A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field
This work addresses a critical challenge in automated driving systems by enhancing motion prediction accuracy, which is incremental as it builds on existing learning-based methods with a novel integration.
The paper tackles the problem of trajectory prediction for automated vehicles by proposing a deep neural network that integrates a velocity vector field inspired by fluid dynamics, resulting in improved accuracy for both short and long-term predictions up to 5 seconds and consistent performance with reduced observation windows.
Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning. Unfortunately, contemporary learning-based approaches for motion prediction exhibit significant performance degradation as the prediction horizon increases or the observation window decreases. This paper proposes a novel technique for trajectory prediction that combines a data-driven learning-based method with a velocity vector field (VVF) generated from a nature-inspired concept, i.e., fluid flow dynamics. In this work, the vector field is incorporated as an additional input to a convolutional-recurrent deep neural network to help predict the most likely future trajectories given a sequence of bird's eye view scene representations. The performance of the proposed model is compared with state-of-the-art methods on the HighD dataset demonstrating that the VVF inclusion improves the prediction accuracy for both short and long-term (5~sec) time horizons. It is also shown that the accuracy remains consistent with decreasing observation windows which alleviates the requirement of a long history of past observations for accurate trajectory prediction. Source codes are available at: https://github.com/Amir-Samadi/VVF-TP.