暑假就要结束了，开学季越来越多的同学联系峰哥和柱哥。有刚入行的研一小白，也有秋招激战正酣的研二/研 三的同学，也不乏打算转行自驾的小伙伴。很开心，通过自动驾驶之心和大家连接在一起，共同推进行业的发 展。 我们的愿景是让AI与自动驾驶走进每个有需要的同学。 端到端自动驾驶如何入门？一段式/二段式量产中如何使用？ 传统规划控制想转端到端VLA，求学习路线图！ 自动驾驶多模态大模型预训练数据集有哪些？求自动驾驶VLA微调数据集？ 多传感器融合现在还适合就业吗？ 3DGS和闭环仿真如何结合？应用中需要考虑哪些元素？ 世界模型是个啥？业内如何应用，研究还有切入点么？ 业内哪家公司前景好一些，适合跳槽，都有什么岗位开放招聘？求星主内推~ 博士入学，哪个方向容易出成果？ 闭环强化学习如何入门？ 我们会不定期和一线的学术界&工业界大佬畅聊自动驾驶发展趋势，探讨技术走向和量产痛点： 这是一个认真做内容的社区，一个培养未来领袖的地方。 星球内部梳理了近40+自动驾驶技术方向，同时也有面 向求职的问答梳理。 | 0 国内高校著名自动驾驶团队整理 链接: https://t.zsxq.com/hlVJZ | 5 算法进阶 | (17 ...

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]