在十字路口的车辆与行人相互作用的实时风险评估的新型模型
Tao Wang1, Ying-En Ge1, Yongjie Wang2
1School of Transportation Engineering, Chang'an University, Xi'an 710064, PR China.
Accident; analysis and prevention
|July 30, 2024
概括
这项研究引入了一种新的模型,用于评估十字路口的车辆与行人交互风险,改进了超越传统方法的安全评估. 拟议的模型为自动驾驶系统提供了更准确和持续的风险评估.
科学领域:
- 交通安全 交通安全
- 自主驾驶系统 自主驾驶系统
- 人与车辆的互动
背景情况:
- 目前交叉路口的车辆安全模型因理想化的假设而难以实时评估车辆与行人互动的风险.
- 现有的模型通常在评估动态交叉环境时表现出效率低下,不准确和不连续性.
- 局限性包括依赖常速假设和特定交互点,导致错误的风险判断.
研究的目的:
- 提出一种新的模型来评估交叉路口的车辆和行人相互作用风险.
- 利用行人分布密度,开发一个司机与行人互动偏好的通用模型.
- 通过"驾驶风险指数"和"驾驶风险梯度"等新概念,加强安全关键事件的识别.
主要方法:
- 开发一个多维的车辆与行人相互作用风险 (VPIR) 模型.
- 将行人分布密度抽象化为通用的驾驶员与行人交互偏好.
- 模型参数的校准使用来自三个现实世界的交叉点的轨迹数据.
主要成果:
- 拟议的VPIR模型有效地克服了基于交互点的现有模型的局限性.
- 与时间到碰撞 (TTC) 等基准相比,在复杂和动态的十字路口场景中对驾驶风险的优越评估.
- 案例研究证实了该模型在各种场景中评估车辆与行人相互作用风险的有效性.
结论:
- VPIR模型提供了在十字路口的车辆与行人相互作用风险的理想评估.
- 促进人类学习自动驾驶的进步,使导航更安全,更有效.
- 为自动驾驶汽车系统实时,客观和持续的风险评估提供了一个强大的框架.
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