Experimental Study of Multi-Camera Infrastructure Perception for V2X-Assisted Automated Driving in Highway Merging

Accurate and reliable perception of the surrounding environment, e.g., detection and classification of nearby objects, is the primary and most important function of automated/autonomous vehicles. However, onboard perception systems face challenges in complex road segments due to various environmental effects, such as occlusions, or high sensor noise. A potential enhancement is to equip such environments with cost-effective infrastructures that perceive the environment and provide additional perception support to autonomous vehicles through vehicle-to-everything (V2X) communication technologies. This paper develops an experimental study of vehicle detection and tracking on a bird's eye view (BEV) map using raw video collected from several low-cost roadside monocular cameras with overlapping views installed near a motorway junction to support the merging of autonomous vehicles. The paper explains how to produce vehicle tracks from the camera infrastructure and reports the real-world evaluation of the proposed solution on a physical test bed in the UK's West Midland region.