SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning

2026-07-01

AI Summary

This work addresses the challenge of achieving scalable and robust ego-vehicle trajectory prediction without reliance on high-definition maps. To this end, we propose an end-to-end system that integrates front-view images, vehicle kinematics, and navigation paths derived from standard-definition (SD) maps. We introduce SD map paths as a semantic prior for trajectory prediction for the first time, and design a dual-hypothesis fusion architecture with a gated classifier to handle challenges such as route corruption or visual ambiguity. Evaluated on 480,000 real-world driving scenarios spanning ten European countries and the United States, our method reduces the average displacement error (ADE) over an 8-second horizon by 16.9% compared to a baseline using only images and kinematics. We also release an open-source toolkit for SD path generation to support community benchmarking.

Abstract

This paper presents SD-RouteFusion, a deployable end-to-end ego-trajectory prediction method that fuses a front-facing camera, vehicle kinematics, and a navigation route derived from a Standard Definition (SD) map. Unlike approaches that rely on High Definition (HD) map geometry, SD-RouteFusion aligns the learning objective with scalable and production-ready SD-map route inputs, enabling route-aware prediction without requiring HD-map infrastructure. First, we demonstrate that SD-map route prior provides a powerful long-horizon semantic prior. Through a comprehensive study on a large-scale real-world dataset comprising 480k driving scenarios across 10 European countries and the U.S., we quantify the value of SD-route conditioning: incorporating SD-map routes yields a 10.5% ADE improvement over an image-and-kinematics baseline, while our full fusion strategy achieves a 16.9% ADE reduction given a prediction horizon of 8 seconds. The fusion strategy consists of a dual-hypothesis design paired with a gated classifier, to ensure robustness under route corruption and visual uncertainty. Finally, to support broader evaluation, we release an SD-route generation toolkit that enables SD-route-conditioned ego-trajectory prediction on all datasets containing ego pose and future trajectories. Together, SD-RouteFusion establishes a practical path toward robust, route-aware ego-trajectory prediction at scale.

Problem

• ego-trajectory prediction
• SD-map
• route conditioning
• scalable deployment
• autonomous driving

Innovation

• SD-RouteFusion
• ego-trajectory prediction
• SD-map route conditioning
• route-aware prediction
• deployable fusion