自动驾驶汽车在各种有风险的行人交叉行为下做出决策
1Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 211189, China; School of Transportation, Southeast University, Nanjing 211189, China.
Accident; analysis and prevention
|February 17, 2026
概括
自动驾驶汽车 (AV) 现在可以使用一种新的风险意识深度强化学习 (DRL) 框架,与有风险的行人一起在复杂的城市交通中导航. 这种方法在具有挑战性的现实驾驶场景中提高了安全性和交通效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 城市规划 城市规划
背景情况:
- 无法控制的中间路段和有风险的行人行为增加了交通冲突,使自动驾驶汽车 (AV) 的决策更加复杂.
- 现有的AV研究经常使用简化的场景,限制了复杂的城市环境中的适用性.
- 对与不可预测的行人互动的自动驾驶汽车缺乏有效的缓解策略.
研究的目的:
- 开发一个高保真模拟平台,用于模拟复杂的AV与行人交互.
- 为使用深度强化学习 (DRL) 的自动驾驶者创建一个风险意识的决策框架.
- 在城市环境中提高AV安全性和效率,以堵塞或分心的行人.
主要方法:
- 开发了一个多代理模拟平台,复制自动驾驶汽车,人驾驶汽车和行人.
- 实施了用于一般可见度建模的极性部门分析,并对遮进行了核算.
- 设计了一个基于DRL的决策框架,整合了自动驾驶汽车的风险评估和安全过.
主要成果:
- 在复杂的场景中,DRL框架显著提高了安全边际和控制流性.
- 与基线相比,学习的AV策略显示了增强的预测行为.
- 在不确定的驾驶条件下,安全和交通效率之间取得了更好的平衡.
结论:
- 风险意识的DRL显示出对管理高度不确定的和交互式城市驾驶环境的承诺.
- 拟议的方法为AVS的安全部署提供了有价值的见解.
- 这项研究解决了复杂的城市环境中现实的AV决策的差距.
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