Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry is witnessing rapid development in end-to-end methods, particularly the one-stage approach exemplified by UniAD, which directly models vehicle trajectories from sensor inputs [1][3]. - There are two main paradigms in the industry: one-stage and two-stage methods, with the one-stage approach gaining traction and leading to various derivatives based on perception, world models, diffusion models, and VLA [3][5]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, focusing on cutting-edge algorithms in both one-stage and two-stage end-to-end methods, aimed at bridging academic and industrial advancements [5][15]. - The course is structured into several chapters, covering the history and evolution of end-to-end methods, background knowledge on VLA, and detailed discussions on both one-stage and two-stage approaches [9][10][12]. Group 3: Key Technologies - The course emphasizes critical technologies such as BEV perception, visual language models (VLM), diffusion models, and reinforcement learning, which are essential for mastering the latest advancements in autonomous driving [5][11][19]. - The second chapter of the course is highlighted as containing the most frequently asked technical keywords for job interviews in the next two years [10]. Group 4: Practical Applications - The course includes practical assignments, such as RLHF fine-tuning, allowing participants to apply their knowledge in real-world scenarios and understand how to build and experiment with pre-trained and reinforcement learning modules [13][19]. - The curriculum also covers various subfields of one-stage end-to-end methods, including those based on perception, world models, diffusion models, and VLA, providing a comprehensive understanding of the current landscape in autonomous driving technology [14][19].

BigBite思维随笔 . Big Bite Small Talk, 杂谈随笔，聊科技，AI，成长，理财，经验杂谈。Stay Hungry 作者 | BigBite 来源 | BigBite思维随笔 原文链接： 模仿学习无法真正端到端 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30个 方向 学习 路线 以下文章来源于BigBite思维随笔 ，作者BigBite >>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 自动驾驶行业新的技术名词层出不穷，在大家争论到底是VLA更好，还是世界模型更先进的时候，其实忽略了相比模型架构，训练方法才是决定功能效 果的关键。事实上无论是VLA也好，世界行为模型也罢，本质上他们都是实现端到端的具体模型结构，可是随着越来越多头部企业在端到端的技术范式 上努力探索投入，头部团队逐渐发现单纯依靠模仿学习实现不了彻底的端到端自动驾驶！ 那么模仿学习在自动驾驶领域中的问题和局限性到底在哪里呢？ 模仿学习假定专家数据是最优的 模仿学习的潜在假设是每一条训练数据轨迹都给出了在当前状态下最优的行为真值，因此越接近训练数据的行 ...

Core Viewpoint - The article emphasizes the establishment of a comprehensive community for autonomous driving enthusiasts, aiming to facilitate knowledge sharing, technical discussions, and job opportunities in the field of autonomous driving and AI [1][13]. Group 1: Community Development - The "Autonomous Driving Heart Knowledge Planet" has grown to over 4,000 members, with a goal to reach nearly 10,000 in the next two years, providing a platform for exchange and technical sharing [1]. - The community offers a variety of resources, including video content, articles, learning paths, Q&A sessions, and job exchange opportunities [1][2]. Group 2: Learning Resources - The community has organized nearly 40 technical routes for members, covering various aspects of autonomous driving, including end-to-end learning, multi-modal models, and data annotation practices [2][5]. - A complete learning stack and roadmap for beginners have been prepared, making it suitable for those with no prior experience [7][9]. Group 3: Industry Insights - The community regularly invites industry leaders and experts to discuss trends in autonomous driving, technology directions, and production challenges [4][62]. - Members can engage in discussions about job opportunities, industry developments, and academic advancements, fostering a collaborative environment [59][64]. Group 4: Technical Focus Areas - Key focus areas include end-to-end autonomous driving, multi-sensor fusion, 3DGS, and NeRF technologies, with detailed resources and discussions available for each topic [31][32][33]. - The community also provides insights into the latest advancements in visual language models (VLM) and their applications in autonomous driving [35][36].

Core Insights - The smart driving industry is currently in a critical phase of competing on technology and cost, with many companies struggling to survive in 2024, although the overall environment has improved slightly this year [2][6] - Traditional planning and control (规控) has matured over the past decade, and professionals in this field need to continuously update their technical skills to remain competitive [7][8] Group 1: Industry Trends - The smart driving sector has faced significant challenges, with many companies unable to endure the tough conditions last year, but some, like Xiaopeng, have found a way to thrive [6] - The price war in the industry has been curtailed by government intervention, yet competition remains fierce [6] Group 2: Career Guidance - For professionals in traditional planning and control, it is advisable to continue in their current roles while also learning new technologies, particularly in emerging areas like end-to-end models and large models [7][8] - There is a growing trend of professionals transitioning from traditional planning and control to end-to-end and large model applications, with many finding success in these new areas [8] Group 3: Community and Resources - The "Automated Driving Heart Knowledge Planet" community offers a platform for technical exchange, featuring members from renowned universities and leading companies in the smart driving field [21] - The community provides access to a wealth of resources, including over 40 technical routes, open-source projects, and job opportunities in the automated driving sector [19][21]

Core Viewpoint - The article emphasizes the shift in research focus from traditional perception and planning methods to end-to-end Vision-Language-Action (VLA) models in the autonomous driving field, highlighting the emergence of various subfields and the need for researchers to adapt to these changes [2][3]. Group 1: VLA Research Directions - The end-to-end development has led to the emergence of multiple technical subfields, categorized into one-stage and two-stage end-to-end approaches, with examples like PLUTO and UniAD [2]. - Traditional fields such as BEV perception and multi-sensor fusion are becoming mature, while the academic community is increasingly focusing on large models and VLA [2]. Group 2: Research Guidance and Support - The program offers structured guidance for students in VLA and autonomous driving, aiming to help them systematically grasp key theoretical knowledge and develop their own research ideas [7][10]. - The course includes a comprehensive curriculum covering classic and cutting-edge papers, coding implementation, and writing methodologies, ensuring students can produce a solid research paper [8][11]. Group 3: Enrollment and Requirements - The program is open to a limited number of students (6 to 8 per session) who are pursuing degrees in VLA and autonomous driving [6]. - Students are expected to have a foundational understanding of deep learning, Python, and PyTorch, with additional support provided for those needing to strengthen their basics [12][14]. Group 4: Course Structure and Outcomes - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance, culminating in a maintenance period for the research paper [11]. - Participants will produce a draft of a research paper, receive project completion certificates, and may obtain recommendation letters based on their performance [15].

Core Viewpoint - The article discusses the impact of end-to-end autonomous driving technology on traditional rule-based control (PNC) methods, highlighting the shift towards data-driven approaches and the complementary relationship between the two systems [2][6]. Summary by Sections Differences Between Approaches - Traditional PNC relies on manually coded rules and logic for vehicle planning and control, utilizing algorithms like PID, LQR, and various path planning methods. Its advantages include clear algorithms and strong interpretability, suitable for stable applications [4]. - End-to-end algorithms aim to directly map raw sensor data to control commands, reducing system complexity and enabling the model to learn human driving behavior through large-scale data training. This approach allows for joint optimization of the entire driving process [4]. Advantages and Disadvantages - **End-to-End Approach**: - Advantages include reduced system complexity, natural driving style emulation, and minimized information loss between modules [4]. - Disadvantages involve challenges in traceability of decision processes, high data scale requirements, and the need for rule-based fallback in extreme scenarios [4]. - **PNC Approach**: - Advantages include clear module functions, ease of debugging, and stable performance in known scenarios, making it suitable for high safety requirements [5]. - Disadvantages consist of high development costs and potential difficulties in handling complex scenarios without suitable rules [5]. Complementary Relationship - The analysis indicates that end-to-end systems require PNC for certain scenarios, while PNC can benefit from the efficiencies of end-to-end approaches. This suggests a complementary rather than adversarial relationship between the two methodologies [6]. Job Opportunities - The article highlights job openings in both end-to-end and traditional PNC roles, indicating a demand for skilled professionals in these areas with competitive salaries ranging from 30k to 100k per month depending on the position and location [8][10][12][14].

Core Insights - NVIDIA is applying its AI models to autonomous vehicles in collaboration with Mercedes, aiming to launch a fleet globally using its end-to-end autonomous driving technology this year [1] Group 1 - NVIDIA's CEO Jensen Huang announced the application of AI models in autonomous driving [1] - The collaboration with Mercedes aims to implement this technology on a global scale [1] - The deployment of the autonomous driving fleet is expected to occur within this year [1]