Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

TL;DR

This paper introduces a crowdsourcing-based approach using vehicle trajectories to improve autonomous vehicle mapping and navigation in work zones, addressing limitations of traditional HD maps and SLAM in dynamic environments.

Contribution

The paper proposes a novel method integrating crowdsourced trajectories with GMM to enhance mapping of temporary drivable areas, improving navigation accuracy in work zones.

Findings

Enhanced drivable area detection in work zones

Improved compliance with traffic rules during navigation

Better integration with path planning and control modules

Abstract

Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping include high definition map (HD map) or real-time Simultaneous Localization and Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or embedded maps) and can not adapt well to temporarily changed drivable areas such as work zones. Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information. Difficulties in improving perception accuracy and ensuring the correct interpretation of perception results are challenging to the vehicle in these situations. This paper presents a prototype that introduces crowdsourcing trajectories information into the mapping process to enhance CAV's understanding on the drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to construct the temporarily changed drivable area and occupancy grid map (OGM) based on crowdsourcing trajectories. The proposed method is compared with SLAM without any human driving information. Our method has adapted well with the downstream path planning and vehicle control module, and the CAV did not violate driving rule, which a pure SLAM method did not achieve.