Collaborative Perception for Autonomous Driving: Current Status and
Future Trend
- URL: http://arxiv.org/abs/2208.10371v1
- Date: Mon, 22 Aug 2022 14:51:29 GMT
- Title: Collaborative Perception for Autonomous Driving: Current Status and
Future Trend
- Authors: Shunli Ren, Siheng Chen, Wenjun Zhang
- Abstract summary: Collaborative perception has been proposed which enables vehicles to share information to perceive the environments beyond line-of-sight and field-of-view.
This paper introduces the fundamental concepts, generalizing the collaboration modes and summarizing the key ingredients and applications of collaborative perception.
- Score: 33.6716877086539
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Perception is one of the crucial module of the autonomous driving system,
which has made great progress recently. However, limited ability of individual
vehicles results in the bottleneck of improvement of the perception
performance. To break through the limits of individual perception,
collaborative perception has been proposed which enables vehicles to share
information to perceive the environments beyond line-of-sight and
field-of-view. In this paper, we provide a review of the related work about the
promising collaborative perception technology, including introducing the
fundamental concepts, generalizing the collaboration modes and summarizing the
key ingredients and applications of collaborative perception. Finally, we
discuss the open challenges and issues of this research area and give some
potential further directions.
Related papers
- Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory.
This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
arXiv Detail & Related papers (2024-09-19T14:36:00Z) - Panoptic Perception for Autonomous Driving: A Survey [0.0]
This survey reviews typical panoptic perception models and compares them to performance, responsiveness, and resource utilization.
It also delves into the prevailing challenges faced in panoptic perception and explores potential trajectories for future research.
arXiv Detail & Related papers (2024-08-27T20:14:42Z) - The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition [136.32656319458158]
The 2024 RoboDrive Challenge was crafted to propel the development of driving perception technologies.
This year's challenge consisted of five distinct tracks and attracted 140 registered teams from 93 institutes across 11 countries.
The competition culminated in 15 top-performing solutions.
arXiv Detail & Related papers (2024-05-14T17:59:57Z) - Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models [57.86303579812877]
Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions.
Existing approaches often require numerous human interventions per image to achieve strong performances.
We introduce a trainable concept realignment intervention module, which leverages concept relations to realign concept assignments post-intervention.
arXiv Detail & Related papers (2024-05-02T17:59:01Z) - Evaluating Roadside Perception for Autonomous Vehicles: Insights from
Field Testing [7.755003755937953]
This paper introduces a comprehensive evaluation methodology specifically designed to assess the performance of roadside perception systems.
Our methodology encompasses measurement techniques, metric selection, and experimental trial design, all grounded in real-world field testing.
The findings of this study are poised to inform the development of industry-standard benchmarks and evaluation methods.
arXiv Detail & Related papers (2024-01-22T22:47:02Z) - Collaborative Perception for Connected and Autonomous Driving:
Challenges, Possible Solutions and Opportunities [10.749959052350594]
Collaborative perception with connected and autonomous vehicles (CAVs) shows a promising solution to overcoming these limitations.
In this article, we first identify the challenges of collaborative perception, such as data sharing asynchrony, data volume, and pose errors.
We propose a scheme to deal with communication efficiency and latency problems, which is a channel-aware collaborative perception framework.
arXiv Detail & Related papers (2024-01-03T05:33:14Z) - Towards Full-scene Domain Generalization in Multi-agent Collaborative
Bird's Eye View Segmentation for Connected and Autonomous Driving [54.60458503590669]
We propose a unified domain generalization framework applicable in both training and inference stages of collaborative perception.
We employ an Amplitude Augmentation (AmpAug) method to augment low-frequency image variations, broadening the model's ability to learn.
In the inference phase, we introduce an intra-system domain alignment mechanism to reduce or potentially eliminate the domain discrepancy.
arXiv Detail & Related papers (2023-11-28T12:52:49Z) - Collaborative Perception in Autonomous Driving: Methods, Datasets and
Challenges [19.0876933975015]
Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving.
This work reviews recent achievements in this field to bridge this gap and motivate future research.
arXiv Detail & Related papers (2023-01-16T05:08:50Z) - Evaluating Interactive Summarization: an Expansion-Based Framework [97.0077722128397]
We develop an end-to-end evaluation framework for interactive summarization.
Our framework includes a procedure of collecting real user sessions and evaluation measures relying on standards.
All of our solutions are intended to be released publicly as a benchmark.
arXiv Detail & Related papers (2020-09-17T15:48:13Z) - Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks [11.737037965090535]
We introduce the concept of feature sharing for cooperative object detection (FS-COD)
In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles.
It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches.
arXiv Detail & Related papers (2020-02-19T20:47:09Z)
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.