Enhancing predictive performance on long-tail trajectories via clustering and specialized decoders
G. Ganeshaaraj, Tharindu Fernando, Sridha Sridharan, Clinton Fookes · Pattern Recognition · 2025
Accurate forecasting of traffic participants’ future trajectories is crucial for the advancement of autonomous driving systems. Constructing robust models for such tasks requires access to comprehensive datasets that include a variety of diverse cases. Current naturalistic trajectory prediction datasets are often imbalanced, featuring a large number of easier examples and a deficiency of more challenging instances. This long-tail distribution poses a significant challenge, resulting in inadequate model performance on the rare, yet safety-critical, parts of the data. To address this issue, we have proposed a framework that utilizes an embedding-based clustering technique and a distribution-sensitive decoder module to generate precise predictions for tail samples. In addition, the proposed framework includes a trajectory clustering module to refine predictions and improve the model’s capacity to generate multiple plausible future trajectories. Experimental results show that our framework outperforms the state-of-the-art long tail prediction method on tail samples by 19.5% on the Average Displacement error (ADE) and 25.5% on the Final Displacement error (FDE). Additionally, our approach attains state-of-the-art performance in terms of ADE metric on the ETH/UCY datasets, while only slightly trailing Y-Net in terms of the FDE metric. We further conduct ablation studies to highlight the efficacy of each of the proposed innovations. Source codes are available at his GitHub repository