School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Xingjian College, Tsinghua University, Beijing 100084, China.
Int J Environ Res Public Health. 2023 Jan 27;20(3):2278. doi: 10.3390/ijerph20032278.
Driver disability has become an increasing factor leading to traffic accidents, especially for commercial vehicle drivers who endure high mental and physical pressure because of long periods of work. Once driver disability occurs, e.g., heart disease or heat stroke, the loss of driving control may lead to serious traffic incidents and public damage. This paper proposes a novel driving intervention system for autonomous danger avoidance under driver disability conditions, including a quantitative risk assessment module named the Emergency Safety Field (ESF) and a motion-planning module. The ESF considers three factors affecting hedging behavior: road boundaries, obstacles, and target position. In the field-based framework, each factor is modeled as an individual risk source generating repulsive or attractive force fields. Individual risk distributions are regionally weighted and merged into one unified emergency safety field denoting the level of danger to the ego vehicle. With risk evaluation, a path-velocity-coupled motion planning module was designed to generate a safe and smooth trajectory to pull the vehicle over. The results of our experiments show that the proposed algorithms have obvious advantages in success rate, efficiency, stability, and safety compared with the traditional method. Validation on multiple simulation and real-world platforms proves the feasibility and adaptivity of the module in traffic scenarios.
驾驶员残疾已成为导致交通事故的一个日益重要的因素，特别是对于商业车辆驾驶员来说，由于长时间工作，他们承受着高度的精神和身体压力。一旦驾驶员残疾发生，例如心脏病或中暑，失去对驾驶的控制可能会导致严重的交通事故和公共伤害。本文提出了一种用于驾驶员残疾情况下自主避险的新型驾驶干预系统，包括一个名为紧急安全场（ESF）的定量风险评估模块和一个运动规划模块。ESF 考虑了影响避险行为的三个因素：道路边界、障碍物和目标位置。在基于场的框架中，每个因素都被建模为一个单独的风险源，产生排斥或吸引力场。个体风险分布在区域上进行加权，并合并为一个统一的紧急安全场，以表示对自身车辆的危险程度。通过风险评估，设计了一个路径-速度耦合的运动规划模块，以生成安全平稳的轨迹，将车辆拉到路边。实验结果表明，与传统方法相比，所提出的算法在成功率、效率、稳定性和安全性方面具有明显的优势。在多个模拟和真实平台上的验证证明了该模块在交通场景中的可行性和适应性。
