As the technical trend of the next generation of high-level autonomous driving， brain-inspired learning is a class of methods that employ deep neural networks （DNN） as the strategy carrier and reinforcement learning （RL） as the training algorithm to realize strategy self evolution through continuous interaction with traffic environments， ultimately obtaining the optimal mapping from the environmental state to execution action. Currently， brain-inspired learning is mainly applied in decision-making and motion control modules of autonomous driving. Its key technologies include how to design its system framework to support interactive training， high-fidelity autonomous driving simulation platform， accurate and flexible representation of environment statues， multiple dimensional evaluation metrics， and effective training algorithm that drives policy updates. This paper systematically summarizes the history and future trends of decision-making and control functionalities in autonomous vehicles， including two main modular architectures （HDC， i.e.， hierarchical decision & control and IDC， i.e.， integrated decision & control） and three mainstream technical solutions （i.e.， rule-based design， supervised learning， and brain-inspired learning）. An overview of autonomous driving simulation platforms are briefly introduced， followed by three effective designing methods for representing traffic environment states （i.e.， object-based design， feature-based design， and combined design）. The paper also introduces multiple dimensional evaluation metrics for autonomous vehicles， which can describe self-driving performances including driving safety， regulatory compliance， driving comfort， travel efficiency， energy efficiency. Typical reinforcement learning algorithms， including their design principles， taxonomy， and algorithm performances， are introduced， as well as their application on brain-inspired autonomous driving systems in the systematic design of road-cloud cooperation.
