Lane change trajectory prediction by using recorded human driving data

Being able to predict the trajectory of a human driver's potential lane change behavior in urban high way scenario is crucial for lane change risk assessment task. A good prediction of the driver's lane change trajectory makes it possible to evaluate the risk and warn the driver beforehand. Rather than generating such a trajectory only using a mathematical model, this paper develops a lane change trajectory prediction approach based on real human driving data stored in a database. In real-time, the system generates parametric trajectories by interpolating k human lane change trajectory instances from the pre-collected database that are similar to the current driving situation. In order to build this real lane change database, a human lane change data collection vehicle platform is developed. Extensive experiments have been carried out in urban highway environments to build a significant database with more than 200 lane changes. Real results show that this approach produces lane change trajectories that are quite similar to real ones which makes it suitable to predict humanlike lane change maneuvers.