当我们把端到端量产需要的能力展开后......

Core Viewpoint - The article emphasizes the rising importance of end-to-end (E2E) systems in the autonomous driving industry, highlighting the shift from modular perception to direct environmental sensing and action generation, which simplifies system complexity and enhances the ability to handle complex driving scenarios [2]. Group 1: End-to-End Systems - The success of Horizon HSD has prompted a reevaluation of the significance of E2E systems in smart driving, moving away from heavy reliance on modular perception and strict rule-based systems [2]. - E2E systems face challenges in practical applications, such as trajectory instability, primarily due to the lack of continuous correction capabilities based on environmental feedback [3]. - Reinforcement Learning (RL) offers a new approach for E2E systems, transitioning from imitation to optimization by incorporating reward signals to refine action strategies and address limitations of pure imitation learning [4][5]. Group 2: Industry Trends and Talent Demand - Leading companies in the industry have developed a comprehensive model iteration approach, which includes imitation learning training, closed-loop reinforcement learning, and rule-based planning, indicating a high barrier to entry for talent in E2E production [6]. - The high barrier to entry and scarcity of skilled professionals have resulted in generous salaries, with top talents earning starting salaries of 1 million and above [7]. Group 3: Challenges in Mass Production - The mass production of E2E systems encounters numerous challenges, including complex scenarios like congestion, static yaw, and collision situations, necessitating both data mining and data cleaning [8]. - There is a notable gap in practical experience among many candidates, as many have only theoretical knowledge without real-world application experience [8]. Group 4: Course Offering - The article introduces a specialized course aimed at bridging the gap in practical skills for E2E systems, led by top-tier algorithm engineers from the industry [9]. - The course covers various aspects of E2E systems, including task overview, two-stage and one-stage algorithms, navigation information applications, RL algorithms, trajectory optimization, and production experiences [12][14][15][16][17][18][19][20][21]. Group 5: Target Audience and Prerequisites - The course is designed for advanced learners with a foundational understanding of autonomous driving perception, reinforcement learning, and programming skills, although those with weaker backgrounds can still participate [22][23].