HAD-Gen:为可控制的场景生成提供类似人类和多样化的驾驶行为建模
Cheng Wang1, Lingxin Kong2, Massimiliano Tamborski3
1School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
Accident; analysis and prevention
|October 7, 2025
概括
HAD-Gen通过模拟自动驾驶汽车 (AV) 的各种类似人类驾驶行为来生成现实的交通场景. 这种先进的框架通过在复杂的驾驶情况下增强概括能力来改进AV测试.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 基于模拟的测试对于自动驾驶汽车 (AV) 验证和验证至关重要.
- 当前的驾驶员模型 (决定性,模仿学习) 无法捕捉到人类驾驶的变化.
- 要解决这些局限性,需要创建现实的交通场景.
研究的目的:
- 提出HAD-Gen,这是一个用于生成现实的交通场景的一般框架,具有各种类似人类的驾驶行为.
- 加强基于模拟的自动驾驶汽车测试.
- 提高在未见的场景中推动政策的概括能力.
主要方法:
- 根据安全特征将车辆轨迹数据聚合到不同的驾驶风格中.
- 应用最大反增强学习来学习驾驶风格特定的奖励函数.
- 整合线下强化学习预训练和多代理强化学习,以实现强大的驾驶政策.
主要成果:
- 在高速公路场景中,HAD-Gen成功地产生了多样化的,类似人类的驾驶行为.
- 该框架在新的,未见的驾驶场景中展示了强大的概括能力.
- 实现了90.96%的目标达到率,2.08%的越野率和6.91%的碰撞率,在目标达到方面超过了以前的方法的20%.
结论:
- 拟议的HAD-Gen框架有效地模拟了类似人类的驾驶行为,以实现现实的AV测试.
- HAD-Gen显著提高了自动驾驶政策的性能和通用性.
- 这种方法提供了一个强大的解决方案,用于生成复杂的交通场景,这对于AV开发至关重要.
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