Leveraging SD mAP to Augment HD mAP-Based Trajectory Prediction

Abstract

Latest trajectory prediction models in real-world autonomous driving systems often rely on online High-Definition (HD) maps to understand the road environment.However, online HD maps suffer from perception errors and feature redundancy, which hinder the performance of HD map-based trajectory prediction models.To address these issues, 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.