基于天气条件的自主应急车模型
Ling Han1, RuoYu Fang1, Hui Zhang1
1School of Mechatronic Engineering, Changchun University of Technology, Changchun, China.
Traffic injury prevention
|July 12, 2023
概括
这项研究引入了一种自适应式自主紧急制动 (AEB) 系统,该系统可以改善在恶劣天气中避免碰撞. 适应性AEB系统在雨天和雾中提高了安全性和可靠性,性能优于传统的AEB系统.
科学领域:
- 汽车安全工程 汽车安全工程
- 交通运输中的人工智能
- 机器学习用于车辆动力学
背景情况:
- 车辆主动安全系统,如自主紧急制动 (AEB),对于避免碰撞至关重要.
- 传统的AEB系统依赖于针对正常天气条件优化的安全距离计算.
- 恶劣天气显著降低了传统AEB系统的性能和预警能力.
研究的目的:
- 开发一种能够在恶劣天气条件下有效运行的自适应AEB系统算法.
- 在具有挑战性的环境场景中提高AEB系统的安全性和可靠性.
- 为了提高在雨和雾等恶劣天气中避免碰撞的性能.
主要方法:
- 使用多层感知子 (MLP) 模型来分析事故和天气数据.
- 该MLP模型被训练来预测事故严重程度,这告诉了自适应的AEB算法.
- 适应性AEB模型使用Prescan模拟和驾驶员在循环系统进行了验证.
主要成果:
- 与传统的AEB系统相比,自适应AEB系统在恶劣天气模拟中表现出更高的性能.
- 测试证实,自适应AEB算法在雨和雾等条件下提高了安全性和可靠性.
- 该系统成功地增加了雨天的安全距离,并防止了雾环境中的碰撞.
结论:
- 开发的自适应式AEB系统有效地减轻了在恶劣天气下AEB的性能下降.
- 适应性AEB算法在恶劣天气下显著改善了车辆安全和避免碰撞.
- 这项研究强调了人工智能驱动的自适应系统在现实驾驶条件下提高汽车安全的潜力.
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