苦战七年卷了三代！关于BEV的演进之路：哈工大&清华最新综述

Core Viewpoint - The article discusses the evolution of Bird's Eye View (BEV) perception as a foundational technology for autonomous driving, highlighting its importance in ensuring safety and reliability in complex driving environments [2][4]. Group 1: Essence of BEV Perception - BEV perception is an efficient spatial representation paradigm that projects heterogeneous data from various sensors (like cameras, LiDAR, and radar) into a unified BEV coordinate system, facilitating a consistent structured spatial semantic map [6][12]. - This top-down view significantly reduces the complexity of multi-view and multi-modal data fusion, aiding in the accurate perception and understanding of spatial relationships between objects [6][12]. Group 2: Importance of BEV Perception - With a unified and interpretable spatial representation, BEV perception serves as an ideal foundation for multi-modal fusion and multi-agent collaborative perception in autonomous driving [8][12]. - The integration of heterogeneous sensor data into a common BEV plane allows for seamless alignment and integration, enhancing the efficiency of information sharing between vehicles and infrastructure [8][12]. Group 3: Implementation of BEV Perception - The evolution of safety-oriented BEV perception (SafeBEV) is categorized into three main stages: SafeBEV 1.0 (single-modal vehicle perception), SafeBEV 2.0 (multi-modal vehicle perception), and SafeBEV 3.0 (multi-agent collaborative perception) [12][17]. - Each stage represents advancements in technology and features, addressing the increasing complexity of dynamic traffic scenarios [12][17]. Group 4: SafeBEV 1.0 - Single-Modal Vehicle Perception - This stage utilizes a single sensor (like a camera or LiDAR) for BEV scene understanding, with methods evolving from homography transformations to data-driven BEV modeling [13][19]. - The performance of camera-based methods is sensitive to lighting changes and occlusions, while LiDAR methods face challenges with point cloud sparsity and performance degradation in adverse weather [19][41]. Group 5: SafeBEV 2.0 - Multi-Modal Vehicle Perception - Multi-modal BEV perception integrates data from cameras, LiDAR, and radar to enhance performance and robustness in challenging conditions [42][45]. - Fusion strategies are categorized into five types, including camera-radar, camera-LiDAR, radar-LiDAR, camera-LiDAR-radar, and temporal fusion, each leveraging the complementary characteristics of different sensors [42][45]. Group 6: SafeBEV 3.0 - Multi-Agent Collaborative Perception - The development of Vehicle-to-Everything (V2X) technology enables autonomous vehicles to exchange information and perform joint reasoning, overcoming the limitations of single-agent perception [15][16]. - Collaborative perception aggregates multi-source sensor data in a unified BEV space, facilitating global environmental modeling and enhancing safety navigation in dynamic traffic [15][16]. Group 7: Challenges and Future Directions - The article identifies key challenges in open-world scenarios, such as open-set recognition, large-scale unlabeled data, sensor performance degradation, and communication delays among agents [17]. - Future research directions include the integration of BEV perception with end-to-end autonomous driving systems, embodied intelligence, and large language models [17].