临界场景 基于层次增强架构的智能汽车测试对抗生成方法
Bing Zhu1, Rui Tang1, Jian Zhao1
1National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130022, China.
Accident; analysis and prevention
|March 23, 2025
概括
本研究介绍了一种使用层次强化学习的对抗生成方法,以创建智能汽车的多样化和有效的关键测试场景. 与传统方法相比,这种方法显著提高了测试效率和资源利用率.
科学领域:
- 智能运输系统 智能运输系统
- 自动驾驶技术 自动驾驶技术
- 机器学习用于模拟
背景情况:
- 智能汽车需要在各种场景中进行广泛的测试,以验证安全性和性能.
- 创建关键测试场景的现有方法缺乏多样性和有效性,阻碍了准确的评估.
- 敌对生成提供了一种有希望的方法来克服这些局限性.
研究的目的:
- 为关键测试场景提出一个基于层次化的强化学习框架的对抗生成方法.
- 增强智能汽车关键场景生成的多样性,有效性和效率.
- 为了减少智能汽车开发中的整体测试资源利用率.
主要方法:
- 一个分层的强化学习框架,有三个模块:分层安排,冲突预测和场景评估.
- 层次安排管理测试期 (指导,对抗,探索) 以解决奖励稀疏性.
- 冲突预测使用动力学分析和适应性策略;评估使用轨迹,时间,空间分析和知觉有限的重复测试.
主要成果:
- 拟议的方法有效地在高速公路环境 (HighD数据集) 中生成多样化和关键的测试场景.
- 它在测试过程中证明了较好的碰撞率和周期贡献.
- 与Deep Q-Network相比,实现了测试资源利用率减少49.49%,用于生成同等关键场景.
结论:
- 对抗生成方法有效地解决了智能汽车关键测试场景创建方面的局限性.
- 层次化的强化学习框架提高了场景的多样性,有效性和测试效率.
- 这种方法为自动驾驶汽车测试的资源利用提供了显著的改善.
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