自动驾驶汽车的安全评估:高速公路合并场景的参考驾驶员模型
Cheng Wang1, Fengwei Guo2, Shuaijie Zhao3
1School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
Accident; analysis and prevention
|July 17, 2024
概括
与人类驾驶员相比,高速公路合并 (CCDM2) 的新驾驶员模型显示出更高的安全性能. 这种可解释的模型在复杂的场景中增强了自动驾驶汽车安全评估.
科学领域:
- 自动驾驶汽车的安全性
- 智能运输系统 智能运输系统
- 机器学习用于机器人技术
背景情况:
- 自动驾驶汽车 (AV) 的安全评估依赖于基准驾驶员模型.
- 当前的模型,如联合国欧洲经济委员会第1号规则中的模型. 157,仅限于汽车追随的场景.
- 高速公路合并带来了独特的挑战,而这些挑战并未得到现有参考模型的解决.
研究的目的:
- 为高速公路合并场景开发一个量化,谨慎和合格的驾驶员模型.
- 确保拟议的模型是可解释的,并表现出类似人类的驾驶特征.
- 推进自动驾驶汽车在复杂的机动作中的安全评估能力.
主要方法:
- 利用可解释的强化学习来进行决策.
- 实现了安全约束控制,以实现强大的性能.
- 在模拟的高速公路合并场景中,将模型的安全性能与人类驾驶员进行了比较.
主要成果:
- 拟议的CCDM2模型表现出"谨慎"和"有能力"的驾驶行为.
- 该模型在具有挑战性的合并情况下,与人类驾驶员相比,表现出更高的安全性能.
- 在整个模型的决策过程中保持了可解释性.
结论:
- 在高速公路合并中,CCDM2模型有效地解决了当前驱动模型的局限性.
- 该模型在复杂场景中为自动驾驶汽车安全验证提供了显著的价值.
- 这项研究有助于为AV安全法规开发未来的参考驾驶员模型.
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