自动驾驶岗位面试时，一份好的简历是什么样的？ 可以适当夸大，别太过分（简历上写的一定要是自己非常了解的）： 自驾行业是出了名的工资高，好多同学都想往这个方向卷！但你真的知道怎么写一份合格的简历 吗？最近好几位同学让我们帮忙改简历，但都存在各种各样的问题。 看了这么多简历，我觉得其中一位同学的蛮好，最终拿到了某新势力60k的offer，才3年经验！总结 下来，一份合格的简历是条理清晰、重点突出、细节体现、能力体现几个部分。不要乱堆项目和奖 励，要找符合项目岗位的优势点。 1）开门见山 结论先行，直接说出自己的成果和成就（可以在项目前） 举例主要成就： A公司：搭建了什么动态感知后融合，发表专利三篇； B公司：优化了静态目标的融合算法，优秀个人； 2）职责清晰 BEV 算法框架搭建：主要参与者（算法负责人） BEV 算法模型优化：负责人 3）逻辑清晰 每一个点都有目的，多用数字，条理分明，按照序号和标题进行改进（千万别段落式） 1）模型上 ，采用ohem + focal 解决长尾分布问题（经验），提升10%。改进ohem的方案（思考能 力） 2）数据上，10w数据整理，协调（综合能力和沟通能力） 3）部署和融合上 ...

Core Insights - The article discusses the evolving landscape of large models in autonomous driving, highlighting the focus on lightweight solutions, hardware compatibility, knowledge distillation, and efficient fine-tuning of large models [1] - It emphasizes the importance of advanced reasoning paradigms such as Chain-of-Thought (CoT) and VLA combined with reinforcement learning in enhancing spatial perception capabilities [1] Group 1: Course Overview - The course aims to explore cutting-edge optimization methods for large models, focusing on parameter-efficient computation, dynamic knowledge expansion, and complex reasoning [2] - Key challenges in model optimization include parameter compression through pruning and quantization, dynamic knowledge injection techniques, and advanced reasoning paradigms [2][3] Group 2: Enrollment and Requirements - The course is limited to 6-8 participants per session, targeting individuals with a foundational understanding of deep learning and machine learning [4][8] - Participants are expected to have basic programming skills in Python and familiarity with PyTorch, along with a genuine interest in research [8] Group 3: Course Outcomes - The course aims to provide a systematic understanding of large model optimization, helping participants develop their own research ideas and enhance their coding skills [6][7] - Participants will receive guidance on writing and submitting academic papers, including methodologies for drafting and revising manuscripts [6][7] Group 4: Course Structure - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance, covering topics such as model pruning, quantization, and dynamic knowledge expansion [7][18] - Each week focuses on specific themes, including advanced reasoning techniques and collaborative multi-agent systems [18][20] Group 5: Additional Information - The course will utilize publicly available datasets and baseline codes tailored to specific applications, ensuring practical relevance [15][16] - Participants will engage in discussions and hands-on experiments using mainstream large models like LLaMA and GPT [2][18]

Core Viewpoint - The company offers discounted renewal options for existing students whose course validity has expired, eliminating the need to repurchase at full price [1]. Renewal Options - The company provides four renewal options: 1 month, 3 months, 6 months, and 12 months, with varying discounts based on the duration of renewal: - 1 month renewal is calculated as: (original price / 12) x 1 x 100% - 3 months renewal is calculated as: (original price / 12) x 3 x 70% - 6 months renewal is calculated as: (original price / 12) x 6 x 50% - 12 months renewal is calculated as: (original price / 12) x 12 x 30% - Longer renewal periods offer greater discounts [2]. Contact Information - For further inquiries regarding the renewal process, students are encouraged to contact the assistant for assistance [3].

Core Viewpoint - The article discusses the development of Bezier curve Gaussian splatting (BezierGS) by Fudan University, which addresses the challenges of dynamic target reconstruction in autonomous driving scenarios, improving the accuracy and efficiency of scene element separation and reconstruction [1][2]. Group 1 - BezierGS utilizes learnable Bezier curves to represent the motion trajectories of dynamic targets, leveraging temporal information to calibrate pose errors [1]. - The method introduces additional supervision for dynamic target rendering and consistency constraints between curves, leading to improved reconstruction outcomes [1]. - Experiments on the Waymo Open Dataset and nuPlan benchmark demonstrate that BezierGS outperforms state-of-the-art alternatives in both dynamic and static scene target reconstruction [1]. Group 2 - The article highlights the potential to build a high-quality street scene world for training and exploring self-driving models, which can reduce data collection costs [2]. - It emphasizes the reduction of reliance on the accuracy of bounding box annotations, which are often imprecise in current industry and open-source datasets [2]. - The work represents a step towards exploring a true self-driving world model, although it currently only achieves trajectory interpolation and not extrapolation [2].

Core Insights - The article discusses the launch of Kwai Keye-VL, an 8 billion parameter multimodal large language model (MLLM) designed to enhance understanding of short video content, addressing the limitations of existing models in processing dynamic and information-dense media [2][3]. Group 1: Model Development - Kwai Keye-VL is built on a large-scale dataset containing over 600 billion tokens, primarily focused on high-quality video data, and employs an innovative training strategy [2][4]. - The training process consists of a four-stage pre-training phase followed by a two-stage post-training phase, aimed at aligning visual and language features effectively [4][18]. Group 2: Training Methodology - The first stage of training focuses on optimizing basic capabilities such as instruction following through supervised fine-tuning and mixed preference optimization [5]. - The second stage enhances reasoning abilities using a five-mode "cold start" data mixing strategy, which includes various reasoning tasks and high-quality video data [6][12]. Group 3: Performance Evaluation - Keye-VL has demonstrated advanced performance in public benchmark tests, outperforming other leading models of similar size in user experience evaluations [3][27]. - The model's capabilities were validated through extensive evaluation experiments, including the development of a new benchmark, KC-MMBench, tailored for real-world short video scenarios [3][28]. Group 4: Technical Innovations - The model incorporates a hybrid parallelism strategy for efficient training, combining data and sequence parallelism to optimize memory usage and computational efficiency [22][23]. - A dynamic load balancing mechanism is implemented to address computational load imbalances during multimodal training, significantly improving training speed [24]. - A sample-level auto-resume mechanism enhances training stability by allowing automatic recovery from interruptions [25].

写在前面 & 笔者的个人理解 扩散模型在自动驾驶场景视频生成中已经展现出比较有前景的视觉生成质量。然而，现有的基于视频扩散的世界模型在灵活长度、长时序预测以及轨迹规划方面 仍存在不足。这是因为传统视频扩散模型依赖于对固定长度帧序列的全局联合分布建模，而非逐步构建每个时间下的局部化分布。本研究提出 Epona ，一种自回 归扩散世界模型，通过两项关键创新实现局部时空分布建模：1） 解耦的时空分解 ，将时间动态建模与细粒度未来世界生成分离；2） 模块化的轨迹与视频预测 ，通过端到端框架无缝整合运动规划与视觉建模。本文的架构通过引入一种新的"链式前向训练策略"（chain-of-forward training strategy），在实现高分辨率、长持 续时间生成的同时解决了自回归循环中的误差累积问题。实验结果表明，与现有方法相比，Epona在FVD指标上提升7.4%，预测时长可达数分钟。该世界模型进一 步可作为实时端到端规划器，在NAVSIM基准测试中优于现有端到端规划器。 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 今天自动驾驶之心为大家分享 地平线联合 ...

Core Viewpoint - Didi has a strong technical foundation in the autonomous driving sector, particularly in perception algorithms, and is a key player for those interested in pursuing careers in this field [2]. Group 1: Interview Process - The interview process for the perception algorithm position at Didi consists of three technical rounds, with a focus on project details and technical principles [2]. - Candidates are advised to thoroughly understand every detail on their resumes, as interviewers may ask in-depth questions [2]. Group 2: Technical Questions - The first round includes self-introduction and targeted questions about the candidate's research output and direction [3]. - Candidates are asked to explain the core innovations of their papers, leading to discussions on 2D object detection [4]. - The evolution of 2D object detection algorithms from traditional methods to deep learning is a key topic [5]. - Understanding of Anchor-Free detection, specifically the core process of the FCOS algorithm, is assessed [6]. - Candidates are questioned about their familiarity with end-to-end detection algorithms, reflecting the latest developments in the field [7]. - The mechanism of DETR in achieving end-to-end object detection is explored in depth [8]. Group 3: Project Experience - Candidates are expected to present their project experiences, such as a perception project based on the BEVDet model, detailing the algorithm architecture and detection process [9]. - Interviewers inquire about specific challenges faced during the implementation of algorithms in real-world applications and the solutions devised [10]. Group 4: Coding Assessment - A coding challenge is included, where candidates must write the NMS (Non-Maximum Suppression) post-processing code on-site [11]. Group 5: Community and Networking - A community has been established for job seekers in autonomous driving and related fields, with nearly 1,000 members from various companies, providing a platform for networking and support [12].

最近有很多同学咨询我们自动驾驶产业到底怎么样了？有哪些职位和方案，今天为大家盘点下当下的一些情况！ 所有内容出自AuotRobo求职星球，自动驾驶、具身智能、AI类求职聚集地！这里有最专业的面经和岗位分享~ 自动驾驶分级与应用 主要功能：行车，泊车，座舱，V2X 核心系统构成：芯片，软件，传感器 技术趋势一览 1）传统自动驾驶pipline 2）端到端自动驾驶 3）VLM方案 4）VLA方案 主机厂和自驾公司 1）主机厂 新势力：小鹏，理想，蔚来，华为，极氪，小米，零跑，岚图，深蓝（长安）等； 老牌车厂：比亚迪，吉利，长安，奇瑞（星途），长城，上汽（智己），广汽（埃安）外企：奔驰，大众，现代 等； 2）供应商 已经上市：地平线，小马智行，黑芝麻智能，文远智行，知行汽车等； 未上市：momenta，轻舟智行，元戎启行，卓驭，大疆大厂：百度，滴滴等，京东； 其它：商汤绝影，毫末智行，四维图新，经纬恒润等； 职位与方向一览 1）传统方案 定位建图： 1. 定位匹配 2. 建图（nerf，splatting） 感知层次： 1. 障碍物，红绿灯，地面元素 2. BEV算法，OCC ，mapfree 后融合：静态后融合、 ...

Core Viewpoint - The article discusses the transition of autonomous driving technology from Level 2/3 (assisted driving) to Level 4/5 (fully autonomous driving), highlighting the challenges and opportunities in the industry as well as the evolving skill requirements for professionals in the field [2]. Industry Trends - The shift towards high-level autonomous driving is creating a competitive landscape where traditional sensor-based approaches, such as LiDAR, are being challenged by cost-effective vision-based solutions like those from Tesla [2]. - The demand for skills in reinforcement learning and advanced perception algorithms is increasing, leading to a sense of urgency among professionals to upgrade their capabilities [2]. Talent Market Dynamics - The article notes a growing anxiety among seasoned professionals as they face the need to adapt to new technologies and methodologies, while newcomers struggle with the overwhelming number of career paths available in the autonomous driving sector [2]. - The reduction in costs for LiDAR technology, exemplified by Hesai Technology's price drop to $200 and BYD's 70% price reduction, indicates a shift in the market that requires continuous learning and adaptation from industry professionals [2]. Community and Learning Resources - The establishment of the "Autonomous Driving Heart Knowledge Planet" aims to create a comprehensive learning community for professionals, offering resources and networking opportunities to help individuals navigate the rapidly changing landscape of autonomous driving technology [7]. - The community has attracted nearly 4,000 members and over 100 industry experts, providing a platform for knowledge sharing and career advancement [7]. Technical Focus Areas - The article outlines several key technical areas within autonomous driving, including end-to-end driving systems, perception algorithms, and the integration of AI models for improved performance [10][11]. - It emphasizes the importance of understanding various subfields such as multi-sensor fusion, high-definition mapping, and AI model deployment, which are critical for the development of autonomous driving technologies [7].

Core Viewpoint - The article introduces a new job coaching service focused on helping individuals transition into the intelligent driving sector, particularly targeting recent graduates and professionals without specific job experience in this field [2]. Summary by Sections Coaching Scope - Basic services include personalized 1-on-1 coaching sessions, analysis of the learner's profile, and the creation of a detailed learning plan to bridge the gap between current skills and job requirements [5][6]. - The service also offers resume optimization and job referral opportunities based on the learner's situation [6]. Pricing Structure - The coaching service is priced at 8000 per person, which includes a minimum of 10 online meetings, each lasting at least one hour [4]. Advanced Services - Additional services include project practice opportunities that can be added to resumes and simulated interviews that encompass both HR and business interviews, available for an extra fee [5][6]. Instructor Background - Instructors are industry experts with over 8 years of experience in intelligent driving, having worked with leading companies in the sector [7][9].