V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges
- URL: http://arxiv.org/abs/2310.03525v3
- Date: Thu, 9 May 2024 11:49:43 GMT
- Title: V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges
- Authors: Tao Huang, Jianan Liu, Xi Zhou, Dinh C. Nguyen, Mostafa Rahimi Azghadi, Yuxuan Xia, Qing-Long Han, Sumei Sun,
- Abstract summary: Cooperative Perception with Vehicle-to-Everything (V2X) has emerged as a solution to overcome obstacles and enhance driving automation systems.
This paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments.
An extensive literature review is conducted within this taxonomy, evaluating existing datasets and simulators.
- Score: 32.11627955649814
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Accurate perception is essential for advancing autonomous driving and addressing safety challenges in modern transportation systems. Despite significant advancements in computer vision for object recognition, current perception methods still face difficulties in complex real-world traffic environments. Challenges such as physical occlusion and limited sensor field of view persist for individual vehicle systems. Cooperative Perception (CP) with Vehicle-to-Everything (V2X) technologies has emerged as a solution to overcome these obstacles and enhance driving automation systems. While some research has explored CP's fundamental architecture and critical components, there remains a lack of comprehensive summaries of the latest innovations, particularly in the context of V2X communication technologies. To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies. Additionally, a contemporary generic framework is also proposed to illustrate the V2X-based CP workflow, aiding in the structured understanding of CP system components. Furthermore, this paper categorizes prevailing V2X-based CP methodologies based on the critical issues they address. An extensive literature review is conducted within this taxonomy, evaluating existing datasets and simulators. Finally, open challenges and future directions in CP for autonomous driving are discussed by considering both perception and V2X communication advancements.
Related papers
- Unified End-to-End V2X Cooperative Autonomous Driving [21.631099800753795]
UniE2EV2X is a V2X-integrated end-to-end autonomous driving system that consolidates key driving modules within a unified network.
The framework employs a deformable attention-based data fusion strategy, effectively facilitating cooperation between vehicles and infrastructure.
We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving.
arXiv Detail & Related papers (2024-05-07T03:01:40Z) - Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A
Benchmarking Study [39.214784277182304]
This paper addresses the vertical handover problem in V2X using Deep Reinforcement Learning (DRL) algorithms.
The benchmarked algorithms outperform the current state-of-the-art approaches in terms of redundancy and usage rate of V-VLC headlights.
arXiv Detail & Related papers (2023-10-04T12:32:14Z) - Towards Vehicle-to-everything Autonomous Driving: A Survey on
Collaborative Perception [40.90789787242417]
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems.
Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception.
We provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community.
arXiv Detail & Related papers (2023-08-31T13:28:32Z) - Convergence of Communications, Control, and Machine Learning for Secure
and Autonomous Vehicle Navigation [78.60496411542549]
Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations.
This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
arXiv Detail & Related papers (2023-07-05T21:38:36Z) - Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts,
Datasets and Metrics [77.34726150561087]
This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles.
Concepts and characteristics related to both sensors, as well as to their fusion, are presented.
We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
arXiv Detail & Related papers (2023-03-08T00:48:32Z) - Exploring Contextual Representation and Multi-Modality for End-to-End
Autonomous Driving [58.879758550901364]
Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context.
We introduce a framework that integrates three cameras to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation.
Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset.
arXiv Detail & Related papers (2022-10-13T05:56:20Z) - A Survey and Framework of Cooperative Perception: From Heterogeneous
Singleton to Hierarchical Cooperation [14.525705886707089]
This paper reviews the research progress on Cooperative Perception (CP) and proposes a unified CP framework.
CP is born to unlock the bottleneck of perception for driving automation.
A Hierarchical CP framework is proposed, followed by a review of existing datasets and Simulators to sketch an overall landscape of CP.
arXiv Detail & Related papers (2022-08-22T20:47:35Z) - V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision
Transformer [58.71845618090022]
We build a holistic attention model, namely V2X-ViT, to fuse information across on-road agents.
V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention.
To validate our approach, we create a large-scale V2X perception dataset.
arXiv Detail & Related papers (2022-03-20T20:18:25Z) - V2X-Sim: A Virtual Collaborative Perception Dataset for Autonomous
Driving [26.961213523096948]
Vehicle-to-everything (V2X) denotes the collaboration between a vehicle and any entity in its surrounding.
We present the V2X-Sim dataset, the first public large-scale collaborative perception dataset in autonomous driving.
arXiv Detail & Related papers (2022-02-17T05:14:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.