Augmenting Car Surrounding Information by Inter-Vehicle Data Fusion

Abstract

Collecting vehicle surrounding information is a key issue for accident prevention and autonomous driving applications. Although GPS and 4G/LTE are widely accepted. it is still a challenge for a vehicle to get complete information of its surrounding vehicles. In this work, we consider the integration of multi-sensory data through V2V communications to help a vehicle to understand its complex surroundings. We propose a fusion algorithm that can integrate four types of sensory inputs: V2V communications, GPS, camera, and inertial data. We show that through such fusion, it is possible for a vehicle to visually see the driving states of its surrounding vehicles.