汽车工程 ›› 2024, Vol. 46 ›› Issue (9): 1617-1627.doi: 10.19562/j.chinasae.qcgc.2024.09.009

• • 上一篇    

自动驾驶车辆乘坐舒适性评价研究综述

张国娟1,胡宏宇1,李浩淼1,王明剑2,高菲1(),高镇海1   

  1. 1.吉林大学,汽车底盘集成与仿生全国重点实验室,长春 130022
    2.中国第一汽车股份有限公司,长春 130013
  • 收稿日期:2024-05-30 修回日期:2024-08-05 出版日期:2024-09-25 发布日期:2024-09-19
  • 通讯作者: 高菲 E-mail:gaofei123284123@jlu.edu.cn
  • 基金资助:
    国家自然科学基金(52272417);吉林省重大科技专项(20220301009GX)

A Survey on Ride Comfort Evaluation of Autonomous Vehicles

Guojuan Zhang1,Hongyu Hu1,Haomiao Li1,Mingjian Wang2,Fei Gao1(),Zhenhai Gao1   

  1. 1.Jilin University,National Key Laboratory of Automotive Chassis Integration and Bionics,Changchun 130022
    2.China FAW Corporation Limited,Changchun 130013
  • Received:2024-05-30 Revised:2024-08-05 Online:2024-09-25 Published:2024-09-19
  • Contact: Fei Gao E-mail:gaofei123284123@jlu.edu.cn

摘要:

随着自动驾驶技术的快速发展,乘坐舒适性已成为影响自动驾驶车辆用户接受度和体验感的关键因素。本文针对自动驾驶车辆乘坐舒适性评价的研究现状进行系统性综述。首先,阐述了舒适性的含义,并分析了影响乘坐舒适性的主要因素。其次,对自动驾驶车辆的量化指标和评价模型进行了分类与详细阐述。其中,量化指标分为主观量化指标、基于车辆参数的量化指标、基于生理信号的量化指标以及基于驾驶员行为的量化指标;评价模型包括心理物理学模型、生物力学模型、统计学模型以及基于学习的评价模型。最后,提出了自动驾驶车辆舒适性研究的未来发展趋势,为进一步研究自动驾驶车系统设计与用户体验提升提供了技术参考。

关键词: 自动驾驶汽车, 乘坐舒适性, 量化指标, 评价模型, 综述

Abstract:

With the rapid development of automated driving technology, ride comfort has become a key factor affecting user acceptance and overall experience with automated vehicles. In this paper, a comprehensive review of the current state of research concerning the evaluation of riding comfort in automated vehicles is presented. Firstly, the concept of comfort is thoroughly articulated, followed by an analysis of key factors influencing ride comfort. Subsequently, the quantitative indicators and evaluation models pertinent to automated vehicles are classified and elaborated in detail. The quantitative indicators are classified into four categories: subjective indicators, indicators derived from vehicle parameters, indicators based on physiological signals, and indicators related to driver behaviour. The evaluation models encompass psychophysical models, biomechanical models, statistical models, and learning-based evaluation models. Finally, prospective trends in the research of comfort in automated vehicles is brought forward, thereby offering a technical framework for further studies on the system design and user experience in this domain.

Key words: automated vehicles, ride comfort, quantitative indicators, evaluation model, review