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.
Surround vehicles trajectory analysis with recurrent neural networks
01.11.2016
951755 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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