Lane change is one of the most principle driving behaviors on structure roads. It frequently happens in daily driving. A key issue in lane change technique is trajectory planning, where a set of trajectories describing possible vehicle motions are generated by applying a parametric function, and by uniformly sampling the end states in configuration space; the trajectories are then examined to find an optimal one for execution. However, such a trajectory set has poor efficiency due to the large sample number. Many trajectories in this set seldom happen in real human driving behaviors. In this research, lane change trajectories are collected from real driving data of different drivers. Their statistics are analyzed, through which, a simplified trajectory set is generated. Experiment results show that the trajectory set has much less number of samples but can still guarantee to cover usual lane change behaviors of human being.
Learning lane change trajectories from on-road driving data
2012 IEEE Intelligent Vehicles Symposium ; 885-890
01.06.2012
1863411 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Learning Lane Change Trajectories from On-Road Driving Data
British Library Conference Proceedings | 2012
|Generating lane-change trajectories using the dynamic model of driving behavior
Tema Archiv | 2011
|