Inferring Driving Maps by Deep Learning-based Trail Map Extraction
This addresses the challenge of efficient and robust map creation for autonomous driving systems, though it appears incremental as it builds on existing offline mapping with a novel data integration twist.
The paper tackles the problem of automating high-definition map creation for autonomous driving by proposing an offline mapping approach that integrates trail data from multiple vehicles using transformer models. The method outperforms state-of-the-art online mapping approaches, achieving improved generalization to unseen environments and sensor configurations.
High-definition (HD) maps offer extensive and accurate environmental information about the driving scene, making them a crucial and essential element for planning within autonomous driving systems. To avoid extensive efforts from manual labeling, methods for automating the map creation have emerged. Recent trends have moved from offline mapping to online mapping, ensuring availability and actuality of the utilized maps. While the performance has increased in recent years, online mapping still faces challenges regarding temporal consistency, sensor occlusion, runtime, and generalization. We propose a novel offline mapping approach that integrates trails - informal routes used by drivers - into the map creation process. Our method aggregates trail data from the ego vehicle and other traffic participants to construct a comprehensive global map using transformer-based deep learning models. Unlike traditional offline mapping, our approach enables continuous updates while remaining sensor-agnostic, facilitating efficient data transfer. Our method demonstrates superior performance compared to state-of-the-art online mapping approaches, achieving improved generalization to previously unseen environments and sensor configurations. We validate our approach on two benchmark datasets, highlighting its robustness and applicability in autonomous driving systems.