Review on Human-machine Shared Control of Intelligent Vehicles

Abstract

A shared control of intelligent vehicles consists of a human driver and an automation to complete the driving task. Through human-machine intelligence hybrid enhancement, driving safety is guaranteed and driving performance is improved. According to the current research status of human-machine shared control, the concept is mentioned. The decision-making method of human-machine shared control can be divided into objective risk assessment indicators and driver factors. The characteristics and application scope of direct and indirect shared control frameworks are analyzed. Five shared control methods are reviewed, and the advantages and limitations of each control strategy are discussed. The evaluation indexes of human-machine shared control system are summarized. Finally, the existing problems and future research direction of human-machine shared control are pointed out. The analysis shows that the future research direction is to combine machine learning with existing model-based methods and comprehensively consider the impact of driver information, vehicle state and dynamic environment on driving risk. Simultaneously, the safety test standards and evaluation systems for the safety of the intended functionality of autonomous driving should be established. It is the key of commercial application to ensure driving safety through safety certification of the intended functionality.