Traffic Scene Representation and Encoding With Graph Structure Learning and Exploration
Xiaoyu Mo, Baichuan Lou, Zhiqi Mao, Qihang Huang, Weigao Sun, Yafei Wang, Chen Lv · IEEE Transactions on Intelligent Transportation Systems · 2025
Effective scene representation is critical for trajectory generation tasks in autonomous driving. Existing attention-based methods often rely on fixed-length input elements, limiting their ability to adapt to dynamic traffic environments, and frequently overlook lane connectivity. Methods that consider lane connectivity often segment lanes into smaller pieces, which consequently creates a more complex graph structure with an increased number of nodes and edges. Additionally, many current approaches select agent interactions based on fixed distance thresholds, which may miss important long-range or indirect interactions and ignore the fact that proximity does not indicate interaction. In this paper, we propose SceneGNN, a novel framework for learning traffic scenario representation through interaction graph learning and lane graph exploration. SceneGNN constructs a single heterogeneous graph that integrates both agents and lanes, leveraging high-definition maps without over-segmentation. To model lane connectivity more effectively, we introduce LaneGNN, which combines an Omnidirectional Lane Aggregator (OLA) and Directional Lane Explorer (DLE) to explore lane-to-lane interdependencies. Additionally, instead of relying on proximity-based heuristics, our interaction graph learner dynamically constructs inter-agent edges based on learned features, allowing the model to capture meaningful interactions beyond mere distance. We evaluate SceneGNN on the Waymo Open Motion Dataset, where it achieves competitive performance. Our results demonstrate that by efficiently capturing both agent-lane relationships and inter-agent interactions, SceneGNN improves the accuracy and scalability of multi-agent trajectory prediction for real-world autonomous driving applications.