Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications

• URL: http://arxiv.org/abs/2410.06905v2
• Date: Thu, 10 Oct 2024 07:25:25 GMT
• Title: Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications
• Authors: Manuel Hetzel, Hannes Reichert, Konrad Doll, Bernhard Sick,
• Abstract summary: Vehicle systems need reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions. This paper presents a lightweight method to address these requirements, combining Long Short-Term Memory and Mixture Density Networks. We discuss essential requirements for human trajectory prediction in autonomous vehicle applications and demonstrate our method's performance using traffic-related datasets.
• Score: 1.8294777056635267
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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