School of Transportation Engineering, Chang'an University, Xi'an 710064, PR China.
School of Transportation Engineering, Chang'an University, Xi'an 710064, PR China; School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK.
Accid Anal Prev. 2024 Oct;206:107727. doi: 10.1016/j.aap.2024.107727. Epub 2024 Jul 29.
Safety decisions for vehicles at an intersection rely on real-time, objective and continuous assessment of risks in vehicle-pedestrian interactions. Existing surrogate safety models, constrained by ideal assumptions of constant current speed and reliant on interaction points, often misjudge risks, and show inefficiency, inaccuracy and discontinuity. This work proposes a novel model for evaluation of those risks in vehicle-pedestrian interactions at intersections, which abstracts the pedestrian distribution density around a vehicle into a generalized model of driver-pedestrian interaction preferences. The introduction of two conceptions: 'driving risk index' and 'driving risk gradient,' facilitates the delineation of driving spaces for identifying safety-critical events. By means of the trajectory data from three intersections, model parameters are calibrated and a multidimensional vehicle-pedestrian interaction risk (VPIR) model is proposed to adapt the complex and dynamic characteristics of vehicle-pedestrian interactions at intersections. Commonly used surrogate safety models, such as Time to Collision (TTC), are selected as benchmark models. Results show that the proposed model overcomes the limitations of the existing interaction-point-based models, and offers a ideal assessment of driving risks at intersections. Finally, the model is illustrated with a case study that assesses the risks in vehicle-pedestrian interactions in varied scenarios and the case study indicates that the VPIR model works well in evaluating vehicle-pedestrian interaction risks. This work can facilitate humanoid learning in the autonomous driving domain, and achieve an ideal evaluation of vehicle-pedestrian interaction risks for safe and efficient vehicle navigation through an intersection.
车辆在交叉路口的安全决策依赖于对车辆-行人交互中风险的实时、客观和连续评估。现有的替代安全模型受到恒定电流速度的理想假设的限制，并依赖于交互点，往往会错误判断风险，并且表现出低效、不准确和不连续。本工作提出了一种新的模型，用于评估交叉路口车辆-行人交互中的风险，该模型将车辆周围行人分布密度抽象为驾驶员-行人交互偏好的广义模型。引入了两个概念：“驾驶风险指数”和“驾驶风险梯度”，便于划分驾驶空间，以识别安全关键事件。通过三个交叉口的轨迹数据，对模型参数进行了校准，并提出了多维车辆-行人交互风险（VPIR）模型，以适应交叉口车辆-行人交互的复杂和动态特征。常用的替代安全模型，如碰撞时间（TTC），被选为基准模型。结果表明，所提出的模型克服了现有基于交互点模型的局限性，为交叉口的驾驶风险提供了理想的评估。最后，通过案例研究说明了该模型，该案例研究评估了不同场景下的车辆-行人交互风险，并表明 VPIR 模型在评估车辆-行人交互风险方面表现良好。这项工作可以促进自动驾驶领域的拟人学习，并通过安全高效地通过交叉路口实现对车辆-行人交互风险的理想评估。
