دورية أكاديمية
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Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users.

التفاصيل البيبلوغرافية
العنوان: Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users.
المؤلفون: Guo Y; State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China., Liu Y; State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China., Wang B; State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China., Huang P; State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China., Xu H; China Merchants Testing Vehicle Technology Research Institute Co., Ltd, Chongqing, 400041, China., Bai Z; State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China. Electronic address: baizhonghao@hnu.edu.cn.
المصدر: Accident; analysis and prevention [Accid Anal Prev] 2024 Aug; Vol. 203, pp. 107610. Date of Electronic Publication: 2024 May 14.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Pergamon Press Country of Publication: England NLM ID: 1254476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2057 (Electronic) Linking ISSN: 00014575 NLM ISO Abbreviation: Accid Anal Prev Subsets: MEDLINE
أسماء مطبوعة: Publication: Oxford : Pergamon Press
Original Publication: [New York, Pergamon Press]
مواضيع طبية MeSH: Accidents, Traffic*/statistics & numerical data , Accidents, Traffic*/prevention & control , Craniocerebral Trauma*/prevention & control , Craniocerebral Trauma*/etiology, Humans ; Biomechanical Phenomena ; Computer Simulation ; Automobile Driving/statistics & numerical data ; Automation ; Motorcycles ; Models, Theoretical
مستخلص: Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC 15 ). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R 2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier Ltd.)
فهرسة مساهمة: Keywords: Electric two-wheelers (E2Ws); Head injury; Trajectory planning; Vulnerable road users
تواريخ الأحداث: Date Created: 20240515 Date Completed: 20240601 Latest Revision: 20240601
رمز التحديث: 20240602
DOI: 10.1016/j.aap.2024.107610
PMID: 38749269
قاعدة البيانات: MEDLINE
الوصف
تدمد:1879-2057
DOI:10.1016/j.aap.2024.107610