Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications
<|reference_start|>Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications: Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Furthermore, they need to know the uncertainty of the predictions for risk assessment to provide safe path planning. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. Our method predicts probability distributions, including confidence level estimations for positional uncertainty to support subsequent risk management applications and runs on a low-power embedded platform. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method's performance using multiple traffic-related datasets. Furthermore, we explain reliability and sharpness metrics and show how important they are to guarantee the correctness and robustness of a model's predictions and uncertainty assessments. These essential evaluations have so far received little attention for no good reason. Our approach focuses entirely on real-world applicability. Verifying prediction uncertainties and a model's reliability are central to autonomous real-world applications. Our framework and code are available at: https://github.com/kav-institute/mdn_trajectory_forecasting.<|reference_end|>
@article{hetzel2024reliable,
    title={Reliable Probabilistic Human Trajectory Prediction for Autonomous
  Applications},
    author={Manuel Hetzel, Hannes Reichert, Konrad Doll and Bernhard Sick},
    journal={arXiv preprint arXiv:2410.06905},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06905},
    primaryClass={cs.CV}
}