开发基于实际驾驶员偏好的驾驶员适应性车道保持辅助系统的方法.
Jiachen Chen1, Hui Chen1, Xiaoming Lan1
1School of Automotive Studies, Tongji University, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究引入了一种驾驶员适应车道维护辅助 (DALKA) 系统,该系统学习驾驶员的个人偏好. 这种新的方法通过调整系统来提高驾驶员的满意度.
科学领域:
- 汽车工程 汽车工程
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 车道维护辅助 (LKA) 系统旨在适应个体驾驶员,但经常假设驾驶员的偏好与自然驾驶相匹配.
- 这种假设限制了当前适应性LKA系统的有效性.
研究的目的:
- 开发一种新的驾驶员自适应车道维护辅助 (DALKA) 系统,准确地反映个人驾驶员的实际偏好.
- 通过个性化LKA行为来提高驾驶员满意度和系统效率.
主要方法:
- 从自然驾驶数据中提取指标,使用行动点理论来描述驾驶员.
- 利用主观和客观的评估来确定司机真正的LKA偏好.
- 采用机器学习来建模驾驶特征和偏好之间的关系,将预测集成到DALKA系统中.
主要成果:
- 由测试驾驶员主观评估开发的DALKA系统.
- 结果表明,拟议的DALKA系统可以增强或维持大多数驾驶员的主观评价.
结论:
- 这种新的方法有效地捕捉并将个人驾驶员的偏好集成到适应性LKA系统中.
- 根据实际驾驶员偏好个性化LKA系统可以提高用户体验和满意度.
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