Data-Driven Vehicle Trajectory Forecasting
This addresses safety concerns in autonomous driving by enhancing trajectory prediction, though it appears incremental with unspecified gains.
The paper tackles the problem of predicting future trajectories of surrounding vehicles for self-driving safety by developing a Convolutional Neural Network approach that learns from raw trajectory data in real-time, showing improvement over baselines.
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.