Data-Driven Vehicle Trajectory Forecasting

Abstract

An active area of research is to increase the safety of self-driving vehicles. Although safety cannot be guarenteed completely, the capability of a vehicle to predict the future trajectories of its surrounding vehicles could help ensure this notion of safety to a greater deal. We cast the trajectory forecast problem in a multi-time step forecasting problem and develop a Convolutional Neural Network based approach to learn from trajectory sequences generated from completely raw dataset in real-time. Results show improvement over baselines.

Published in ECML KNOWMe: 2nd International Workshop on Knowledge Discovery from Mobility and Transportation Systems 2018

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Cited by in corpus (2)

• Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data
• Vehicle trajectory prediction in top-view image sequences based on deep learning method