Inferring Driving Maps by Deep Learning-based Trail Map Extraction

TL;DR

This paper introduces a deep learning-based offline mapping method that incorporates trail data from multiple sources to create accurate, updatable HD maps for autonomous driving, outperforming existing online mapping techniques.

Contribution

The paper presents a novel offline mapping approach using transformer models that integrates trail data for continuous, sensor-agnostic map updates, enhancing generalization and robustness.

Findings

Outperforms state-of-the-art online mapping methods

Demonstrates robustness across different environments and sensors

Enables continuous map updates with a transformer-based model

Abstract

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.