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Open Access Publications from the University of California

Learning, Classification and Prediction of Maneuvers of Surround Vehicles at Intersections using LSTMs

  • Author(s): Khosroshahi, Aida
  • Advisor(s): Trivedi, Mohan M
  • et al.
Abstract

Behavior analysis of vehicles surrounding the ego-vehicle 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. A variety of sensros including stereo cameras, LIDAR, GPS, and IMU measurements are used to extract ego-motion compensated surround trajectories from five different datasets. 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.

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