自动驾驶场景的定量表示 基于对抗性政策搜索的难度场景
Shuo Yang1, Caojun Wang1, Yuanjian Zhang2
1School of Automotive Studies, Tongji University, Shanghai 201804, China.
Research (Washington, D.C.)
|December 15, 2025
概括
本研究引入了一种数据驱动的方法来量化自动驾驶汽车 (AV) 场景的难度. 它可以生成有针对性的场景,以改善AV学习和适应复杂的交通环境.
科学领域:
- 自主系统 自主系统
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自动驾驶汽车需要通过学习算法不断提高性能,以适应动态交通.
- 现实世界交通的复杂性和变化需要方法来表示和生成具有挑战性的场景,以进行有效的自动驾驶培训.
- 现有的方法通常依赖于离散的,基于规则的难度表示,限制了适应性.
研究的目的:
- 提出一种数据驱动的定量方法来表示自动驾驶中的场景困难.
- 为了能够生成有针对性的,逐渐困难的场景,以实现强大的AV算法演变.
- 开发一个系统,在没有专家规则设计的情况下提供连续和可解释的难度指标.
主要方法:
- 引入了使用强化学习与机制知识进行培训的"环境代理人"的概念.
- 从各种培训阶段的环境代理参数开发了一个政策组,以创建具有不同对抗强度的代理.
- 在模拟环境中利用这些代理来生成跨越各种场景困难的数据.
- 构建了一个数据驱动模型,以根据环境代理政策量化表示场景难度.
主要成果:
- 提出的方法有效地产生了合理的,可解释的,高度歧视性的场景.
- 在不依赖专家逻辑规则的情况下,可以实现量化难度表示.
- 这种方法可以实现连续的,而不是离散的场景难度表示,优于基于规则的方法.
- 实验结果验证了数据驱动的定量表示模型的有效性.
结论:
- 开发的方法提供了一个强大的,数据驱动的方法来量化和生成自动驾驶汽车培训场景.
- 这促进了AV算法的更有效,更有针对性的进化,提高了它们对复杂环境的适应性.
- 难度表示的连续性和可解释性比传统方法具有显著的优势.
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