FLAMD: Assembling Multilevel Feature Learning and Multiscale Motion Decoding for Trajectory Prediction in Autonomous Vehicles

Accurate motion prediction is a cornerstone of safe autonomous driving and a key enabler for building efficient intelligent transportation systems in Internet of Things environments. Mainstream methods typically follow a divided paradigm: first modeling scene dependencies and then generating trajectories in a one-shot manner. Despite their promising performance, persistent challenges still exist, especially regarding insufficient future representation learning due to constrained information and suboptimal motion decoding from overlooking varying temporal correlations. In this paper, we propose FLAMD, a novel trajectory prediction framework that integrates multi-level feature learning with multi-scale motion decoding. Different from previous methods with decoupled design, FLAMD introduces a joint optimization scheme for multimodal motion representation learning and scene context modeling. Equipped with the carefully designed motion-aware feature interaction module and cross-consistency enhancement mechanism, FLAMD promotes long-range interactions and feature co-evolution between historical observations and anticipated scenarios, guided by diverse future-oriented queries. This mutual interaction enables deeper reasoning about scene dynamics, leading to context-aware and future-guided representations. In the decoding phase, FLAMD proposes to extend traditional single-stage decoding into a multi-scale framework. Multiple horizon-specific decoders operate at different temporal resolutions, each custom-made for the complexity of its assigned prediction window. This structure helps to capture temporal correlations and allocates appropriate feature granularity across varying future steps, thereby improving the capacity of trajectory generation. Extensive experiments on two real-world benchmarks demonstrate the superiority of our approach in generating accurate and reliable predictions in diverse traffic scenarios.