个性化的前方碰撞预警模型,从人类偏好中学习
Ning Xie1, Rongjie Yu1, Weili Sun2
1College of Transportation Engineering, Tongji University, Shanghai, 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804 Shanghai, China.
Accident; analysis and prevention
|September 29, 2024
概括
个性化前方碰撞预警 (FCW) 模型通过适应驾驶员个人的风险偏好来提高车辆安全. 这提高了警报同步和驾驶员的信心,降低了碰撞风险.
科学领域:
- 汽车安全 汽车安全
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 背后撞车是最常见的车辆碰撞.
- 现有的前方碰撞预警系统 (FCW) 的响应率很低,这限制了它们的有效性.
- 需要对FCW系统进行个性化,以增强驾驶员的信心和安全.
研究的目的:
- 开发个性化的FCW模型,与个体驾驶员的风险偏好保持一致.
- 提高FCW系统与驾驶员行为的响应率和同步.
- 提高司机对自动化安全系统的信心.
主要方法:
- 开发了一个警告反指数,以量化驾驶员风险感知和FCW模型之间的差异.
- 个人驾驶员的风险感知偏好使用奖励模型进行了表征.
- 一个基准FCW模型使用近接政策优化 (PPO) 算法进行了微调,以个性化奖励模型为指导.
- 经验分析使用了来自74名司机的95,814个警告片段.
主要成果:
- 由个性化模型生成的伪警告结果的精度从53.5%增加到78.2%.
- FCW警告和驾驶员车行为之间的平均时间差距从2.4秒减少到1.6秒.
- 在个性化的FCW模型和个体驾驶员的风险感知之间实现了更高的同步水平.
结论:
- 个性化的FCW模型显著提高了警告的准确性和及时性.
- 加强FCW系统与驾驶员之间的同步,提高了驾驶员的信心和系统的有效性.
- 这种方法为开发更有效,更值得信赖的车辆安全系统提供了一个有希望的方向.
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