Toward human-like lane following behavior in urban environment with a learning-based behavior-induction potential map

Abstract

In order to achieve harmony in the mixed traffic, it is crucial to have autonomous vehicles behave like human drivers. This work addresses a vision-based approach toward human-like lane following behavior in complex urban environment. At first, a deep architecture is adopted to generate a set of vehicle hypotheses. Subsequently, a hybrid merging procedure is performed to jointly output the final detection results based on both the image evidence and the statistical support of vehicle hypotheses. After that, the detected vehicles are classified into six categories by Bayesian Network, i.e., leader vehicle, parking vehicle, tail-end vehicle, exiting vehicle, merging vehicle and other vehicle. With this information, a learning-based instance-level behavior-induction potential map is constructed to generate a safe as well as reasonable local path for following a predefined lane-level route. Experimental results in various typical but challenging urban traffic scenes substantiated the effectiveness of the proposed approach.