Ego‐lane index‐aware vehicular localisation using the DeepRoad Network for urban environments

Abstract

Abstract Accurate real‐time localisation is crucial for various autonomous driving applications. Numerous methods for localization, combining various kinds of input such as data from environmental sensors, inertial measurement units, and the Global Positioning System, have been proposed. Given the present state of affairs with regards to map compactness and sensor affordability, lane map‐matching‐based methods with cameras used as environmental sensors have been widely proposed. In previous methods, however, lanes detected by cameras could falsely correspond to other lanes in a map. This would result in inaccuracies such as a two‐lane or three‐lane deviation from the actual pose. This paper proposes a localisation method integrating the ego‐lane index to avoid lane deviations during lane map matching. A novel ego‐lane index identification algorithm and methodology to integrate the ego‐lane index information into the lane‐based map‐matching function is presented. It is demonstrated that incorporating ego‐lane index information improves the accuracy of map‐matching localisation. The efficacy of our approach in diverse environments, including complex urban scenarios, was successfully demonstrated.

Keywords

• Mobile radio systems
• Radionavigation and direction finding
• Traffic engineering computing