Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications

TL;DR

This paper introduces a lightweight, reliable probabilistic trajectory prediction method for autonomous systems, combining LSTM and Mixture Density Networks to provide accurate, uncertainty-aware predictions suitable for real-world applications.

Contribution

It presents a novel, resource-efficient approach that predicts probability distributions with confidence levels, emphasizing reliability and robustness in human trajectory forecasting for autonomous vehicles.

Findings

Effective in multiple traffic datasets

Provides uncertainty estimates for risk management

Runs efficiently on low-power embedded platforms

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.