R-RNet: Probability-Driven Networks for Pedestrian Trajectory Prediction

Accurate prediction of pedestrian trajectories is crucial for safe motion planning of autonomous vehicles in urban environments. Many existing temporal and generative models are constrained by the long-tailed distribution of the data, which limits their ability to handle random or irregular pedestrian movements. Moreover, few studies have addressed the problem of scoring and probabilistic evaluation of predicted trajectories, despite their importance for downstream decision-making tasks. To address these issues, we propose a regularization-randomization network (R-RNet). The core regularization-randomization (R-R) module enables flexible trajectory prediction across diverse scenarios, while the probability predictor provides trajectory scoring and probability estimation to enhance reliability and utility in subsequent tasks. Besides, a self-attention mechanism is utilized to enhance prediction performance by capturing features from the distribution of the goals. The experimental results on the ETH and the UCY datasets show that R-RNet is capable of making reliable evaluations on output trajectories and achieves competitive results in terms of average displacement error and miss rate, while maintaining a lightweight architecture. Extensive experiments and analyses underscore the critical importance of both regularization and randomization operations.