Core Insights - The article emphasizes the growing importance of large models (VLM) in the field of intelligent driving, highlighting their potential for practical applications and production [2][4]. Group 1: VLM and VLA - VLM (Vision-Language Model) focuses on foundational capabilities such as detection, question answering, spatial understanding, and reasoning [4]. - VLA (Vision-Language Action) is more action-oriented, aimed at trajectory prediction in autonomous driving, requiring a deep understanding of human-like reasoning and perception [4]. - It is recommended to learn VLM first before expanding to VLA, as VLM can predict trajectories through diffusion models, enhancing action capabilities in uncertain environments [4]. Group 2: Community and Resources - The article invites readers to join a knowledge-sharing community that offers comprehensive resources, including video courses, hardware, and coding materials related to autonomous driving [4]. - The community aims to build a network of professionals in intelligent driving and embodied intelligence, with a target of gathering 10,000 members in three years [4]. Group 3: Technical Directions - The article outlines four cutting-edge technical directions in the industry: Visual Language Models, World Models, Diffusion Models, and End-to-End Autonomous Driving [5]. - It provides links to various resources and papers that cover advancements in these areas, indicating a robust framework for ongoing research and development [6][31]. Group 4: Datasets and Applications - A variety of datasets are mentioned that are crucial for training and evaluating models in autonomous driving, including pedestrian detection, object tracking, and scene understanding [19][20]. - The article discusses the application of language-enhanced systems in autonomous driving, showcasing how natural language processing can improve vehicle navigation and interaction [20][21]. Group 5: Future Trends - The article highlights the potential for large models to significantly impact the future of autonomous driving, particularly in enhancing decision-making and control systems [24][25]. - It suggests that the integration of language models with driving systems could lead to more intuitive and human-like vehicle behavior [24][25].

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 本篇文章针对26届自动驾驶和互联网求职同学，和大家一起聊聊今年招聘大趋势的边和和秋招的进程。欢迎大家 收藏转发~ 大趋势的变化 去年自动驾驶和互联网整体上都不太景气，诸多公司宣布裁员、倒闭。25届的很多同学都遭遇了校招滑铁卢。今 年业内不少公司都缓过来一口气，开始回复大规模招聘（小米、比亚迪、小鹏等等），预计26届的行情整体还是 可以的，有希望追平24届。 还有一点不同，提前批的效力在逐年减弱，甚至可以说名存实亡。除了顶级天才真的能拿到提前批的超级offer， 大部分同学基本上都是在七月底到十一月底这个阶段拿到秋招offer，十一月底到过年是秋招补录阶段。 对于大公司来说，暑期实习真的很重要。广义上的可转正暑期实习从2月底会持续到10月底，即使到了秋招的时 候，也都同步会有可转正实习的hc，因为大厂目前更倾向于招聘实习转正的人，第一是更有实际的工作基础，第 二当然也是更好压价，实习转正的薪酬一般都不会开的太高。 请大家一定充分认识到暑期实习的重要性，有条件的同学尽可能的争取到一份暑期实习，联系下毕业的学长学姐 ...

Core Viewpoint - The paper explores the relationship between reasoning capabilities and hallucinations in multimodal reasoning models, questioning whether increased reasoning leads to decreased visual perception accuracy [2][3][37]. Group 1: Reasoning Models and Hallucinations - Multimodal reasoning models exhibit a tendency to amplify hallucinations as their reasoning capabilities improve, leading to potential misinterpretations of visual data [2][3][5]. - The study introduces a new metric, RH-AUC, to assess the balance between reasoning length and perception accuracy, indicating that longer reasoning chains may lead to increased hallucinations [4][30]. Group 2: Attention Mechanism and Performance - The attention mechanism in reasoning models shows a significant drop in focus on visual elements, leading to a reliance on language-based assumptions rather than visual evidence [5][18]. - Experiments reveal that reasoning models perform poorly on perception tasks compared to non-reasoning models, indicating that hallucination rates are higher in reasoning models regardless of their size [8][37]. Group 3: Training Paradigms and Data Quality - The paper identifies two main training paradigms: pure reinforcement learning (RL-only) and supervised fine-tuning combined with reinforcement learning (SFT+RL), with RL-only models generally performing better in balancing reasoning and perception [10][35]. - Data quality is emphasized over quantity, suggesting that models trained on high-quality, domain-specific data perform better in maintaining the reasoning-hallucination balance [39][42]. Group 4: Evaluation Metrics and Future Directions - The RH-Bench benchmark is introduced, consisting of 1000 multimodal tasks to evaluate models' reasoning and perception capabilities comprehensively [30][32]. - Future research directions include exploring broader model architectures and developing mechanisms for dynamically adjusting reasoning lengths to enhance model reliability [44].

以下文章来源于3D视觉之心 ，作者3D视觉之心 3D视觉之心 . 3D视觉与SLAM、点云相关内容分享 点击下方 卡片 ，关注" 3D视觉之心 "公众号 第一时间获取 3D视觉干货 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 大规模场景生成的挑战 近年来，生成式人工智能的进步对自动驾驶产生了深远影响，其中扩散模型成为数据合成和驾驶仿真的关键工具。 一些方法将扩散模型作为数据生成机器，用于生成高保真的驾驶视频或多模态的合成数据，以增强感知任务，并生 成如车辆插队等关键但罕见的情况，从而丰富规划数据。除此之外，还有一些方法将扩散模型作为世界模型，用于 预测未来的驾驶状态，从而实现端到端的规划和闭环仿真。这些研究主要强调通过时间递归生成长期视频，鼓励扩 散模型输出时序一致的视频序列，以服务于后续任务。 然而，具备空间扩展能力的大规模场景生成仍是一个新兴但尚未被充分研究的方向，其目标是构建可用于任意驾驶 仿真的广阔而沉浸式的三维环境。一些开创性工作已经探索了大规模的三维驾驶场景生成。例如，有的方法利用扩 散 ...

今天自动驾驶之心为大家分享 英伟达、复旦大学 最新的工作！ GTRS：可泛化的 多模式端到端轨迹规划! 如果您有相关工作需要分享，请在文末联系我们！ 自动驾驶课程学习与技术交流群事宜，也欢迎添加小助理微信AIDriver004做进一 步咨询 >>点击进入→ 自动驾驶之心 『端到端自动驾驶』技术交流群 论文作者 | Zhenxin Li等 编辑 | 自动驾驶之心 论文链接：https://arxiv.org/abs/2506.06664 Github：https://github.com/NVlabs/GTRS NVIDIA技术博客：https://blogs.nvidia.com/blog/auto-research-cvpr-2025/?ncid=so-nvsh-677066 CVPR 2025 Autonomous Grand Challenge: https://opendrivelab.com/legacy/challenge2025/index.html 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 端到端自动驾驶挑战赛背景 NAVSIM v2 ...

重磅！预售来啦。面向科研&教学级自动驾驶全栈小车黑武士系列001正式开售了。世界太枯燥了，和我们一起做点有意思的事情吧。 原价36999元，现 在下单赠送3门课程（ 模型部署+点云3D检测+多传感器融合 ），优先锁定的安排组装发货。 这两个月订单排满了，正在不断组装调试，5台及以上订单可以优惠哦！欢迎高校和研究院所批量采购。感兴趣的同学可以早点下单哦～ 1）黑武士001 我们测试了室内、室外、地库等场景下感知、定位、融合、导航规划等功能； 整体功能介绍 本科生学习进阶+比赛；√ 研究生科研+发论文；√ 研究生找工作+项目；√ 高校实验室教具；√ 培训公司/职业院校教具；√ 户外公园行驶 点云3D目标检测 自动驾驶之心团队推出的教研一体轻量级解决方案，支持感知、定位、融合、导航、规划等多个功能平台，阿克曼底盘。 黑武士支持二次开发和改装，预留了众多安装位置和接口，可以加装相机、毫米波雷达等传感器； 2）效果展示 室内地库2D激光建图 室内地库3D激光建图 上下坡测试 室外大场景3D建图 室外夜间行驶 3）硬件说明 | 主要传感器 | 传感器说明 | | --- | --- | | 3D激光雷达 | Mid 36 ...

Core Viewpoint - The article discusses the Vision-Language-Action (VLA) model, which integrates visual perception, language understanding, and action decision-making into a unified framework for autonomous driving, enhancing system generalization and adaptability [2][4][12]. Summary by Sections Introduction to VLA - VLA stands for Vision-Language-Action, aiming to unify the processes of environmental observation and control command output in autonomous driving [2]. - The model represents a shift from traditional modular approaches to an end-to-end system driven by large-scale data [2][4]. Technical Framework of VLA - The VLA model consists of four key components: 1. Visual Encoder: Extracts features from images and point cloud data [8]. 2. Language Encoder: Utilizes pre-trained language models to understand navigation instructions and traffic rules [11]. 3. Cross-Modal Fusion Layer: Aligns and integrates visual and language features for unified environmental understanding [11]. 4. Action Decoder: Generates control commands based on the fused multi-modal representation [8][11]. Advantages of VLA - VLA enhances scene generalization and contextual reasoning, allowing for quicker and more reasonable decision-making in complex scenarios [12]. - The integration of language understanding allows for more flexible driving strategies and improved human-vehicle interaction [12]. Industry Applications - Various companies, including DeepMind and Yuanrong Qixing, are applying VLA concepts in their autonomous driving research, showcasing its potential in real-world applications [13]. - The RT-2 model by DeepMind and the "end-to-end 2.0 version" by Yuanrong Qixing highlight the advancements in intelligent driving systems [13]. Challenges and Future Directions - Despite its advantages, VLA faces challenges such as lack of interpretability, high data quality requirements, and significant computational resource demands [13][15]. - Solutions being explored include integrating interpretability modules, optimizing trajectory generation, and combining VLA with traditional control methods to enhance safety and robustness [15][16]. - The future of VLA in autonomous driving looks promising, with expectations of becoming a foundational technology as advancements in large models and edge computing continue [16].

本文提出了Challenger框架，首次实现了物理合理且视觉逼真的对抗性驾驶视频生成。其突破性在于通过两项 关键技术解决了轨迹空间优化与高保真传感器数据生成的联合挑战： 该框架在nuScenes数据集上生成多样化对抗场景（如强行切入、盲区超车、跟车过近），并利用 MagicDriveDiT渲染器输出多视角逼真视频。实验表明，所生成场景显著提升主流端到端自动驾驶模型（如 UniAD、VAD）的碰撞率（最高达26倍），且发现的对抗行为具有跨模型可迁移性，揭示了自动驾驶系统的共 性脆弱性。 自 动 驾 驶 之 心 论论论论论论论论文文文文文文文文文文文文文文文文文文辅辅辅辅辅辅辅辅导导导导导导导导来来来来来来来来啦啦啦啦啦啦啦啦 知知知知知知知知识识识识识识识识星星星星星星星星球球球球球球球球交交交交交交交交流流流流流流流流社社社社社社社社区区区区区区区区 >>直播和内容获取转到 → 自动驾驶之心知识星球 近4000人的交流社区，近300+自动驾驶公司与科研结构加入！ 涉及30+自动驾驶技术栈学习路线，从0到一 带你入门 自动驾驶感知（大模型、端到端自动驾驶 、 世界模型 、 仿真闭环 、 3D检测 、 车道线 、 ...