Data-Driven Vehicle Trajectory Forecasting

2019/02/09 by Shayan Jawed, Jawed, Shayan, Eya Boumaiza +5
Computer Science · Engineering · Mathematics · #Autonomous Vehicle Technology and Safety #Traffic Prediction and Management Techniques #Traffic and Road Safety #cs.CV #cs.LG #cs.RO #stat.ML

paper · pdf · doi:10.48550/arxiv.1902.05400

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

arxiv created 2019/02/09 · arxiv updated 2019/02/15

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.

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