Learning Implicit Map Representations from Trajectories: An Enhanced Map-Free Framework for Motion Forecasting

With the advancement of autonomous driving technology, trajectory prediction has become a critical task for ensuring traffic safety and intelligent decision-making. Existing motion forecasting models suffer from HD (High-Definition) map dependency, leading to high costs and poor adaptability. Furthermore, their accuracy sharply declines when maps are unavailable, motivating research into map-free alternatives. However, map-free models typically exhibit lower accuracy. To address this issue, we propose a universal enhancement framework that employs trajectory-map contrastive learning, utilizing a trajectory-to-map encoder to extract implicit map representations from raw trajectories, thereby improving performance. Extensive experiments on the Argoverse dataset demonstrate that, after incorporating our trajectory-to-map encoder into map-free models, the average minADE and minFDE are improved by 2.7% and 3.5%, respectively. These results underscore our method's robustness and generalizability in enhancing map-free models, confirming the efficacy of implicit map representation learning and offering a promising solution for HD-map-free autonomous driving in dynamic open-road environments. © 2025 IEEE.