自动驾驶汽车与路过者互动的决策:一种意识到风险的深度强化学习方法
Ziqian Zhang1, Haojie Li1, Tiantian Chen2
1School of Transportation, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.
Accident; analysis and prevention
|November 20, 2024
概括
一种新的风险意识深度强化学习 (DRL) 方法通过预测路过风险来提高自动驾驶汽车 (AV) 的安全性. 这种方法可以改善复杂的行人与车辆交互中的决策,提高安全性和效率.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 运输工程 运输工程
背景情况:
- 由于反应时间有限,捷径行走对驾驶员和行人构成重大风险.
- 自动驾驶汽车 (AV) 技术为减轻这些风险提供了潜在的解决方案.
- 当前的自动驾驶决策框架可能无法充分解决 jaywalker - 车辆交互的复杂性.
研究的目的:
- 开发和评估一种风险意识的深度强化学习 (DRL) 方法.
- 在涉及路过者的场景中增强AV决策.
- 提高自主导航的安全性和效率.
主要方法:
- 使用编码器-解码器模型的风险预测模块被集成到DRL框架中.
- 该方法在模拟环境 (Anylogic) 中与真实世界的运动数据进行了测试.
- 在不同的情景中评估了AV政策,其中的 jaywalker 量有所不同.
主要成果:
- 建议的风险意识DRL方法在安全指标上显著超过了基线政策,稳定了"低TTC比率"在0.08附近.
- 该方法的优越性随着场景的复杂性 (更高的 jaywalker 卷) 的增加而增加.
- 效率表现排名第二,复杂场景中AV延迟最小.
结论:
- 风险意识的DRL方法使AV能够有效地预测和减轻街行风险.
- 这项技术通过改善高风险地区的AV导航来提高行人安全.
- 拟议的方法为更安全,更高效的自动驾驶提供了实际解决方案.
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