Review on Autonomous Vehicle Motion Planning Methods Considering Social Behavior

doi:10.3901/JME.2024.10.112

Abstract

Abstract: At present, autonomous driving technology is at a critical stage of transition from demonstration operations in parks to large-scale market applications in open scenarios. If self-driving vehicles are to be used in people's daily lives, they are bound to coexist with human-driven vehicles on the road for a long time, which inevitably generates a large number of interaction scenarios, and how to properly interact with human-driven vehicles becomes the key to improving the driver's experience and their acceptance of autonomous driving. Human interaction is based on its social attributes, and so should autonomous vehicles. However, there is no comprehensive review in this area. In light of this, the ins and outs and recent developments are reviewed. The driving dilemma faced by traditional methods, the research on the mechanism of driver social behaviour, and the recent progress and application cases of motion planning methods considering social attributes are discussed in turn. On this basis, the shortcomings of the existing methods are summarized and future research directions are prospected. The analysis shows that combining theoretical tools from sociology and other fields, analysing drivers’ social behavioural mechanisms in complex traffic flows, and constructing an automated decision-making system for driving behaviour that meets public expectations based on this is the main research direction for future social motion planning for autonomous driving. In addition, further establis0hing a sound evaluation system for the social behaviour of autonomous driving is equally important for the social humanization design of intelligent vehicles.

Key words: autonomous vehicles, social compliance, motion planning, interaction behaviour, new mixed traffic flow

CLC Number:

U46
TP399

GAO Zhenhai, YU Tong, SUN Tianjun. Review on Autonomous Vehicle Motion Planning Methods Considering Social Behavior[J]. Journal of Mechanical Engineering, 2024, 60(10): 112-128.

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