Title: UIT-Pred: Universal Intermittent Trajectory Predictor for Autonomous Driving
Primary area: applications to robotics, autonomy, planning
Keywords: Trajectory prediction, Autonomous Driving, Attention, Mamba, Intermittent Data

Abstract: Trajectory prediction is a fundamental component of autonomous driving, requiring models that can handle intermittent observation patterns such as variable-length histories and missing data. Existing state-of-the-art methods, however, often assume fixed-length trajectories and complete input, which challenges their applicability in real-world scenarios where sensor occlusions, communication delays, and temporal sparsity are common. Moreover, conventional approaches typically address tasks such as trajectory prediction, variable-length modeling, or missing data handling in isolation, making them less effective in multi-task settings that naturally arise in practice. To address these challenges, we propose Universal Intermittent Trajectory Predictor (UIT-Pred) that processes inputs with the time index features, which capture temporal variations to effectively adapt to diverse input patterns within the domain. Particularly, We extend recent State Space Models (SSMs) by introducing the Bidirectional Time Decay Mamba (BTD-Mamba), designed to capture dependencies both forward and backward along the sequence. By integrating a decay process, BTD-Mamba effectively analyzes trajectories while maintaining relationships under intermittent observation. Furthermore, the proposed prediction module employs state encoding to capture the underlying motion patterns in the input data and models a multimodal trajectory distribution to account for uncertainty in future predictions. These components are fused through a unified fusion module, enabling the model to jointly reason over observed dynamics and potential future behaviors. Extensive experiments on Argoverse 1 and Argoverse 2 datasets validate the effectiveness of the proposed model. By simultaneously handling prediction, variable-length observations, and missing inputs within a universal architecture, the framework proposes to meet the challenges of real-world autonomous driving systems.