Toward human-like automated vehicles: Review and perspectives on behavioural decision making and intelligent motion planning

Abstract

Abstract Rapid advancements in automated vehicles (AVs) technology have transformed the measurability, controllability, and unpredictability of transportation systems. Evaluating driving risk and effectively managing driving behaviours is critical. AVs should be enabled to identify, analyse, evaluate, and devise effective countermeasures for driving risk by autonomously learning from human driving experiences. This learning will enhance the interactive decision-making capabilities and achieve driving behaviours that reflect human-like logic. The primary challenge lies in integrating human-like logic and driving risk constraints based on behavioural decision-making outcomes. This integration is crucial to align the AVs' cognitive levels—movement, comprehension, memory, and inference—more closely with human driving necessities, habits, and styles. Such alignment holds the potential to improve the prediction and planning of future actions and facilitate the development of motion planning schemes geared towards minimizing driving risk. We performed a comprehensive review of AVs' behavioural decision-making and intelligent motion planning research from 2000 to 2023 from four key perspectives—driving risk, human-like logic, behavioural decision-making and motion planning. Based on the Web of Science and China National Knowledge Internet database, the results of our review indicate that significant progress has been made in AVs behavioural decision-making and intelligent motion planning over time. When AVs and human-driven vehicles coexist, greater incorporation of human-like logic is required. Guided by these findings, we delineate future development directions and propose a research paradigm for human-like logic and a research framework for human-like logic-driven behavioural decision-making and intelligent motion planning of AVs.