Integration of Uncertainty-Aware Map Estimation and BEV Features for Robust Trajectory Prediction in Autonomous Driving

While the substantial cost of constructing and maintaining high-definition (HD) maps motivates the pursuit of online map generation for improved efficiency and cross-domain adaptability, current online maps inevitably suffer from geometric noise, occlusions, and other imperfections. These impacts can drive trajectory predictions beyond feasible boundaries and accumulate over extended horizons, thereby compromising the trustworthiness of downstream modules. The explicit modeling and robust handling of map uncertainty, however, remain largely unexplored. To address this gap, an uncertainty-aware trajectory prediction framework is presented for autonomous driving that integrates bird's-eye-view (BEV) features and quantifies map uncertainty to adaptively balance HD map geometric constraints against BEV evidence. This dual constraint paradigm, marrying uncertainty quantification with geometric priors of map structure, yields robust trajectory prediction in complex, real-world environments. Experimental results demonstrate that our method effectively contends with uncertainty across diverse, challenging settings and markedly improves both the accuracy and reliability of downstream trajectory prediction, with particularly notable improvements in highly interactive scenes and regions characterized by poor map quality.