Enhancing Connected Cooperative ADAS: Deep Learning Perception in an Embedded System Utilizing Fisheye Cameras
Authors: Guillem Delgado Gonzalo Cristina Pérez David Pujol Alejandro Miranda Iu Aguilar Aleksandar Jevtic
Date: 25.02.2024
Abstract
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∘ 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.
BIB_text
title = {Enhancing Connected Cooperative ADAS: Deep Learning Perception in an Embedded System Utilizing Fisheye Cameras},
pages = {82-99},
keywds = {
Cooperative advanced driver assistance systems; Deep learning; V2X
}
abstract = {
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∘ 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.
}
isbn = {978-303159056-6},
date = {2024-02-25},
}
