An Intelligent Architecture for Cognitive Autonomous Driving Based on Parallel Testing
Driven by the new generation of artificial intelligence technologies such as big data,cloud computing and machine learning,the perceptive intelligence of autonomous driving has been significantly improved and progressed in recent years.However,compared with the self-purpose driven human driving process,the current autonomous driving technologies are mainly focusing on the auxiliary driving functions,and still stay in a primary-intelligence stage which is dominated by passive perception,planning and control.In order to cross the"cognition gap"of vehicle intelligence,from data-driven environment perception,assisted decision making and passive planning to knowledge-driven scenario cognition,reasonable decision making and active planning,it is important to enhance the humanoid abilities of the vehicles,including but not limited to summarize and extract complex external informa-tion from the environment,reasoning,evaluation and estimation.This paper reviews the evolution and application of key technologies in autonomous driving,analyzes the effectiveness of testing on vehicle intelligence and perform-ance evaluation.After then,based on the parallel test theory,it puts forward the space construction method for training,testing and evaluating the cognitive intelligence of autonomous vehicle,and proposes an intelligent train-ing framework for cognitive autonomous driving.The work is expected to provide a feasible and possible path for autonomous vehicle cognitive intelligence.
万方数据
维普
