Multi-modal cooperative awareness of connected and automated vehicles in smart cities

Abstract

Cooperative autonomous driving in 5G and smart cities environment is expected to further improve safety, security and efficiency of transportation systems. To this end, involved vehicles is imperative to have accurate knowledge of both their own and neighboring vehicles’ location, a task known as cooperative awareness. In this paper, we have formulated two novel distributed localization and tracking schemes, based on Gradient Descent and Extended Kalman Filter algorithms, to cope with erroneous GPS location. Sensor-rich vehicles exploit Vehicle-to-Vehicle communications and a multitude of integrated sensors, like LIDAR and Cameras, to generate and fuse heterogeneous data. Each vehicle interacts only with its own connected neighboring vehicles, formulating individual star topologies. Extensive simulation studies using CARLA autonomous driving simulator, verify the significant reduction of GPS error achieved by the two methods in various experimental conditions. Distributed tracking proves to be much superior than Gradient descent algorithm, both in the case of self (58% reduction of GPS error) and neighboring vehicles location estimation (38% reduction of average GPS error).