Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges (2301.06262v4)

Abstract: Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This work reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlook challenges between current academic research and real-world applications. The project page is https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Driving

• The paper presents a comprehensive review of collaborative perception methods, showing how multi-agent sensor fusion mitigates occlusion and sensor failure issues.
• The review categorizes perception schemes into early, intermediate, and late collaboration, with intermediate collaboration balancing bandwidth use and perceptual accuracy.
• The study highlights the importance of large-scale datasets like V2X-Sim and OPV2V for benchmarking and outlines future challenges such as communication latency and privacy-preserving strategies.

Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

The paper "Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges" comprehensively reviews recent advances in the field of collaborative perception for autonomous driving, emphasizing the significance of addressing occlusion and sensor failure issues through multi-agent systems. This manuscript provides a meticulous examination of collaborative perception methodologies, the emergence of large-scale datasets, and persistent challenges in integrating collaborative strategies into real-world scenarios.

Collaborative Perception and Its Schemes

Collaborative perception in autonomous driving aims to enhance environmental understanding by leveraging data from multiple agents—vehicles or infrastructure—through communication networks. Addressing the limitations associated with individual perception, such as occlusion and sensing range restrictions, collaborative perception utilizes three primary schemes: early collaboration, intermediate collaboration, and late collaboration. Early collaboration involves the sharing and fusion of raw data at the network input stage, providing a potentially enhanced perception field at the cost of high bandwidth demands. Intermediate collaboration shares processed features, facilitating a balance between transmission efficiency and perceptual improvement through optimized communication mechanisms and feature fusion strategies. Late collaboration aggregates predictions at the network's output stage, favoring minimal bandwidth usage but often sacrificing detailed perceptual accuracy.

Advances in Collaborative Perception Methods

The review systematically categorizes methods developed for ideal collaborative perception scenarios and those addressing real-world application challenges.

1. Ideal Scenarios: For scenarios without practical constraints, methods focus on utilizing advanced feature fusion techniques, including traditional, graph-based, and attention-based strategies. Graph neural networks (GNNs) and attention mechanisms play pivotal roles in capturing complex inter-agent relationships and promoting efficient feature aggregation. The manuscript highlights state-of-the-art methods like V2VNet and DiscoNet for their utilization of GNNs and attention-driven transformations, resulting in notable perceptual improvements.
2. Real-world Challenges: Real-world implementation must tackle issues such as localization errors, communication latency, model discrepancies, and privacy concerns. Innovative approaches like RobustV2VNet and FPV-RCNN propose solutions for pose consistency, while frameworks such as V2X-ViT incorporate delay-aware positional encoding to address temporal misalignment. Privacy-preserving strategies and robust defenses against adversarial attacks further underpin the readiness of collaborative perception systems for practical deployment.

Assessment Using Large-Scale Datasets

The review stresses the importance of large-scale datasets to support collaborative perception research, providing benchmarks for performance evaluation across various perception tasks, including 3D object detection, tracking, and BEV semantic segmentation. Datasets like V2X-Sim, OPV2V, and DAIR-V2X are instrumental in driving forward collaborative perception research, providing essential data for training and testing innovative algorithms.

The comparison of collaborative perception methods demonstrates the superiority of intermediate collaboration under controlled conditions, with methods like CoBEVT yielding enhanced results in complex multi-view settings, thereby supporting the notion that dynamic interaction modeling is crucial for optimal perceptual outcomes.

Future Directions and Challenges

The manuscript outlines several challenges and opportunities that remain, highlighting areas for innovation and refinement. Key challenges include enhancing transmission efficiency, adapting perception systems to complex driving environments, leveraging federated learning for privacy-preserving collaboration, and reducing dependence on extensive labeling through weakly supervised learning techniques. Addressing these challenges will be critical in advancing the deployment and reliability of collaborative perception in autonomous driving.

Conclusion

In summary, the paper provides a robust framework for understanding collaborative perception mechanisms, their application, and associated challenges in autonomous driving. Through thorough examination of methods, datasets, and potential future directions, the review serves as an essential reference for researchers aiming to enhance vehicle perception capabilities, advocating for continued development in this promising field.