Map-Less Yet Accurate: Trajectory Prediction for Traffic Agents Using Online HD Map Reconstruction for Autonomous Driving Mercedes-Benz Research and Development, Pvt., Limited

Format:

Book

Conference/Event

Author/Creator:

Upreti, Minali, author.

Contributor:

B A, NaveenKumar

Chakraborty, Bodhisattwa

Ghosh, Shankhanil

Girijal, Rahul

Thontepu, Phani

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Accurate trajectory prediction of traffic agents is critical for enabling safer and more reliable autonomous driving, particularly in urban driving scenarios where close-range interactions are most safety critical. High-definition (HD) and standard-definition (SD) maps play a vital role in this process by providing lane topology and directional cues for forecasting agent movements. However, HD maps are expensive and resource-intensive to create, often requiring specialized sensors, while SD maps lack the precision needed for reliable autonomous navigation. To address this, we propose a novel framework for trajectory prediction that leverages online reconstruction of HD maps using vehicle-mounted cameras, offering a scalable and cost-effective alternative. Our method achieves improvements in predicting accuracy, particularly in close-range scenarios, the most crucial for urban driving, while also performing robustly in settings without pre-built maps. Furthermore, we introduce a new safety-aware evaluation metric that incorporates heuristic weights based on agent relevance and distance, enhancing traditional metrics like Brier-minFDE with a stronger focus on safety-critical scenarios. Extensive experiments demonstrate that our approach outperforms state-of-the-art map-less methods, particularly in close-range prediction, while our proposed metric establishes a more domain-relevant benchmark for assessing trajectory prediction in autonomous driving

Notes:

Vendor supplied data

Publisher Number:

2026-26-0039

Access Restriction:

Restricted for use by site license