Trajectory Prediction for Autonomous Driving Using a Transformer Network
<|reference_start|>Trajectory Prediction for Autonomous Driving Using a Transformer Network: Predicting the trajectories of surrounding agents is still considered one of the most challenging tasks for autonomous driving. In this paper, we introduce a multi-modal trajectory prediction framework based on the transformer network. The semantic maps of each agent are used as inputs to convolutional networks to automatically derive relevant contextual information. A novel auxiliary loss that penalizes unfeasible off-road predictions is also proposed in this study. Experiments on the Lyft l5kit dataset show that the proposed model achieves state-of-the-art performance, substantially improving the accuracy and feasibility of the prediction outcomes.<|reference_end|>
@article{li2024trajectory,
    title={Trajectory Prediction for Autonomous Driving Using a Transformer Network},
    author={Zhenning Li, Hao Yu},
    journal={International Joint Conferences on Artificial Intelligence 2021
  AI4AD},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16501},
    primaryClass={cs.RO}
}