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混合监督和强化学习用于自动驾驶汽车的运动疾病意识路径跟踪

Yukang Lv¹, Yi Chen¹, Ziguo Chen¹

¹College of Automotive Engineering, Jilin University, Changchun 130025, China.

Sensors (Basel, Switzerland)

|June 27, 2025

概括

本研究介绍了一种混合监督强化学习 (HSRL) 框架,用于自动驾驶路径跟踪. HSRL提高了跟踪精度和计算效率,同时显著减少了乘客的运动病 (MS).

科学领域:

机器人和控制系统机器人和控制系统
人工智能的人工智能
人与计算机的交互

背景情况:

路径跟踪对于自动驾驶 (AD) 的安全,舒适和效率至关重要.
现有的方法难以平衡跟踪精度与计算成本,并且没有解决运动性疾病 (MS).
随着乘客从事非驾驶活动,MS在AD中越来越令人担忧.

研究的目的:

开发一个新的框架,混合监督强化学习 (HSRL),用于AD路径跟踪.
为了减少乘客的不适和运动性疾病 (MS),同时保持高跟踪精度和计算效率.
为了解决当前路径跟踪控制器的局限性.

主要方法:

实施了混合监督强化学习 (HSRL) 框架.
利用专家数据引导的监督学习来优化路径跟踪模型,解决强化学习 (RL) 样本效率的问题.
将乘客MS机制集成到RL的多目标奖励功能中,以增强强性和舒适性.

主要成果:

与比例整数导数 (PID) 和模型预测控制 (MPC) 相比,HSRL框架显示出更高的性能.
实现了高精度路径跟踪,并提高了计算效率.
在各种场景中显著降低了乘客的累积运动恶性剂量值 (MSDV).

结论:

HSRL有效地平衡了高精度路径跟踪与在自动驾驶中优化乘客舒适度.
拟议的框架为AD路径跟踪提供了一个计算效率高的解决方案.
HSRL提出了一种有希望的方法来缓解自动驾驶汽车的运动病.

关键词:

自动驾驶汽车是自动驾驶的运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 运动性 motion sickness 路径跟踪跟踪路径跟踪强化学习是一种强化学习.监督学习学习监督学习

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