deploying SAE Level 3 systems. It highlights how fail -operability is a necessary characteristic to sustain DDT to tolerate safety-critical failures (single point, plausible dual point, or common cause failures) and to initiate MRM to bring the vehicle to a safe state or until the driver takes over.
Mudunuri, Venkateswara Raju, Jayakumar, Namitha
Analysis of OEM Collision Avoidance Data Outputs during VRU Testing2025-01-80534/1/2025
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
Empirical Analysis on Machine Vision Recognition of Green Bike Lanes for Vulnerable Road Users Safety2025-01-80174/1/2025
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 metrics, such as Blind/Reference-less Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, and Entropy-based Image Quality Assessment. The results indicate that green bike lanes are more likely to be recognized by machine vision systems across a wide range of environmental conditions, demonstrating enhanced recognition capabilities. Green lane markings exhibit enhanced visibility and stability, with BRISQUE scores below 82, a median contrast ratio of 17.6, and improved resilience to motion blur and NIQE variations under diverse conditions.
Ponnuru, Venkata Naga Rithika, Das, Sushanta, Grant, Joseph, Naber, Jeffrey, Bahramgiri, Mojtaba
Efficient Sidewalk Route Planning on Aerial Images with Cost-Based A* and Mini-Max Objective Function2025-01-80324/1/2025
With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for sidewalks on aerial/HD maps.
Bao, Zhibin, Lang, Haoxiang, Lin, Xianke
Compensation Algorithm Development in Platform for Pedestrian ADAS Testing2025-01-80664/1/2025
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
Study on the Improvement of Pedestrian and Vehicle Visibility by Geometric Patterns Projection Lighting2025-01-86634/1/2025
The proportion of pedestrian fatalities due to traffic accidents is higher at night than during the day. Drivers can more easily recognize pedestrians by setting their headlights to high beam, but use of high beam poses the issue of increasing glare for pedestrians. This study proposes a lighting technology that increases the noticeability of pedestrians for drivers and the noticeability of approaching vehicles for pedestrians while at the same time helping to reduce glare for pedestrians. The newly designed lighting enables geometric patterns projection lighting that makes use of projection technology. This geometric pattern projection lighting was compared with conventional low beam and high beam headlights to verify the effectiveness. Tests were conducted on a closed course with the participation of 20 drivers to evaluate the functionality of each headlight type. In these tests, subjects performed specific tasks such as evaluation of pedestrian visibility from the driver’s point of view, and noticeability of approaching vehicles and glare from the pedestrian’s point of view. Human subject tests used an experimental design in which study subjects experienced all three types of headlights in multiple trials. The results showed that while high beam provided the longest visibility distance, the glare was also the greatest. Geometric patterns projection lighting was shown to be better than low beam in both pedestrian visibility distance and distance at which an approaching vehicle is noticed. Overall, geometric patterns projection lighting was able to achieve a good balance between visibility distance and lower glare, and was verified to be a promising means of increasing visibility for drivers at night.
Kawamura, Kazuyuki, Oshida, Kei
Biofidelity Evaluation of AC-HUMs Pedestrian Model Based on Generic Sedan Buck Model2025-01-87434/1/2025
Objective: This study aims to evaluate the biofidelity of the Advanced Chinese Human Body Model (AC-HUMs) by utilizing a generic sedan buck model and post-mortem human surrogates (PMHS) test data. Methods: The boundary conditions of the simulation were derived from the PMHS test with the buck vehicle. The methodology involved the pose adjustment of the upper and lower extremities of AC-HUMs, executed through a pre-simulation approach. Subsequently, a 200 milliseconds whole body pedestrian crash simulation was conducted using the buck vehicle and the AC-HUMs pedestrian model. The trajectories of AC-HUMs during the period from initial position to head impact were recorded, including the Head CG, T1, T8 and pelvis. Based on the knee joint, the corridors of trajectories from the PMHS test were scaled to match the Chinese 50th percentile male to evaluate the biofidelity of AC-HUMs's kinematic response. Furthermore, the biomechanical responses were compared with the PMHS tests, including injuries of chest and lower extremities. This comparison comprehensively evaluated the injury prediction capability of the AC-HUMs pedestrian model under whole-body pedestrian collision scenarios. Conclusion: The results indicate that the trajectories of the four markers on the AC-HUMs pedestrian model were all within the scaled trajectory corridors, confirming that the model exhibits good biofidelity. The results reconstructed similar ligament rupture scenarios (left LCL, right ACL, and MCL) as well as partial rib injuries. The findings also revealed potential biofidelity issues in the neck, ribs, knee joint, and tibia regions of the AC-HUMs model. Despite these challenges, the AC-HUMs pedestrian model demonstrates good biofidelity in motion trajectories and possesses the ability to replicate biomechanical responses. This indicates that the AC-HUMs model has significant potential for virtual vehicle safety assessments in China, positioning it as a promising tool for this purpose.
Qian, Jiaqi, Wang, Qiang, Liu, Yu, Wu, Xiaofan, Huida, Zhang, Bai, Zhonghao
Study on the Impact of Vehicle Pitch During Braking on Pedestrian Leg Injuries2025-01-87414/1/2025
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 Utility Vehicle (SUV), and comparing injury indicators for the thigh, lower leg, and knee joint. The results indicate that the vehicle's pitch angle reduces the elongation of the medial collateral ligament (MCL) in the pedestrian's knee and increases the maximum bending moment of the thigh. For sedan with pitch angles, the maximum bending moment of the pedestrian's lower leg decreases at a vehicle speed of 40 km/h and increases at speeds of 30 km/h and 20 km/h. The impact of SUV on the maximum bending moment of the lower leg is opposite to that of sedan. This study holds guiding significance for optimizing vehicle design, enhancing the effectiveness of AEB systems, and establishing stricter pedestrian protection standards.
Hong, Cheng, Ye, Bin, Zhan, Zhenfei, Liu, Yu, Wan, Xinming, Hao, Haizhou
Longitudinal Analysis of Forward Blind Zone Changes in Popular Vehicle Models (1997–2023)09-13-01-00053/26/2025
Background. In 2022, vulnerable road user (VRU) deaths in the United States increased to their highest level in more than 40 years. At the same time, increasing vehicle size and taller front ends may contribute to larger forward blind zones, but little is known about the role that visual occlusion may play in this trend. Goal. Researchers measured the blind zones of six top-selling light-duty vehicle models (one pickup truck, three SUVs, and two passenger cars) across multiple redesign cycles (1997–2023) to determine whether the blind zones were getting larger. Method. To quantify the blind zones, the markerless method developed by the Insurance Institute for Highway Safety was used to calculate the occluded and visible areas at ground level in the forward 180° arc around the driver at ranges of 10 m and 20 m. Results. In the 10-m forward radius nearest the vehicle, outward visibility declined in all six vehicle models measured across time. The SUV models showed up to a 58% reduction in visibility within a 10 m radius. Other vehicles exhibited smaller (7%–19%) reductions. At longer distances (10 m–20 m), vehicles demonstrated both increases and decreases in visibility. Conclusion. The markerless method provides a straightforward and replicable assessment of driver visibility. The observed decrease in direct outward visibility near the vehicles points to the need for further study regarding this trend, including analysis of the repeatability and viability of the measurement technique.
Epstein, Alexander K., Brodeur, Alyssa, Drake, Juwon, Englin, Eric, Fisher, Donald L., Zoepf, Stephen, Mueller, Becky C., Bragg, Haden
Research on Automatic Emergency Braking Pedestrian System Considering Road Adhesion Coefficient10-09-03-00223/8/2025
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
Tunnel Traffic Anomaly Detection via Multi-Modal LLMs2025-01-71292/21/2025
Tunnels play a crucial role in urban transportation, yet they frequently encounter various incidents during operation. Manual video inspections and sensor-based systems are inefficient and limited in accurately detecting and addressing these issues. The emergence of artificial intelligence has led to the development of object detection models such as YOLO, which have shown promise in real-time anomaly detection. However, these single-modality models achieve suboptimal results when dealing with complex events. Multi-modal large language models (LLMs) offer a potential solution, with their ability to process and understand information from different modalities. This paper develops a novel tunnel traffic anomaly detection method that combines single-modal models and multi-modal LLMs. The proposed system first employs YOLO for an initial detection round and then utilizes a specially designed LLM with an effective prompt and a data filtering strategy tailored for traffic tunnel scenarios. This two-step approach enables the system to detect anomalies such as fires and ponding water, facilitating real-time monitoring of tunnel conditions and maintaining traffic flow. We are the first to introduce a well-designed multi-modal LLM into tunnel traffic anomaly detection, for real-time and accurate detection. We create a tunnel-specific algorithm that covers model design, prompt strategy, and detection logic, effectively handling complex weather and traffic scenarios. The system has demonstrated an accuracy rate of up to 90% in detecting numerous surveillance cameras simultaneously, reducing labor costs and potential economic losses associated with tunnel incidents. Our research thus aims to enhance tunnel safety and efficiency through an innovative and effective anomaly detection system.
Liu, Hongyu, Zhou, Ruohan, Bai, Jiayang, Li, Yuanqi
Verification of Vulnerable Road User Pre-Crash Scenarios in Vision-Based Safety Applications12-08-01-00041/10/2025
Verifying training datasets in vision-based vehicle safety applications is crucial to understanding the potential limitations of detection capabilities that may result in a higher safety risk. Vision-based pedestrian safety applications with crash avoidance technologies rely on prompt detection to avoid a crash. This research aims to develop a verification process for vulnerable road user safety applications with vision-based detection functionalities. It consists of reviewing the application’s safety requirements, identifying the target objects of detection in the operational design domain and pre-crash scenarios, and evaluating the safety risks qualitatively by examining the training dataset based on the results of pre-crash scenarios classification. As a demonstration, the process is implemented using open-source pedestrian tracking software, and the pre-crash scenarios are classified based on the trajectories of pedestrians in an example training dataset used in a pedestrian automatic emergency braking system. The dataset provides abundant vulnerable road users under various operating conditions. Results show that the dataset does not cover snowy weather, fog, smoke, smog, and dust/dirt of particulate matter conditions. Crossing, cut-in, and front pre-crash scenarios are less well-represented in the dataset than pedestrians on the sidewalk. Very few pedestrians were found in the front-near zone, which suggests the dataset lacks sufficient imminent pre-crash scenarios that pose the greatest safety risk. The performance of the pedestrian tracking software is evaluated and future research for improving the proposed mechanism is discussed.
Hsu, Chung-Jen
Lightweight Neural Network Model and Algorithm for Pedestrian Detection12-08-03-002712/18/2024
Traditional pedestrian detection methods have poor robustness. Deep learning-based methods have shown high performance in recent years but rely on substantial computational resources. Developing a lightweight, deep learning-based pedestrian detection algorithm is essential for applying deep learning-based algorithms in resource-limited scenarios, such as driverless and advanced driver assistance systems. In this article, an improved model based on YOLOv3 called “YOLOPD” (You Only Look Once—Pedestrian Detection), is proposed. It is obtained by constructing a self-attentive module, introducing a CIOU (Complete Intersection over Union) loss function and a depth separated convolutional layer. Experimental results show that on the INRIA (National Institute for Research in Computer Science and Automation), Caltech, and CityPerson pedestrian dataset, the MR (miss rate) of the model YOLOPD is better than that of the original YOLOv3 model, and the number of parameters is reduced by about 1/3, which significantly improves the speed of network derivation while improving detection accuracy.
Li, Shanglin, Wang, Qi Feng, Li, Ren Fa, Xiao, Juan
Skeletal Surrogate Metastructural Anisotropy Mimicry and the Influence of Fabrication Material09-12-02-000911/12/2024
Human body models have been used for decades to inform efforts in promoting automobile occupant and pedestrian safety. However, many of these models fail to capture the intricacies of individual variability. Cadaveric subjects typically exceed representative age ranges and hence mechanics. Animal subjects typically require specific setups that stray from that which is representative of human crash scenarios. Computational models can only consider so many practical real-world variables. Artificial surrogates, dummies being popular among them, are very popular for reusability and robust data collection. However, even the biomechanically accurate skeletal surrogates available commercially are limited in that they do not consider human variability and skeletal microstructure local variability. The objective of the work herein is to assess computational methods of metastructural variability mimicry by fabrication material. We implement mimicry approaches focusing on bulk isotropic elasticity and in-house structural optimization approaches focusing on pure anisotropy skeletal microstructure mimicry. This allows us to assess rapid and detailed approaches alike and determine which fabrication materials are ideal under which approach. We found that Fortify DT was ideal for mimicking the phenomena present in the GHBMC M50 L5 model when using a walled Gyroid. For microstructural mimicry, we found there to be a range in acceptable bulk material elastic moduli between 2.98 and 36.6 GPa. Ultimately, these findings have the potential to guide practitioners of skeletal microstructure biomimicry.
Hezrony, Benjamin S., C. F. Lopes, Pedro, Brown, Philip J.
Enhancing Availability of Autonomous Shuttle Services: A Conceptual Approach toward Challenges and Opportunities12-08-03-002310/9/2024
Autonomous vehicle technologies have become increasingly popular over the last few years. One of their most important application is autonomous shuttle buses that could radically change public transport systems. In order to enhance the availability of shuttle service, this article outlines a series of interconnected challenges and innovative solutions to optimize the operation of autonomous shuttles based on the experience within the Shuttle Modellregion Oberfranken (SMO) project. The shuttle shall be able to work in every weather condition, including the robustness of the perception algorithm. Besides, the shuttle shall react to environmental changes, interact with other traffic participants, and ensure comfortable travel for passengers and awareness of VRUs. These challenging situations shall be solved alone or with a teleoperator’s help. Our analysis considers the basic sense–plan–act architecture for autonomous driving. Critical components like object detection, pedestrian tracking, trajectory planning, and teleoperation are analyzed, and concepts for overcoming the challenges mainly in these components are presented.
Dehghani, Ali, Salaar, Hamza, Srinivasan, Shanmuga Priya, Zhou, Lixian, Arbeiter, Georg, Lindner, Alisa, Patino-Studencki, Lucila
Micromobility and the Next Infrastructure WaveEPR202402210/4/2024
US transportation infrastructure is dominated by the automobile form factor. Alternative modalities of movement, such as bikes, golf carts, and other micromobility options, have existed but are decidedly at a lower tier of importance. Even pedestrian access ways are not overly emphasized in the US transportation system. This lack of prioritization matches the reality that the vast majority of people and commerce moves through the motor vehicle infrastructure, with micromobility sitting in the periphery. Additionally, given the current lack of commercial applications, there are limited direct fee-based funding mechanisms connected to micromobility form factors. Micromobility and the Next Infrastructure Wave discusses how recent technological innovations in electrification, e-commerce, and autonomy are enabling a new class of micromobility devices which offer palpable value to consumers and enable significant commercial applications. Unlike the past, these micromobility devices now have the scale, commercial funding, and operational economic value to justify a focused infrastructure effort. Click here to access the full SAE EDGETM Research Report portfolio.
Razdan, Rahul
Comprehensive Study on Challenges to Introduce Autonomous Emergency Braking (AEBS) Feature in Indian Perspective2024-01-30509/8/2024
India is a diverse country in terms of road conditions, road maintenance, traffic conditions, traffic density, quality of traffic which implies presence of agricultural tractors, bullock carts, autos, motor bikes, oncoming traffic in same lane, vulnerable road users (VRU) walking in the same lanes as vehicles, VRU’s crossing roads without using zebra crossings etc. as additional traffic quality deterrents in comparison to developed countries. The braking capacity of such vivid road users may not be at par with global standards due to their maintenance, loading beyond specifications, driver behavior which includes the tendency to maintain a close gap between the preceding vehicle etc. which may lead to incidents specifically of rear collisions due to the front vehicle going through an emergency braking event. The following paper provides a comprehensive study of the special considerations or intricacies in implementation of Autonomous Emergency Braking (AEBS) feature into Indian traffic perspective. The paper provides a comparison of the AEBS standard between Indian and EU markets. The paper provides the differences in traffic quality of Indian roads in comparison with EU to justify the limitations to be dealt with and eventually this paper also provides a detailed study on the special considerations to be inculcated in developing such feature specific to Indian customer and to enhance the safety of occupant as well as other road users.
Kartheek, Nedunuri, Khare, Rashmita, Sathyamurthy, Sainathan, Manickam, Praveenkumar, Kuchipudi, Venkata Sai Pavan
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