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Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
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Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.
Forward citations
Cited by 13 Pith papers
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Beyond BEV: Optimizing Point-Level Tokens for Collaborative Perception
CoPLOT replaces BEV features with semantically ordered, frequency-enhanced point-level tokens for collaborative perception, improving 3D detection while cutting overhead.
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CoST: Efficient Collaborative Perception From Unified Spatiotemporal Perspective
CoST unifies multi-agent and multi-time fusion into a single spatio-temporal space and transmits only dynamic object features, improving collaborative 3D detection accuracy while reducing bandwidth.
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Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception
A DDQN-based V2X scheduler using a label-free, semantics-aware reward selects which collaborator's BEV features to transmit and outperforms simple baselines in V2X-Sim simulations.
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STAMP: Scalable Task And Model-agnostic Collaborative Perception
STAMP uses lightweight adapter-reverter pairs to translate each agent's BEV features into a shared protocol domain, enabling heterogeneous agents with different sensors, models, and tasks to collaborate without retrai...
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Non-Overlap-Aware Egocentric Pose Estimation for Collaborative Perception in Connected Autonomy
NOPE combines deep graph matching for overlap detection with cross-attention graph learning for egocentric pose estimation, reporting state-of-the-art results on simulation and real-world connected driving data with 2...
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LangCoop: Collaborative Driving with Language
Natural-language messages under 2 KB replace image sharing between two simulated vehicles, cutting bandwidth by about 96% while achieving driving scores up to 48.8 and route completion up to 90.3% in closed-loop CARLA...
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Collaborative Perception Datasets for Autonomous Driving: A Review
A structured survey that catalogs and compares collaborative perception datasets for autonomous driving across cooperation paradigms, sensors, scenarios, and tasks, with a living online repository.
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One is Plenty: A Polymorphic Feature Interpreter for Immutable Heterogeneous Collaborative Perception
PolyInter is a single prompt-conditioned feature interpreter that adapts to new heterogeneous perception agents by fine-tuning only a per-agent prompt, reporting higher AP than PnPDA and MPDA on OPV2V.
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Beyond Line of Sight: Hybrid Validation of V2X Collective Perception in Complex Scenarios
Introduces Bayesian fusion for V2X collective perception with hybrid validation, claiming 260% FOV increase and recall rise from 0.82 to 0.94 in roundabout tests.
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SComCP: Task-Oriented Semantic Communication for Collaborative Perception
SComCP combines importance-aware feature selection with a learned JSCC codec to improve collaborative 3D detection over noisy V2V channels, reporting gains at low SNR.
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Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey
A survey that frames V2X communication as an information sensor and categorizes cooperative perception research into representation, fusion, and scalability.
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SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving
A retrieval-augmented LLM framework that lets a driving model query a database of environmental sensor data reduces reported trajectory prediction error by roughly 70 percent, but the evaluation design inflates the gain.
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Automated Vehicles Should be Connected with Natural Language
A vision paper recommending natural language as the universal communication medium for connected and automated vehicles.
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