Enhancing Connected Cooperative ADAS: Deep Learning Perception in an Embedded System Utilizing Fisheye Cameras

This paper explores the potential of Cooperative Advanced Driver Assistance Systems (C-ADAS) that leverage Vehicle-to-Everything (V2X) communication to enhance road safety. The authors propose a deep learning based perception system, on a 360 \(^\circ \) surround view within the C-ADAS. This system also utilizes an On-Board Unit (OBU) for V2X message sharing to cater to vehicles lacking their own perception sensors. The feasibility of these systems is demonstrated, showcasing their effectiveness in various real-world scenarios, executed in real-time. The contributions include the introduction of a design for a perception system employing fish-eye cameras in the context of C-ADAS, with the potential for embedded integration, the validation of the feasibility of day 2 services in C-ITS, and the expansion of ADAS functions through Local Dynamic Map (LDM) for Collision Warning Application. The findings highlight the promising potential of C-ADAS in improving road safety and pave the way for future advancements in cooperative perception and driving systems.