自动驾驶汽车安全验证的密集增强学习
Shuo Feng1,2,3, Haowei Sun1, Xintao Yan1
1Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA.
Nature
|March 23, 2023
概括
使用人工智能加速自动驾驶汽车的安全验证. 密集的深度强化学习 (D2RL) 训练代理人创建具有挑战性的场景,大大缩短了测试时间.
科学领域:
- 人工智能
- 自主系统
- 机器学习
背景情况:
- 自动驾驶汽车的安全验证由于罕见的关键事件而昂贵且耗时.
- 目前的方法难以有效地测试安全关键的场景.
研究的目的:
- 为加速自动驾驶汽车安全验证开发智能测试环境.
- 训练人工智能代理人识别和执行敌对的机动.
主要方法:
- 开发了一种密集的深度强化学习 (D2RL) 方法.
- 训练有素的代理人使用自然驾驶数据来学习对手的机动.
- 使用增强现实环境, 结合模拟和现实世界元素进行测试.
主要成果:
- 经过D2RL培训的代理人将安全评估过程加快了10^3到10^5倍.
- 这种方法保持了安全性能验证的公正性.
- 在高速公路和城市测试轨道上表现出有效性.
结论:
- D2RL提供了一种高效的自动驾驶汽车安全验证方法.
- 这种方法大大降低了测试的经济和时间成本.
- D2RL适用于加快其他安全关键自主系统的测试和培训.
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