自动驾驶汽车的智能防御驾驶:框架,战略和验证
Ting Zhang1, Zixuan Wang1, Hong Wang1
1School of Vehicle and Mobility, Tsinghua University, Beijing 100084, PR China.
Accident; analysis and prevention
|December 17, 2025
概括
本研究介绍了自动驾驶汽车 (AV) 的智能防御驾驶,以处理罕见的高风险场景. 通过整合人类驾驶专业知识,自动驾驶汽车可以更好地预测危险并提高安全性.
科学领域:
- 智能运输系统 智能运输系统
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 传统的自动驾驶汽车 (AV) 决策与不常见的高不确定性场景作斗争,导致安全问题.
- 人类防御性驾驶专业知识为提高复杂情况下的自动驾驶安全提供了有价值的模型.
研究的目的:
- 开发一套集成的方案,用于智能防御驾驶在AVs.
- 利用人类的防御性驾驶经验来提高AV安全,并系统地解决长尾问题.
主要方法:
- 构建了一个智能防御驾驶的框架,包括场景分类和层次设计.
- 开发了一个使用经验触发条件的在线监控机制.
- 将正式的防御性驾驶经验整合到安全决策策略中.
- 使用"感知不足"场景验证的性能.
主要成果:
- 防御性驾驶触发机制在各种场景中成功预测了风险.
- 观察到的改进:制动距离7.49m,碰撞时间1.56s,制动时间2.84s.
- 显著降低了紧急事故的概率,同时保持了系统的稳定性和实时性能.
结论:
- 提出的智能防御驾驶方案有效地提高了自动驾驶汽车的安全性,特别是在具有挑战性的场景中.
- 将人类专业知识纳入AV决策是解决长尾问题和提高整体可靠性的关键.
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