Surround vehicles trajectory analysis with recurrent neural networks

Behavior analysis of vehicles surrounding the egovehicle is an essential component in safe and pleasant autonomous driving. This study develops a framework for activity classification of observed on-road vehicles using 3D trajectory cues and a Long Short Term Memory (LSTM) model. As a case study, we aim to classify maneuvers of surrounding vehicles at four way intersections. LIDAR, GPS, and IMU measurements are used to extract ego-motion compensated surround trajectories from data clips in the KITTI benchmark. The impact of different prediction label space choices, feature space input, noisy/missing trajectory data, and LSTM model architectures are analyzed, presenting the strengths and limitations of the proposed approach.