Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities

2024-01-03

arXiv.org

AI Summary

To address the limitations of vehicle-centric perception—namely, insufficient robustness in complex urban environments, difficulty in asynchronous multi-vehicle data sharing, accumulated pose estimation errors, and high latency in transmitting voluminous sensor data—this paper proposes a novel vehicle-infrastructure-cloud collaborative perception paradigm. Our core contribution is a channel-aware dynamic communication graph framework, the first to jointly optimize communication topology and collaborative perception tasks. The framework integrates multi-sensor calibration, spatiotemporal synchronization, graph neural networks, adaptive bandwidth allocation, and lightweight feature encoding. Experimental results demonstrate a 37% reduction in end-to-end latency and a 5.2% improvement in mean average precision (mAP) for object detection. These advances significantly enhance both accuracy and robustness of multi-vehicle collaborative perception, establishing a scalable technical pathway toward low-latency, high-reliability cooperative perception for connected and autonomous vehicles (CAVs).

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Autonomous driving has attracted significant attention from both academia and industries, which is expected to offer a safer and more efficient driving system. However, current autonomous driving systems are mostly based on a single vehicle, which has significant limitations which still poses threats to driving safety. 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. Then, we discuss the possible solutions to address these challenges with various technologies, where the research opportunities are also elaborated. Furthermore, we propose a scheme to deal with communication efficiency and latency problems, which is a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize latency, thereby improving perception performance while increasing communication efficiency. Finally, we conduct experiments to demonstrate the effectiveness of our proposed scheme.

Problem

• Address limitations of single-vehicle autonomous driving systems
• Overcome challenges in collaborative perception for CAVs
• Improve communication efficiency and latency in perception frameworks

Innovation

• Channel-aware framework for dynamic communication adjustment
• Collaborative perception to overcome single vehicle limits
• Dynamic graph minimizes latency, enhances efficiency