A Cognition-Inspired Human-Like Decision-Making Method for Automated Vehicles

Drivers' cognitive mechanisms could benefit the development of human-like automated driving (AD) strategies, which are with high intelligence and comfort levels. The common approach of human-like AD is to learn from human demonstration data, for which it is exhausting and difficult to construct well-rounded and reliable datasets. Therefore, we proposed the human-like AD decision-making method based on drivers' cognition mechanism. The fundamental and difficult thing of this method is to figure out drivers' cognitive mechanism systematically and comprehensively, which is still either too rough or too fragmented for AD development. By integrating the abundant studies about drivers' cognition in multiple fields, we propose two novel conceptual models: Potential Hazard Model (PHM) illustrates the mechanisms of drivers' reaction in the simple meta-scenarios while Candidate Selection Model (CSM) explains how drivers handle complicated scenarios based on PHM. Based on PHM and CSM, we propose the human-like decision-making method for AD. This method integrates cognitive mechanisms, natural driving data, optimization-based planning, and other techniques profoundly. The experiments in extensive road and traffic scenarios verify that the method show good generalizability, interpretability, and human-likeness.