Safety envelope of pedestrians upon motor vehicle conflicts identified via active avoidance behaviour

Nie, Bingbing; Li, Quan; Gan, Shun; Xing, Bobin; Huang, Yuan; Li, Shengbo Eben

doi:10.1038/s41598-021-82331-z

Cited by 28 publications

(9 citation statements)

References 27 publications

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“…In the case of the vehicle’s avoidance of pedestrians, the hazard distance between the vehicle and the pedestrian was set to 1.5 m [ 28 ]. In this paper, the closest distance between the vehicle and the pedestrian was used as the performance index to evaluate the proposed algorithm.…”

Section: Test Verification and Analysis Of Resultsmentioning

confidence: 99%

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

Pan

Jie

Zhao

et al. 2023

Sensors

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V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the “stop–go” state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle’s active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency.

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Section: Test Verification and Analysis Of Resultsmentioning

confidence: 99%

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

Pan

Jie

Zhao

et al. 2023

Sensors

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show abstract

“…For representing typical conflict scenes between pedestrians and vehicles in real-world ( Han et al, 2017 ) and stimulating pedestrians to respond naturally, one of two emergent near-real car–pedestrian conflict scenarios (traffic scene A (TSA) and traffic scene B (TSB)) was created by the experimenter immediately after the subject entered the road. Detailed information on the experimental platform can be found in our previous studies ( Li et al, 2020 ; Nie et al, 2021 ). To record the natural responses of the pedestrians and eliminate the influence of vigilance on crossing behavior caused by repeated experiments, each subject participated only once per scene.…”

Section: Methodsmentioning

confidence: 99%

Kinetic and Kinematic Features of Pedestrian Avoidance Behavior in Motor Vehicle Conflicts

Shang

Pei

et al. 2021

Front. Bioeng. Biotechnol.

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The active behaviors of pedestrians, such as avoidance motions, affect the resultant injury risk in vehicle–pedestrian collisions. However, the biomechanical features of these behaviors remain unquantified, leading to a gap in the development of biofidelic research tools and tailored protection for pedestrians in real-world traffic scenarios. In this study, we prompted subjects (“pedestrians”) to exhibit natural avoidance behaviors in well-controlled near-real traffic conflict scenarios using a previously developed virtual reality (VR)-based experimental platform. We quantified the pedestrian–vehicle interaction processes in the pre-crash phase and extracted the pedestrian postures immediately before collision with the vehicle; these were termed the “pre-crash postures.” We recorded the kinetic and kinematic features of the pedestrian avoidance responses—including the relative locations of the vehicle and pedestrian, pedestrian movement velocity and acceleration, pedestrian posture parameters (joint positions and angles), and pedestrian muscle activation levels—using a motion capture system and physiological signal system. The velocities in the avoidance behaviors were significantly different from those in a normal gait (p < 0.01). Based on the extracted natural reaction features of the pedestrians, this study provides data to support the analysis of pedestrian injury risk, development of biofidelic human body models (HBM), and design of advanced on-vehicle active safety systems.

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“…Through the study of pedestrian-vehicle interaction, some authors, such as [16], have been able to estimate critical zones by calculating the possibility of collision as a function of the relative lateral and longitudinal distance between car and user. The study of the pedestrian's whole-body kinematics when reacting in such potential hit-and-run situations allows to characterize users behavior, generating avoidance patterns in terms of speed and acceleration for the cases of backward and forward motion.…”

Section: State Of the Artmentioning

confidence: 99%

Effectiveness of the Autonomous Braking and Evasive Steering System OPREVU-AES in Real World Vehicle-to-pedestrian collisions

Losada¹,

Páez²,

Luque³

et al. 2022

Preprint

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Among the possible improvements of Autonomous Emergency Braking (AEB) systems, reducing the intensity of the automatic braking process by studying the kinematics and general behavior of the pedestrian while crossing is crucial to determine the progressiveness of the braking, or replacing part of the braking process by an evasive maneuver when a collision is imminent. This paper proposes the integration of an autonomous avoidance system (Automatic Emergency Steering, AES) that acts directly on the steering system to generate an evasive maneuver and avoid a possible pedestrian collision (OPREVU-AES system), as well as the assessment of its effectiveness compared to a commercial AEB system. OPREVU and VULNEUREA are research projects in which INSIA and CEDINT have cooperated to improve driving assistance systems and the safety of pedestrians and cyclists through Virtual Reality (VR) techniques. The analysis of the kinematic and dynamic response of the OPREVU-AES system is conducted in CarSim© software. The effectiveness evaluation procedure is based on the reconstruction of a sample of road vehicle-to-pedestrian crashes (INSIA-UPM database), using the PCCrash® software, and taking as an indicator the probability of head injury severity (ISP). The results show that the AEB system would have prevented part of the collisions, especially after the incorporation of the OPREVU-AES system. In most of the cases where avoidance is not possible, a significant reduction of the ISP is achieved.

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Safety envelope of pedestrians upon motor vehicle conflicts identified via active avoidance behaviour

Cited by 28 publications

References 27 publications

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

Kinetic and Kinematic Features of Pedestrian Avoidance Behavior in Motor Vehicle Conflicts

Effectiveness of the Autonomous Braking and Evasive Steering System OPREVU-AES in Real World Vehicle-to-pedestrian collisions

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