生成自然主义和关键边界场景:一个双层自适应的深度强化学习方法
Junjie Zhou1, Lin Wang2, Qiang Meng3
1State Key Laboratory of Submarine Geoscience, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; College of Engineering, Ocean University of China, Qingdao, 266100, China.
Accident; analysis and prevention
|October 8, 2025
概括
本研究引入了双层自适应深度强化学习 (BADRL) 框架,以创建用于测试自动驾驶汽车的现实驾驶场景. 该BADRL方法提高了关键边界场景生成效率15.89%.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现实世界驾驶对自动驾驶汽车 (AV) 测试提出了复杂的挑战.
- 有限的自然和关键测试场景阻碍了全面的AV性能评估.
- 现有的方法与公正和有效的场景生成作斗争.
研究的目的:
- 提出一个新的框架,用于为AVs生成现实的和多样化的临界边界场景.
- 开发一种人工智能驱动的方法,用于公正的AV性能评估.
- 提高AV测试环境的效率和真实性.
主要方法:
- 开发了一个双层自适应深度强化学习 (BADRL) 框架.
- 人工智能代理人接受了自然驾驶数据的训练,以模仿现实的行为.
- 引入了一个场景复杂性模型,用于实时复杂性评估和动态升级.
- 不同的交通参与者 (车辆,行人,自行车) 被模拟为复杂的互动.
主要成果:
- 该BADRL框架可以在线实时生成自然主义和关键边界场景.
- 该方法有效地模拟了各种交通参与者之间复杂的交互行为.
- 模拟实验证明了BADRL方法在各种驾驶环境中的有效性.
- 与最先进的方法相比,在关键边界场景生成效率上观察到大约15.89%的改善.
结论:
- BADRL框架在为自动驾驶汽车生成高保真测试场景方面取得了重大进展.
- 该方法解决了当前AV测试中的关键局限性,为更强大,更可靠的自动驾驶系统铺平了道路.
- 拟议的方法提高了关键场景生成的效率和现实性,这对于安全的AV部署至关重要.
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