A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges

• URL: http://arxiv.org/abs/2404.16139v2
• Date: Sun, 28 Apr 2024 15:06:51 GMT
• Title: A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges
• Authors: Melih Yazgan, Thomas Graf, Min Liu, Tobias Fleck, J. Marius Zoellner,
• Abstract summary: This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving. We examine various methods, detailing their features and the evaluation metrics they employ.
• Score: 3.0655531578749513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges. We examine various methods, detailing their features and the evaluation metrics they employ. The focus is on addressing challenges like transmission efficiency, localization errors, communication disruptions, and heterogeneity. Moreover, we explore strategies to counter adversarial attacks and defenses, as well as approaches to adapt to domain shifts. The objective is to present an overview of how intermediate fusion methods effectively meet these diverse challenges, highlighting their role in advancing the field of collaborative perception in autonomous driving.

Related papers

• Cross-view geo-localization: a survey [1.3686993145787065]
  Cross-view geo-localization has garnered notable attention in the realm of computer vision, spurred by the widespread availability of copious geotagged datasets. This paper provides a thorough survey of cutting-edge methodologies, techniques, and associated challenges that are integral to this domain.
  arXiv  Detail & Related papers  (2024-06-14T05:14:54Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• 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)
• Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
  We propose a method for a collaborative system that remains safe by having a human making decisions. Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
  arXiv  Detail & Related papers  (2023-11-13T16:00:16Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception [18.358998861454477]
  Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the performance perception of autonomous vehicles over single-agent perception. We propose SCOPE, a novel collaborative perception framework that aggregates awareness characteristics across agents in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-07-26T03:00:31Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Attention Based Feature Fusion For Multi-Agent Collaborative Perception [4.120288148198388]
  We propose an intermediate collaborative perception solution in the form of a graph attention network (GAT) The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision.
  arXiv  Detail & Related papers  (2023-05-03T12:06:11Z)
• Collaborative Perception for Autonomous Driving: Current Status and Future Trend [33.6716877086539]
  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.
  arXiv  Detail & Related papers  (2022-08-22T14:51:29Z)
• Multi-modal Sensor Fusion for Auto Driving Perception: A Survey [22.734013343067407]
  We provide a literature review of the existing multi-modal-based methods for perception tasks in autonomous driving. We propose an innovative way that divides them into two major classes, four minor classes by a more reasonable taxonomy in the view of the fusion stage.
  arXiv  Detail & Related papers  (2022-02-06T04:18:45Z)
• Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations [126.78199124026398]
  In many real-world imitation learning tasks, the demonstrator and the learner have to act in different but full observation spaces. In this work, we model the above learning problem as Heterogeneous Observations Learning (HOIL) We propose the Importance Weighting with REjection (IWRE) algorithm based on the techniques of importance-weighting, learning with rejection, and active querying to solve the key challenge of occupancy measure matching.
  arXiv  Detail & Related papers  (2021-06-17T05:44:04Z)

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.