ITSC 2025 Paper Abstract

Paper TH-EA-T26.4

Song, Zeye (Beijing Institute of Technology), Zhu, Yuanchen (Beijing institute of technology), Luo, Xiaoyang (Beijing Institute of Technology), Wang, Yong (Univiersity of Hong Kong), Zhao, Yanan (Beijing Institute of Technology), Tan, Huachun (Beijing Institute of Technology)

Map-Free Trajectory Prediction Via Dual-Path Spatial-Temporal Network with Mamba

Scheduled for presentation during the Regular Session "S26b-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (TH-EA-T26), Thursday, November 20, 2025, 14:30−14:50, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Deep Learning for Scene Understanding and Semantic Segmentation in Autonomous Vehicles, AI, Machine Learning for Real-time Traffic Flow Prediction and Management

Abstract

Trajectory prediction is essential for autonomous driving, enabling vehicles to anticipate the future motions of surrounding agents. However, most existing approaches rely on high-definition (HD) maps, which are expensive to obtain and often unavailable in fast-changing regions. To overcome this limitation, we propose DSTM (Dual-path Spatial-Temporal Network with Mamba), a map-free prediction framework that separately models agent dynamics and inter-agent relational evolution. DSTM employs a dual-path encoder: the Temporal Motion Branch leverages Mamba’s efficient long-range sequence modeling and attention mechanisms to capture individual motion behaviors and spatial context, while the Spatial-Temporal Relation Branch encodes geometric relations and learns their evolution patterns using Temporal Evolution Mamba. Subsequently, the agent-to-agent interaction is inferred with graph neural network, and the fused features are decoded into multimodal trajectories. Experiments on the Argoverse and INTERACTION datasets demonstrate that DSTM outperforms existing map-free baselines, reducing minADE, minFDE, and miss rate by 16.3%, 19.7%, and 24.4%, respectively, compared to CRAT-Pred on Argoverse, and remains competitive with mapbased models. Compared to a Transformer-based variant under the same dual-path architecture, DSTM reduces computational cost by 36% MACs and 37% parameters without sacrificing prediction accuracy.