AVL

High Definition (HD) Maps have long been favored for their precise depictions of static road elements. However, their accessibility …

Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. …

Vector maps are essential in autonomous driving for tasks like localization and planning, yet their creation and maintenance are notably costly. While recent advances in online vector map generation for autonomous vehicles are promising, current models lack adaptability to different sensor configurations. They tend to overfit to specific sensor poses, leading to decreased performance and higher retraining costs. This limitation hampers their practical use in real-world applications. In response to this challenge, we propose a modular pipeline for vector map generation with improved generalization to sensor configurations. The pipeline leverages probabilistic semantic mapping to generate a bird’s-eye-view (BEV) semantic map as an intermediate representation. This intermediate representation is then converted to a vector map using the MapTRv2 decoder. By adopting a BEV semantic map robust to different sensor configurations, our proposed approach significantly improves the generalization performance. We evaluate the model on datasets that are different from the training set including real-world data collected with our platform with different sensor configurations and show that the model generalizes significantly better than the state-of-the-art methods.