Learning-based trajectory generation for intelligent vehicles in urban environment

Recent technologies of intelligent vehicles are getting more attentions with promising deployment to commercial cars. In this paper, we present a learning-based trajectory generation approach for implementing an advanced driver assistance system (ADAS) with the lane keep assist and adaptive cruise control functions in urban environment. More specifically, a number of objects of interest, including the road and lane boundaries, as well as the surrounding vehicles, are detected and tracked. Particularly, the leader vehicle in the host lane, if available, is detected to provide the real-time, on-site and validated information, including both movements and decisions of how it copes with the current traffic situation, which is subsequently learnt by the ego vehicle for the control purposes. By combining the prior and “live” information of the road environment, a safe, smooth and reasonable trajectory is finally generated based on a cubic spline model with the Mass-Spring-Damper (MSD) system. Experimental results in various typical but challenging urban traffic scenes have substantiated the effectiveness of the proposed system.