据了解，萝卜快跑是百度Apollo自动驾驶出行服务平台。2022年6月10日，萝卜快跑正式在重庆永川区 投入运营。截至2025年3月，百度"萝卜快跑"自动驾驶出行服务平台，已在永川建立近4000多个站点， 运营面积超130平方公里。 凤凰网科技讯 8月7日，多名网友发布视频称，重庆永川区试运营的"萝卜快跑"无人驾驶网约车在行驶 中坠入市政施工沟槽。视频显示，车辆部分陷入坑内，车内女乘客在工作人员和群众帮助下脱困。截至 目前，萝卜快跑方面尚未就此事作出回应。 ...

[Music] Good evening everyone. So today I'll be talking about learning for a world that does not exist. Of course I'm coming it from the technical point of view.Today we are living in a world which is technically very dynamic. We are producing a lot of techni technological advancement and for young engineers and aspirants of engineering. Uh this can be a very challenging moment because um by the time we finish our education new industries emerge, lots of industries saturate and we need to be prepared by the ...

Core Viewpoint - The article emphasizes the importance of preparing for non-technical interview questions in the autonomous driving industry, highlighting the need for candidates to articulate their interests, communication skills, and learning abilities effectively [6][10][11]. Group 1: Interview Preparation - Candidates should reflect on their interests and experiences to answer open-ended questions, as interviewers are often looking for personal insights and opinions [6][10]. - Communication skills are crucial; candidates should demonstrate their ability to engage with mentors and express their thought processes when seeking guidance [6][10]. - Learning ability is assessed through candidates' approaches to acquiring new technical knowledge, emphasizing the importance of establishing a comprehensive understanding before diving into specifics [7][10]. Group 2: Community and Resources - The "Autonomous Driving Heart Knowledge Planet" community provides a platform for technical exchange, featuring members from renowned universities and leading companies in the autonomous driving sector [23][11]. - The community offers a wealth of resources, including over 40 technical routes and numerous open-source projects, aimed at facilitating learning and career development in the autonomous driving field [11][19]. - Members can access job opportunities and industry insights, fostering a complete ecosystem for autonomous driving professionals [21][22]. Group 3: Learning and Development - The community has curated a comprehensive learning path for beginners and advanced researchers, covering various aspects of autonomous driving technology [17][19]. - Regular discussions and Q&A sessions are held to address common industry challenges and share knowledge on emerging technologies [24][87]. - The platform also features live sessions with industry experts, providing members with direct access to cutting-edge research and practical applications [86][11].

Core Viewpoint - WeRide has received approval for late-night testing of its Robotaxi in Beijing, marking a significant step towards establishing a 24/7 autonomous ride-hailing network in the city [1][6] Group 1: Testing and Technology - The approval allows testing from 10pm to 7am in the Beijing High-Level Autonomous Driving Demonstration Zone, which is crucial for developing all-weather, all-day autonomous mobility services [1][6] - WeRide's Robotaxi is equipped with over 20 sensors, including high-precision cameras and LiDARs, achieving 360-degree coverage with a detection range of up to 200 meters, ensuring stable perception and rapid decision-making in low-light conditions [3][4] - The company has conducted Robotaxi testing or operations in 10 cities across four countries, accumulating over 2,200 days of safe open-road experience [5] Group 2: Challenges and Solutions - Night-time road conditions in Beijing present challenges such as low lighting and environmental interference, which require advanced perception and decision-making capabilities [2] - WeRide addresses visibility issues with a proprietary multi-sensor fusion algorithm and a high-performance computing platform, ensuring effective sensor fusion under adverse conditions [3] - The company employs automotive-grade sensors and a smart sensor cleaning system to maintain reliable perception in extreme weather conditions [4] Group 3: Future Outlook - The launch of 24/7 Robotaxi testing in Beijing validates WeRide's technology and safety systems, enhancing public transportation availability during off-peak hours [6] - WeRide aims to leverage its full-stack autonomous driving technology to expand its ride-hailing services and contribute to smarter, more sustainable urban transportation [7] - The company is recognized as a leader in the autonomous driving industry, being the first publicly traded Robotaxi company and having received permits in six markets [9]

Core Insights - The article discusses advancements in trajectory prediction using a generative active learning framework called GALTraj, which applies controllable diffusion models to address long-tail issues in data [1][2]. Group 1: GALTraj Framework - GALTraj is the first framework to apply generative active learning to trajectory prediction tasks, enhancing long-tail learning without modifying the model structure [2]. - The framework employs a tail-aware generation method that differentiates the diffusion guidance for tail, head, and related agents, producing realistic and diverse scenarios while preserving tail characteristics [2][3]. Group 2: Experimental Results - In experiments on WOMD and Argoverse2 datasets, GALTraj significantly improved long-tail sample prediction performance, reducing the long-tail metric FPR₅ by 47.6% (from 0.42 to 0.22) and overall prediction error minFDE₆ by 14.7% (from 0.654 to 0.558) [1][6]. - The results indicate that GALTraj outperforms traditional methods across various metrics, showcasing its effectiveness in enhancing prediction accuracy for rare scenarios [7][8]. Group 3: TopoLiDM Framework - The article also highlights the TopoLiDM framework developed by Shanghai Jiao Tong University and Twente University, which integrates topology-aware diffusion models for high-fidelity LiDAR point cloud generation [13][15]. - TopoLiDM achieved a 22.6% reduction in the Fréchet Range Image Distance (FRID) and a 9.2% reduction in Minimum Matching Distance (MMD) on the KITTI-360 dataset while maintaining a real-time generation speed of 1.68 samples per second [13][15]. Group 4: FastDriveVLA Framework - FastDriveVLA, developed by Peking University and Xiaopeng Motors, introduces a reconstruction-based visual token pruning framework that maintains 99.1% trajectory accuracy with a 50% pruning rate and reduces collision rates by 2.7% [21][22]. - The framework employs a novel adversarial foreground-background reconstruction strategy to enhance the identification of valuable tokens, achieving state-of-the-art performance on the nuScenes open-loop planning benchmark [27][28]. Group 5: PLA Framework - The article presents a unified Perception-Language-Action (PLA) framework proposed by TUM, which integrates multi-sensor fusion and GPT-4.1 enhanced visual-language-action reasoning for adaptive autonomous driving [34][35]. - The framework demonstrated a mean absolute error (MAE) of 0.39 m/s in speed prediction and an average displacement error (ADE) of 1.013 meters in trajectory tracking within urban intersection scenarios [42].

Core Viewpoint - The article emphasizes the convergence of autonomous driving technology, highlighting the shift from numerous diverse approaches to a more unified model, which indicates higher technical barriers in the industry [1] Group 1 - The industry is moving towards a unified solution with models like one model, VLM, and VLA, suggesting a reduction in the need for numerous algorithm engineers [1] - The article encourages the establishment of a large community to support industry professionals, facilitating growth and collaboration among peers [1] - A new job-related community is being launched to discuss industry trends, company developments, product research, and job opportunities [1]

小马智行表示，运营时段充分满足市民包括早晚高峰在内的日常通勤及休闲出行需求，同时运营线路覆 盖浦东新区人民政府、啦啦宝都购物中心、世纪公园、浦东足球场地铁站等核心商圈、地标场所及热门 地铁站，有效连接区域内工作、生活、休闲三大场景，让自动驾驶技术真正服务于市民的日常出行需 求。 责任编辑：王翔 消息称，小马智行联合锦江出租率先在浦东金桥和花木地区推出了面向公众常态化运营的Robotaxi服 务。每周一至周五早7：30至晚9：30，市民通过"小马智行"手机App或小程序，均可呼叫Robotaxi作为 出行交通工具。运营线路覆盖浦东新区人民政府、啦啦宝都购物中心、世纪公园、浦东足球场地铁站等 核心商圈、地标场所及热门地铁站。 8月4日消息，小马智行宣布，8月1日起，小马智行在上海浦东推出了面向公众常态化运营的Robotaxi服 务。 ...

作者 | 王小迪MLE 编辑 | 自动驾驶之心 原文链接： https://www.zhihu.com/question/279973696/answer/3535722816 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 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 最近end2end风头正盛，BEV已成标准范式，但planning进展仍然焦灼。究其原因，interaction modelling是planning的深水区，涉及博弈和不确定性建模，监督学习仍然 不能很好得描述此类问题。这次报告以interaction的建模和求解为切口，分析了近些年常用的框架范式，比如将ego trajectory和agent trajectory的关系加入loss function或 constraint中，ego/agent trajectory从lane routing或neural network生成等。 - (We already have) Reactive: surrounding agents influenc ...

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Core Viewpoint - The article discusses a new detection framework called Butter, designed to improve target detection in autonomous driving scenarios by addressing the challenges of multi-scale semantic information modeling and enhancing detection robustness and deployment efficiency [3][11]. Group 1: Framework Innovations - Butter introduces two core innovations in the Neck layer: the Frequency Consistency Enhancement Module (FAFCE) and the Progressive Hierarchical Feature Fusion Network (PHFFNet) [3][15]. - FAFCE enhances boundary resolution by integrating high-frequency detail enhancement with low-frequency noise suppression, while PHFFNet progressively fuses semantic information to strengthen multi-scale feature representation [3][15]. Group 2: Performance Metrics - Butter outperforms existing state-of-the-art (SOTA) methods in detection accuracy with significantly lower parameter counts, achieving a mean Average Precision (mAP@50) of 94.4% on the KITTI dataset, surpassing the previous best by 1.2 percentage points while using only about one-third of the computational load [32][34]. - On the BDD100K and Cityscapes datasets, Butter achieved mAP@50 scores of 53.7% and 53.2%, respectively, demonstrating superior performance compared to other lightweight models, particularly with a 1.6 percentage point improvement on Cityscapes [32][34]. Group 3: Structural Challenges - Existing Neck structures often face issues such as frequency aliasing and rigid fusion processes, which compromise feature expression and detection accuracy, particularly for small targets in complex environments [9][10]. - Butter's design addresses these structural bottlenecks by decoupling frequency modeling and multi-scale fusion, achieving a balance between accuracy and efficiency [11][12]. Group 4: Methodology Overview - The Butter framework begins with a 640×640 monocular image, extracting initial features through a lightweight Backbone module, followed by refinement through various lightweight blocks before entering the Neck module [16][17]. - The model employs a four-output head in the Head layer to generate final detection results, including class labels, confidence scores, and bounding boxes [16][17]. Group 5: Feature Fusion Techniques - FAFCE enhances feature fusion accuracy and robustness by employing high-frequency amplification and low-frequency damping mechanisms, which improve the consistency and precision of multi-scale feature integration [20][27]. - PHFFNet implements a hierarchical fusion strategy that alleviates semantic discrepancies between non-adjacent layers, significantly enhancing detection accuracy and alignment in scenarios requiring precise boundary detection [29][30].