Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach, we leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths. This method integrates HD map data to improve the robustness and reliability of trajectory predictions for autonomous vehicles.
@article{arxiv.2407.05811,
title = {MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles},
author = {Sushil Sharma and Arindam Das and Ganesh Sistu and Mark Halton and Ciarán Eising},
journal= {arXiv preprint arXiv:2407.05811},
year = {2024}
}
Comments
This paper is a preprint of a paper submitted to the 26th Irish Machine Vision and Image Processing Conference (IMVIP 2024). If accepted, the copy of record will be available at IET Digital Library