Core Viewpoint - The article discusses the limitations of high-definition maps in autonomous driving and introduces BEVTraj, a new trajectory prediction framework that operates without relying on maps, achieving performance comparable to state-of-the-art (SOTA) models based on high-definition maps [1][3][26]. Group 1: Background and Challenges - High-definition maps provide structured information that enhances prediction accuracy but have significant drawbacks, including high costs, limited coverage, and inability to adapt to dynamic changes like road construction or accidents [3]. - The reliance on high-definition maps is a major bottleneck for the large-scale deployment of autonomous driving technology [3]. Group 2: Solutions Explored - Two main paths have been explored to address the challenges: online mapping, which still depends on a mapping module, and a map-free approach that utilizes raw sensor data for predictions [4][6]. - BEVTraj represents the latter approach, leveraging raw sensor data to extract sufficient geometric and semantic information for accurate trajectory predictions [4]. Group 3: BEVTraj Framework - BEVTraj operates in a unified bird's-eye view (BEV) space, consisting of a scene context encoder and an iterative deformable decoder [7]. - The scene context encoder extracts rich scene features from multi-modal sensor data and vehicle historical trajectories, generating a dense BEV feature map [11]. - A key innovation is the deformable attention mechanism, which focuses on a small number of critical sampling points in the BEV feature map, enhancing computational efficiency [11]. Group 4: Iterative Refinement and Prediction - The iterative deformable decoder generates final multi-modal trajectory predictions using the deformable attention mechanism and a sparse goal candidate proposal module [13]. - The sparse goal candidate proposal (SGCP) module predicts a limited number of high-quality candidate points based on vehicle dynamics and scene context, streamlining the prediction process [13][14]. Group 5: Experimental Results - BEVTraj's performance is competitive with SOTA models, demonstrating its effectiveness in generating reasonable trajectories even in complex scenarios like sharp turns and intersections [17][20]. - The results indicate that BEVTraj can learn implicit geometric constraints from raw sensor data, achieving a minimum Average Displacement Error (minADE) of 1.4556 and a minimum Final Displacement Error (minFDE) of 8.4384 [18]. Group 6: Summary and Value - BEVTraj marks a milestone in the field of autonomous driving trajectory prediction by validating the feasibility of map-free solutions and enhancing system flexibility and scalability [21][26]. - The framework's efficient end-to-end architecture, utilizing deformable attention and sparse proposals, provides a valuable design paradigm for the industry [26]. - The open-source code will significantly promote research in map-free perception and prediction within the community [26].

作者 | 我爱计算机视觉 来源 | 我爱计算机视觉 原文链接： https://zhuanlan.zhihu.com/p/1950969540805656985 在自动驾驶技术中，准确预测道路上其他车辆和行人的未来轨迹，是保障行车安全、实现高效导航的核心 环节。当前，最先进的轨迹预测方法大多严重依赖于高精地图（HD Map），利用其提供的车道线、路口拓 扑等精细信息作为强大的先验知识。然而，高精地图的制作和维护成本高昂，覆盖范围有限，且无法应对 临时的道路施工或交通事故等动态变化，这极大地限制了自动驾驶技术的规模化应用。 为了摆脱对高精地图的依赖，来自韩国国民大学的研究团队提出了一个名为 BEVTraj 的全新轨迹预测框 架。该方法完全无需任何地图，直接在BEV空间中处理实时的原始传感器数据，实现了端到端的轨迹预 测。令人瞩目的是，实验证明，BEVTraj的性能足以媲美基于高精地图的SOTA模型，为构建更灵活、更具 扩展性的自动驾驶系统开辟了新道路。 研究背景：高精地图是"蜜糖"还是"砒霜"？ 高精地图为轨迹预测提供了丰富的结构化信息，无疑是提升预测精度的"蜜糖"。但它的"保质期"太短（无 法实时更新），"售 ...