Learning Vehicle Surrounding-aware Lane-changing Behavior from Observed Trajectories.

Predicting lane-changing intentions has long been a very active area of research in the autonomous driving community. However, most of the literature has focused on individual vehicles and did not consider both the neighbor information and the accumulated effects of vehicle history trajectories when making the predictions. We propose to apply a surrounding-aware LSTM algorithm for predicting the intention of a vehicle to perform a lane change that takes advantage of both vehicle past trajectories and their neighbor’s current states. We trained the model on real-world lane changing data and were able to show in simulation that these two components can lead not only to higher accuracy, but also to earlier lane-changing prediction time, which plays an important role in potentially improving the autonomous vehicle’s overall performance.