Multi-Horizon Lane Change Maneuver Prediction Using Multi-Modal Transformers

Abstract

Predicting lane change maneuvers is essential for ensuring safe autonomous driving, especially in complex urban environments. Building upon prior multi-modal and graph-based approaches, this work introduces a novel transformer-based architecture for multi-horizon lane change prediction that jointly estimates the lane change maneuver and the lane change phase. The proposed model integrates visual information from surround-view cameras, semantic masks for free space and lane markings, interaction-aware graph representations, and ego-vehicle state signals, within a unified transformer framework to capture spatial-temporal dependencies. In addition, a multi-level uncertainty estimation branch quantifies confidence at the level of modality, fusion, and prediction, to enhance interpretability and reliability. Experiments are conducted on WylonSet++, an extended in-house dataset collected using an instrumented test vehicle, annotated for lane change behavior analysis and maneuver phase transitions. The dataset comprises synchronized front-facing camera images, left and right surround-view camera images, together with vehicle state data. The dataset contains approximately 600 lane change sequences, providing the foundation for this study. Extensive evaluations demonstrate strong performance in anticipating lane change maneuvers and phase progression across short- and long-term prediction horizons in diverse real-world traffic scenarios.