自动驾驶端到端规划控制就业小班课

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传统规划控制不太好找工作了。。。
自动驾驶之心· 2025-07-11 06:46
Core Viewpoint - The article emphasizes the evolving landscape of autonomous driving, particularly the integration of traditional planning and control (PnC) with end-to-end systems, highlighting the necessity for professionals to adapt to these changes in order to remain competitive in the job market [2][4][29]. Group 1: Industry Trends - The shift towards end-to-end and VLA (Vision-Language Alignment) systems is impacting traditional PnC roles, which are now required to incorporate more advanced algorithms and frameworks [2][4]. - As of 2025, end-to-end systems are expected to become more prevalent, yet traditional PnC methods will still play a crucial role, especially in safety-critical applications like Level 4 autonomous driving [4][29]. - The article discusses the importance of understanding both traditional and modern approaches to planning and control, as they are increasingly being integrated in practical applications [4][29]. Group 2: Educational Offerings - The company has launched specialized courses aimed at bridging the gap between theoretical knowledge and practical application in the field of autonomous driving, focusing on real-world challenges and interview preparation [5][7]. - The courses are designed to provide hands-on experience with current industry practices, including classic and innovative solutions in PnC, and are tailored for individuals with some background in the field [8][12]. - The curriculum includes modules on foundational algorithms, decision-making frameworks, and advanced topics such as contingency planning and interactive planning, which are critical for modern autonomous driving systems [20][21][24][26][29]. Group 3: Career Development - The courses not only focus on technical skills but also offer support in job application processes, including resume reviews and mock interviews, to enhance employability [9][10][31]. - Previous participants have successfully secured positions at major companies in the autonomous driving sector, indicating the effectiveness of the training provided [10][12]. - The program aims to equip participants with the skills necessary to construct decision-making systems and address real-world challenges in autonomous driving, thereby enhancing their career prospects [13][29].
今年,传统规划控制怎么找工作?
自动驾驶之心· 2025-07-02 13:54
Core Viewpoint - The article emphasizes the evolving landscape of autonomous driving, highlighting the integration of traditional planning and control with end-to-end systems, and the importance of adapting to industry trends for job seekers in this field [2][4][29]. Group 1: Industry Trends - The shift towards end-to-end and VLA (Vision-Language Alignment) systems is impacting traditional planning and control roles, which are still essential for safety-critical applications like L4 autonomous driving [2][4][29]. - There is a growing emphasis on combining rule-based algorithms with end-to-end approaches in job interviews, indicating a need for candidates to be proficient in both areas [3][4]. Group 2: Educational Offerings - The company has launched specialized courses aimed at addressing real-world challenges in autonomous driving planning and control, focusing on practical applications and interview preparation [5][7][10]. - The courses are designed to provide hands-on experience with industry-relevant projects, enhancing participants' resumes and job prospects [8][10][12]. Group 3: Course Structure - The curriculum covers foundational algorithms, decision-making frameworks, and advanced topics such as contingency planning and interactive planning, ensuring a comprehensive understanding of the field [20][21][24][26][29]. - The course also includes interview coaching, resume enhancement, and personalized guidance from industry experts, aimed at increasing participants' employability [31][34][36]. Group 4: Target Audience - The courses are tailored for individuals with a background in vehicle engineering, automation, computer science, and related fields, as well as those looking to transition into autonomous driving roles [37][39]. - Participants are expected to have a basic understanding of programming and relevant mathematical concepts to fully benefit from the training [38][39].