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考虑驾驶能力和风格的自动驾驶车辆个性化共享控制

Personalized Shared Control for Automated Vehicles Considering Driving Capability and Styles.

作者信息

Sun Bohua, Shan Yingjie, Wu Guanpu, Zhao Shuai, Xie Fei

机构信息

College of Automotive Engineering, the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.

College of Intelligence and Computing, Tianjin University, Tianjin 300354, China.

出版信息

Sensors (Basel). 2024 Dec 11;24(24):7904. doi: 10.3390/s24247904.

DOI:10.3390/s24247904
PMID:39771642
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11679979/
Abstract

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers' driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers' main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers' driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers' driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems.

摘要

共享控制系统一直是关键的技术框架和趋势，它在克服自动驾驶车辆安全性和舒适性方面的性能不足具有优势。了解人类驾驶员的驾驶能力和风格是提高系统性能的关键，特别是共享控制车辆被人类驾驶员接受和适应的程度。在本研究中，提出了考虑驾驶员主要人为因素的个性化共享控制。建立了一种针对人为因素的模拟场景生成方法。定义并评估了驾驶员的驾驶能力，以提高驾驶权限分配的合理性。在意图感知个性化自动子系统的现场测试中，对驾驶员的驾驶风格进行了分析、特征提取和评估。基于驾驶能力和风格提出了个性化共享控制框架，并建立了其评估标准，包括驾驶安全性、舒适性和工作量。在人在回路仿真平台和基于自动驾驶车辆的现场测试中对个性化共享控制系统进行了评估。结果表明，与仅由人类驾驶员或自动系统驱动的情况相比，所提出的系统在不同驾驶能力、风格和复杂场景下能够实现更好的性能。

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