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.

翻译：自动驾驶车辆的有效性依赖于可靠的感知能力。尽管人工智能与传感器融合技术取得了显著进展，当前的单车感知系统仍面临诸多局限，尤其是视觉遮挡与远距离检测能力不足。通过车对车（V2V）与车对基础设施（V2I）通信实现的协同感知（CP），已成为缓解这些问题并提升自动驾驶系统可靠性的重要解决方案。除通信技术进步外，计算机视觉领域正日益聚焦于通过协同方法提升车载感知性能。然而，目前仍缺乏系统性的文献综述来全面审视现有研究并减少主观偏差。此类系统分析方法有助于识别研究空白、把握跨研究的共性趋势，并为未来研究方向提供参考。为此，本研究遵循PRISMA 2020指南，纳入了106篇同行评审文献。这些文献从感知模态、协同架构与核心感知任务三个维度进行了系统性分析。通过对比研究，本文阐述了不同方法如何处理位姿误差、时序延迟、通信约束、域偏移、异构性及对抗攻击等实际问题。此外，本文批判性地审视了现有评估方法，指出当前评价指标与协同感知根本目标之间的错位现象。通过深入剖析所有相关议题，本综述为车载协同感知领域的研究挑战、机遇与风险提供了深刻见解，可为推动该领域研究发展提供重要参考。