LAUSR

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment.