预研方面，我们看到了很多优秀的算法工作，其中不少自动驾驶之心都首发报道过。在这些已经公开的工作 中，有几位值得大家留意：R en Liu，Yao Yuhan，Sun Hao，Z hang frank， Jiang Anqing，Z hang Youjian 等 等。 整体上来看，博世在自驾以下几个方向投入较大： 此外还有一些闭环仿真方面的工作D GS（NeurIPS 2025）和视觉基础模型DINO-R1等。作为一家近140年的 老牌企业，博世的工程师文化非常浓厚。柱哥有幸和博世的几位技术专家交流过，更能切身感受到他们务实的 精神。相比去年，博世可谓成果颇丰，大方向上博世跟上了前沿的脚步并开始打造自己的特色。 本文精选了 博世汽车业务近期的优秀工作，为大家一窥其最新的研究图景。 PS. 推荐阅读 小米智驾正在迎头赶上 从地平线自动驾驶2025年的工作，我们看到了HSD的野心 2025年的理想还在不断突破，年度成果一览 作为国际Tier1巨头的博世，今年也被国内智驾的飞速发展卷到了。 根据最新的信息，博世汽车电子猛抓预研 和量产两条线。量产方面博世投入更多的资源落地一段式端到端，近期也招聘到不少技术专家加入。自 ...

Core Insights - The article introduces FlowDrive, a novel end-to-end driving framework that integrates energy-based flow field representation, adaptive anchor trajectory optimization, and motion-decoupled trajectory generation to enhance safety and interpretability in autonomous driving [4][45]. Group 1: Introduction and Background - End-to-end autonomous driving has gained attention for its potential to simplify traditional modular pipelines and leverage large-scale data for joint learning of perception, prediction, and planning tasks [4]. - A mainstream research direction involves generating Bird's Eye View (BEV) representations from multi-view camera inputs, which provide structured spatial views beneficial for downstream planning tasks [4][6]. Group 2: FlowDrive Framework - FlowDrive introduces energy-based flow fields in the BEV space to explicitly model geometric constraints and rule-based semantics, enhancing the effectiveness of BEV representations [7][15]. - The framework includes a flow-aware anchor trajectory optimization module that aligns initial trajectories with safe and goal-oriented areas, improving spatial effectiveness and intention consistency [15][22]. - A task-decoupled diffusion planner separates high-level intention prediction from low-level trajectory denoising, allowing for targeted supervision and flow field conditional decoding [9][27]. Group 3: Experimental Results - Experiments on the NAVSIM v2 benchmark dataset demonstrate that FlowDrive achieves state-of-the-art performance, with an Extended Predictive Driver Model Score (EPDMS) of 86.3, surpassing previous benchmark methods [3][40]. - FlowDrive shows significant advantages in safety-related metrics such as Drivable Area Compliance (DAC) and Time to Collision (TTC), indicating superior adherence to driving constraints and hazard avoidance capabilities [40][41]. - The framework's performance is validated through ablation studies, showing that removing any core component leads to significant declines in overall performance [43][47]. Group 4: Technical Details - The flow field learning module encodes dense, physically interpretable spatial gradients to provide fine-grained guidance for trajectory planning [20][21]. - The perception module utilizes a Transformer-based architecture to effectively fuse multi-modal sensor inputs into a compact and semantically rich BEV representation [18][37]. - The training process involves a composite loss function that supervises trajectory planning, anchor trajectory optimization, flow field modeling, and auxiliary perception tasks [30][31][32][34].