小鹏&理想全力攻坚的VLA路线，到底都有哪些研究方向？

Core Viewpoint - The article discusses the transition in intelligent driving technology from rule-driven to data-driven approaches, highlighting the limitations of end-to-end models in complex scenarios and the potential of VLA (Vision-Language Action) as a more streamlined solution [1][2]. Group 1: Challenges in Learning and Research - The technical stack for autonomous driving VLA has not yet converged, leading to a proliferation of algorithms and making it difficult for newcomers to enter the field [2]. - A lack of high-quality documentation and fragmented knowledge in various domains increases the entry barrier for beginners in autonomous driving VLA research [2]. Group 2: Course Development - A new course titled "Autonomous Driving VLA Practical Course" has been developed to address the challenges faced by learners, focusing on a comprehensive understanding of the VLA technical stack [3][4]. - The course aims to provide a one-stop opportunity to enhance knowledge across multiple fields, including visual perception, language modules, and action modules, while integrating cutting-edge technologies [2][3]. Group 3: Course Features - The course emphasizes quick entry into the subject matter through a Just-in-Time Learning approach, using simple language and case studies to help students grasp core technologies rapidly [3]. - It aims to build a framework for research capabilities, enabling students to categorize papers and extract innovative points to form their own research systems [4]. - Practical application is a key focus, with hands-on sessions designed to complete the theoretical-to-practical loop [5]. Group 4: Course Outline - The course covers the origins of autonomous driving VLA, foundational algorithms, and the differences between modular and integrated VLA [6][10][12]. - It includes practical sessions on dataset creation, model training, and performance enhancement, providing a comprehensive learning experience [12][14][16]. Group 5: Instructor Background - The instructors have extensive experience in multimodal perception, autonomous driving VLA, and large model frameworks, with numerous publications in top-tier conferences [22]. Group 6: Learning Outcomes - Upon completion, students are expected to thoroughly understand the current advancements in autonomous driving VLA and master core algorithms [23][24]. - The course is designed to benefit students in internships, job recruitment, and further academic pursuits in the field [26]. Group 7: Course Schedule - The course is set to begin on October 20, with a structured timeline for unlocking chapters and providing support through online Q&A sessions [27].