Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective

Abstract

The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, has emerged as a promising solution to mitigate these issues and enhance the reliability of autonomous systems. Beyond advancements in communication, the computer vision community is increasingly focusing on improving vehicular perception through collaborative approaches. However, a systematic literature review that thoroughly examines existing work and reduces subjective bias is still lacking. Such a systematic approach helps identify research gaps, recognize common trends across studies, and inform future research directions. In response, this study follows the PRISMA 2020 guidelines and includes 106 peer-reviewed articles. These publications are analyzed based on modalities, collaboration schemes, and key perception tasks. Through a comparative analysis, this review illustrates how different methods address practical issues such as pose errors, temporal latency, communication constraints, domain shifts, heterogeneity, and adversarial attacks. Furthermore, it critically examines evaluation methodologies, highlighting a misalignment between current metrics and CP's fundamental objectives. By delving into all relevant topics in-depth, this review offers valuable insights into challenges, opportunities, and risks, serving as a reference for advancing research in vehicular collaborative perception.

38 pages, 8 figures, accepted for publication in IEEE Transactions on Intelligent Transportation Systems (T-ITS)

References in corpus (11)

• Analytical Models of the Performance of C-V2X Mode 4 Vehicular Communications
• Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
• Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication
• Generation of Cooperative Perception Messages for Connected and Automated Vehicles
• PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR
• Vehicle-to-Everything Cooperative Perception for Autonomous Driving
• Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
• Cooperative Perception with Learning-Based V2V communications
• DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception
• CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection
• Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception