SSTP: Efficient Sample Selection for Trajectory Prediction

Trajectory prediction is a core task in autonomous driving. However, training advanced trajectory prediction models on large-scale datasets is both time-consuming and computationally expensive. In addition, the imbalanced distribution of driving scenarios often biases models toward data-rich cases, limiting performance in safety-critical, data-scarce conditions. To address these challenges, we propose the Sample Selection for Trajectory Prediction (SSTP) framework, which constructs a compact yet balanced dataset for trajectory prediction. SSTP consists of two main stages (1) Extraction, in which a pretrained trajectory prediction model computes gradient vectors for each sample to capture their influence on parameter updates; and (2) Selection, where a submodular function is applied to greedily choose a representative subset that covers diverse driving scenarios. This approach significantly reduces the dataset size and mitigates scenario imbalance, without sacrificing prediction accuracy and even improving in high-density cases. We evaluate our proposed SSTP on the Argoverse 1 and Argoverse 2 benchmarks using a wide range of recent state-of-the-art models. Our experiments demonstrate that SSTP achieves comparable performance to full-dataset training using only half the data while delivering substantial improvements in high-density traffic scenes and significantly reducing training time. Importantly, SSTP exhibits strong generalization and robustness, and the selected subset is model-agnostic, offering a broadly applicable solution.