The rapid advance of autonomous vehicles(AVs)has motivated new perspectives and potential challenges for existing modes of transportation.Currently,driving assistance systems of Level 3 and below have been widely produced,and several applications of Level 4 systems to specific situations have also been gradually developed.By improving the automation level and vehicle intelligence,these systems can be further advanced towards fully autonomous driving.However,general development concepts for Level 5 AVs remain unclear,and the existing methods employed in the development processes of Levels 0-4 have been mainly based on task-driven function development related to specific scenarios.Therefore,it is difficult to identify the problems encountered by high-level AVs.The essential logical and physical mechanisms of vehicles have hindered further progression towards Level 5 systems.By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving,we put forward a coordinated and balanced framework based on the brain-cerebellum-organ concept through reasoning and deduction.Based on a mixed mode relying on the crow inference and parrot imitation approach,we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning,self-adaptation,and self-transcendence for AVs.From a systematic,unified,and balanced point of view and based on least action principles and unified safety field concepts,we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs,specifically at Level 5.
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