Leveraging SD Map to Augment HD Map-based Trajectory Prediction

Abstract

Trajectory prediction models in real-world autonomous driving often rely on online High-Definition (HD) maps to understand road environments, but online HD maps suffer from perception errors and feature redundancy, which hinder the performance of these models. To address this issue, we introduce a framework, termed SD map-Augmented Trajectory Prediction (SATP), which leverages Standard-Definition (SD) maps to enhance HD map-based trajectory prediction models. First, we propose an SD-HD fusion approach to leverage SD maps across the diverse range of HD map-based trajectory prediction models. Second, we design a novel AlignNet to align the SD map with the HD map, further improving the effectiveness of SD maps. Experiments on real-world autonomous driving benchmarks demonstrate that SATP not only improves the performance of HD map-based trajectory prediction up to 25% in real-world scenarios using online HD maps but also brings benefits in ideal scenarios with ground-truth HD maps.