Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language Agent) technical talent in the automotive industry, with salaries for experts reaching up to $70,000 per month for positions requiring 3-5 years of experience [1] - The technology stack involved in end-to-end and VLA is complex, covering various advanced algorithms and models such as BEV perception, VLM (Vision-Language Model), diffusion models, reinforcement learning, and world models [2] Course Offerings - The company is launching two specialized courses: "End-to-End and VLA Autonomous Driving Class" and "Practical Course on VLA and Large Models," aimed at helping individuals quickly and efficiently enter the field of end-to-end and VLA technologies [2] - The "Practical Course on VLA and Large Models" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA, including mainstream inference-enhanced VLA [2] - The course includes a detailed theoretical foundation and practical assignments, teaching participants how to build their own VLA models and datasets from scratch [2] Instructor Team - The instructor team consists of experts from both academia and industry, including individuals with extensive research and practical experience in multi-modal perception, autonomous driving VLA, and large model frameworks [7][10][13] - Notable instructors include a Tsinghua University master's graduate with multiple publications in top conferences and a current algorithm expert at a leading domestic OEM [7][13] Target Audience - The courses are designed for individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and who have a grasp of concepts related to transformer large models, reinforcement learning, and BEV perception [15] - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [15]

Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language Agent) technical talent in the automotive industry, with salaries for experts reaching up to $70,000 per month for positions requiring 3-5 years of experience [1] - The technology stack for end-to-end and VLA is complex, involving various advanced algorithms such as BEV perception, Vision-Language Models (VLM), diffusion models, reinforcement learning, and world models [1] - The company is offering specialized courses to help individuals quickly and efficiently learn about end-to-end and VLA technologies, collaborating with experts from both academia and industry [1] Course Offerings - The "End-to-End and VLA Autonomous Driving Course" focuses on the macro aspects of end-to-end autonomous driving, covering key algorithms and theoretical foundations, including BEV perception, large language models, diffusion models, and reinforcement learning [10] - The "Autonomous Driving VLA and Large Model Practical Course" is led by academic experts and covers VLA from the perspective of VLM as an autonomous driving interpreter, modular VLA, and current mainstream inference-enhanced VLA [1][10] - Both courses include practical components, such as building a VLA model and dataset from scratch, and implementing algorithms like the Diffusion Planner and ORION algorithm [10][12] Instructor Profiles - The instructors include experienced professionals and researchers from top institutions, such as Tsinghua University and QS30 universities, with backgrounds in multimodal perception, autonomous driving VLA, and large model frameworks [6][9][12] - Instructors have published numerous papers in prestigious conferences and have hands-on experience in developing and deploying advanced algorithms in the field of autonomous driving [6][9][12] Target Audience - The courses are designed for individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and concepts related to transformer large models, reinforcement learning, and BEV perception [14] - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [14]

Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language Agent) technical talent in the automotive industry, with salaries for experts reaching up to 70k per month for positions requiring 3-5 years of experience [1] - The technology stack for end-to-end and VLA is complex, involving various advanced algorithms such as BEV perception, Vision-Language Models (VLM), diffusion models, reinforcement learning, and world models [1] - The industry is offering specialized courses to help individuals quickly and effectively learn about end-to-end and VLA technologies, featuring collaboration between academia and industry experts [1] Course Offerings - The "End-to-End and VLA Autonomous Driving Course" focuses on key algorithms and theoretical foundations in end-to-end autonomous driving, covering both one-stage and two-stage approaches, including BEV perception and large language models [11] - The "Autonomous Driving VLA and Large Model Practical Course" is designed for beginners in the VLA field, providing a comprehensive overview of VLA, including modules on Vision, Language, and Action, as well as reinforcement learning and diffusion models [2] - Both courses include practical assignments, allowing participants to build their own VLA models and datasets from scratch [2] Instructor Profiles - The course instructors include experts with strong academic backgrounds and practical experience in autonomous driving and large model development, such as those from Tsinghua University and top-tier universities [7][10][13] - Instructors have published numerous papers in prestigious conferences and have experience in leading projects related to multimodal perception and autonomous driving [7][10][13] Target Audience - The courses are aimed at individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and who have a grasp of concepts related to transformer models, reinforcement learning, and BEV perception [15] - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [15]

Core Insights - The article discusses the evolution of end-to-end algorithms in autonomous driving, highlighting the transition from modular production algorithms to end-to-end approaches and the recent focus on Vision-Language Models (VLA) [1][3]. Group 1: End-to-End Algorithms - End-to-end algorithms are central to the current mass production of autonomous driving technology, involving a rich technology stack [1]. - There are two main paradigms in the industry: single-stage and two-stage approaches, with UniAD being a representative of the single-stage paradigm [1]. - The single-stage approach can be further categorized into several subfields, including perception-based, diffusion model-based, world model-based, and VLA-based end-to-end algorithms [1]. Group 2: VLA and Course Offerings - The article mentions the recent surge in interest regarding how to efficiently learn about end-to-end and VLA technologies, leading to the creation of specialized courses [3]. - The "End-to-End and VLA Autonomous Driving Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA approaches [3]. - The course includes a detailed theoretical foundation and practical assignments to help participants build their own VLA models and datasets [3]. Group 3: Course Instructors - The course features a team of instructors with significant academic and practical experience in multi-modal perception, autonomous driving VLA, and large model frameworks [7][9]. - Instructors have published numerous papers in top international conferences and have hands-on experience in developing and implementing cutting-edge algorithms in the field [7][9][10]. Group 4: Target Audience and Requirements - The courses are designed for individuals with a foundational understanding of autonomous driving and familiarity with key technologies such as transformer models, reinforcement learning, and BEV perception [13]. - Participants are expected to have a basic knowledge of probability theory, linear algebra, and proficiency in Python and PyTorch [13].

Core Viewpoint - The article discusses the evolution and current state of end-to-end algorithms in autonomous driving, highlighting the emergence of various subfields, particularly those based on Visual Language Models (VLA) and the increasing interest in these technologies within both academia and industry [1][3]. Summary by Sections End-to-End Algorithms - End-to-end algorithms are central to the current mass production of autonomous driving technologies, involving a rich technology stack. There are primarily two paradigms: single-stage and two-stage. The single-stage approach, exemplified by UniAD, directly models vehicle trajectories from sensor inputs, while the two-stage approach outputs trajectories based on perception results [1]. VLA and Related Technologies - The development has progressed from modular production algorithms to end-to-end systems and now to VLA. Key technologies involved include BEV perception, Visual Language Models (VLM), diffusion models, reinforcement learning, and world models. The article emphasizes the importance of understanding these technologies to grasp the cutting-edge directions in both academia and industry [3]. Courses Offered - The article promotes two courses aimed at helping individuals quickly and efficiently learn about end-to-end and VLA in autonomous driving. The courses are designed for those new to large models and VLA, covering foundational theories and practical applications [3][10]. Course Content - The "VLA and Large Model Practical Course" focuses on VLA, starting from VLM as an interpreter for autonomous driving, and covers modular and integrated VLA, as well as mainstream inference-enhanced VLA. It includes detailed theoretical foundations and practical assignments to build VLA models and datasets from scratch [3][10]. Instructor Team - The courses are led by experienced instructors from both academia and industry, with backgrounds in multi-modal perception, autonomous driving VLA, and large model frameworks. They have published numerous papers in top conferences and have substantial practical experience in the field [7][9][10]. Target Audience - The courses are aimed at individuals with a foundational understanding of autonomous driving, familiar with basic modules, and possessing knowledge of transformer models, reinforcement learning, and BEV perception. A background in probability theory, linear algebra, and programming in Python and PyTorch is also recommended [13].

Core Viewpoint - 2023 is identified as the year of end-to-end production, with 2024 expected to be a significant year for this development in the automotive industry, particularly in autonomous driving technology [1][3]. Group 1: End-to-End Production - Leading new forces and manufacturers have already achieved end-to-end production [1]. - There are two main paradigms in the industry: one-stage and two-stage approaches, with UniAD being a representative of the one-stage method [1]. Group 2: Development Trends - Since last year, the one-stage end-to-end approach has rapidly evolved, leading to various derivatives such as perception-based, world model-based, diffusion model-based, and VLA-based one-stage methods [3]. - Major autonomous driving companies are focusing on self-research and mass production of end-to-end autonomous driving solutions [3]. Group 3: Course Offerings - A course titled "End-to-End and VLA Autonomous Driving" has been launched, covering cutting-edge algorithms in both one-stage and two-stage end-to-end approaches [5]. - The course aims to provide insights into the latest technologies in the field, including BEV perception, visual language models, diffusion models, and reinforcement learning [5]. Group 4: Course Structure - The course consists of several chapters, starting with an introduction to end-to-end algorithms, followed by background knowledge essential for understanding the technology stack [9][10]. - The second chapter focuses on the most frequently asked technical keywords in job interviews over the next two years [10]. - Subsequent chapters delve into two-stage end-to-end methods, one-stage end-to-end methods, and practical assignments involving RLHF fine-tuning [12][13]. Group 5: Learning Outcomes - Upon completion, participants are expected to reach a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer [19]. - The course aims to deepen understanding of key technologies such as BEV perception, multimodal large models, and reinforcement learning, enabling participants to apply learned concepts to real projects [19].

Core Viewpoint - The article discusses the development and features of the PLUTO model within the end-to-end autonomous driving domain, emphasizing its unique two-stage architecture and its direct encoding of structured perception outputs for downstream control tasks [1][2]. Summary by Sections Overview of PLUTO - PLUTO is characterized by its three main losses: regression loss, classification loss, and imitation learning loss, which collectively contribute to the model's performance [7]. - Additional auxiliary losses are incorporated to aid model convergence [9]. Course Introduction - The article introduces a new course titled "End-to-End and VLA Autonomous Driving," developed in collaboration with top algorithm experts from domestic leading manufacturers, aimed at addressing the challenges faced by learners in this rapidly evolving field [12][15]. Learning Challenges - The course addresses the difficulties learners face due to the fast-paced development of technology and the fragmented nature of knowledge across various domains, making it hard for beginners to grasp the necessary concepts [13]. Course Features - The course is designed to provide quick entry into the field, build a framework for research capabilities, and combine theory with practical applications [15][16][17]. Course Outline - The course consists of several chapters covering topics such as the history and evolution of end-to-end algorithms, background knowledge on various technologies, and detailed discussions on both one-stage and two-stage end-to-end methods [20][21][22][29]. Practical Application - The course includes practical assignments, such as RLHF fine-tuning, allowing students to apply their theoretical knowledge in real-world scenarios [31]. Instructor Background - The instructor, Jason, has a strong academic and practical background in cutting-edge algorithms related to end-to-end and large models, contributing to the course's credibility [32]. Target Audience and Expected Outcomes - The course is aimed at individuals with a foundational understanding of autonomous driving and related technologies, with the goal of elevating their skills to the level of an end-to-end autonomous driving algorithm engineer within a year [36].

Core Viewpoint - The company has launched a "Super Discount Card" to address feedback regarding high course prices, offering a 30% discount on all courses for a year [2][4]. Group 1: Course Offerings - The company has introduced several new courses in the field of autonomous driving, including "End-to-End and VLA Autonomous Driving Small Class," "End-to-End and Planning Control (Third Session)," and "4D Annotation Algorithm Employment Small Class" [2]. - The "End-to-End and VLA" course has received positive feedback from participants, indicating strong interest and satisfaction [2]. Group 2: Discount Card Details - The "Super Discount Card" is priced at 299 yuan and provides a 30% discount on all courses related to autonomous driving and embodied intelligence, including future courses [4]. - The card is valid for one year from the date of purchase and can be fully refunded if no courses are purchased within that year [4]. - The promotional period for purchasing the discount card is from September 1 to September 14 [4].

Core Viewpoint - The company has launched a "Super Discount Card" to address feedback regarding high course prices in the field of autonomous driving, offering a 30% discount on all courses for a limited time [2][4]. Group 1: Course Offerings - The company has introduced several new courses in autonomous driving, including "End-to-End and VLA Autonomous Driving Small Class," "End-to-End and Planning Control (Third Session)," and "4D Annotation Algorithm Employment Small Class," which have received positive feedback [2]. - Future plans include launching additional courses focused on VLA and model deployment [2]. Group 2: Discount Card Details - The "Super Discount Card" is priced at 299 yuan and provides a 30% discount on all courses related to autonomous driving and embodied intelligence self-research courses, including future new courses [4]. - The card is valid for one year from the date of purchase and is available for a limited time from September 1 to September 14 [4]. - A full refund is available if no courses are purchased within one year of buying the discount card [4].

Core Viewpoint - The article emphasizes the importance of understanding end-to-end (E2E) algorithms and Visual Language Models (VLA) in the context of autonomous driving, highlighting the rapid development and complexity of the technology stack involved [2][32]. Summary by Sections Introduction to End-to-End and VLA - The article discusses the evolution of large language models over the past five years, indicating a significant technological advancement in the field [2]. Technical Foundations - The Transformer architecture is introduced as a fundamental component for understanding large models, with a focus on attention mechanisms and multi-head attention [8][12]. - Tokenization methods such as BPE (Byte Pair Encoding) and positional encoding are explained as essential for processing sequences in models [13][9]. Course Overview - A new course titled "End-to-End and VLA Autonomous Driving" is launched, aimed at providing a comprehensive understanding of the technology stack and practical applications in autonomous driving [21][33]. - The course is structured into five chapters, covering topics from basic E2E algorithms to advanced VLA methods, including practical assignments [36][48]. Key Learning Objectives - The course aims to equip participants with the ability to classify research papers, extract innovative points, and develop their own research frameworks [34]. - Emphasis is placed on the integration of theory and practice, ensuring that learners can apply their knowledge effectively [35]. Industry Demand and Career Opportunities - The demand for VLA/VLM algorithm experts is highlighted, with salary ranges between 40K to 70K for positions requiring 3-5 years of experience [29]. - The course is positioned as a pathway for individuals looking to transition into roles focused on autonomous driving algorithms, particularly in the context of emerging technologies [28].