Abstract: It is one of the key technologies in intelligent vehicle development to precisely decode the hidden semantic characteristics from driving signals along time sequences. This paper systematically proposed a framework of semantic analysis in driving behavior research for decision making in autonomous driving. The 4 stages of semantic analysis were summarized including car-following and lane change model, driving maneuver offline recognition, driver intention online prediction, and unsupervised sematic analysis. The state-of-the-art development of the 4 key techniques in semantic analysis was introduced in detail at home and abroad. The key techniques include maneuver recognition and prediction, smart decision making, driving style estimation, and behavior segmentation. The learning algorithms in semantic analysis for autonomous driving development were introduced in detail, including Bayesian network, hidden Markov model, deep neural network, and reinforcement learning. Finally, the applications of semantic analysis in driver assistance, intelligent decision making, and path planning were discussed for better development of autonomous driving technologies.

Key words: autonomous driving, intelligent vehicle, driving behavior, semantic analysis, learning algorithms, human-like decision making, driver assistance, path planning