State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China.
State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China.
Accid Anal Prev. 2024 Aug;203:107610. doi: 10.1016/j.aap.2024.107610. Epub 2024 May 14.
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). 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 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.
由于涉及电动两轮车 (E2W) 的事故发生率和高伤亡率不断上升，这已成为道路上的一个主要安全隐患。此外，随着当前自动驾驶技术的广泛采用，对弱势道路参与者的安全提出了更大的挑战。大多数现有的轨迹规划方法主要侧重于自动驾驶车辆本身的安全性、舒适性和动力学性能，往往忽略了对弱势道路使用者（VRU）的保护，通常是 E2W 骑手。本文旨在研究 E2W 在车辆碰撞中的运动学响应，包括 15ms 头部损伤准则 (HIC)。分析关键碰撞参数对头部损伤的影响，为预期场景建立损伤预测模型，并提出基于预测 VRU 头部损伤的自动驾驶车辆轨迹规划框架。首先，基于真实事故数据库，建立了两轮车-车辆碰撞的多刚体模型，纳入了四个关键碰撞参数（初始碰撞速度、初始碰撞位置和碰撞角度）。通过与真实致命事故的验证，验证了多刚体模型的准确性，对碰撞场景进行了参数化。其次，通过多参数化碰撞模拟自动化框架与蒙特卡罗抽样算法的结合，建立了一个大型有效的碰撞数据集。基于该数据集，使用 MLP+XGBoost 回归算法对损伤预测模型进行训练和测试，以探索 E2W 骑手头部损伤与碰撞变量之间的潜在关系。最后，基于提出的损伤预测模型，为自动驾驶车辆生成了基于 VRU 头部碰撞损伤预测的轨迹规划框架，旨在实现道路使用者之间碰撞风险的公平分配。事故重建结果表明，与真实事故现场相比，E2W、汽车和 E2W 骑手最终相对位置的最大误差为 11%，证明了重建模型的可靠性。损伤预测结果表明，本文使用的 MLP+XGBoost 回归预测模型在测试集上的 R 达到 0.92。此外，在手动设计的自动驾驶交通流场景中验证了所提出的轨迹规划算法的有效性和可行性。
