最近邀请到苹果姐和星友做了一次线上交流，分享给大家。 苹果姐 2020年从国有银行大跨度转行至自动驾驶大厂，后又入职某头部L4创业公司和头部新势力， 研究方向也多次转变：从算法评测开始，又先后从事2D交通红绿灯检测，泊车视觉感知，BEV感 知，端到端主动安全算法等。 无论是转行到自动驾驶，还是之后多次的方向转换，柱哥都学习到很多。我提炼一下有两个关键的 点： 一是机会在面前一定要抓住，付出全力： 2020年转行的时候，投的自驾公司很长时间都没有回信， 最后有一家自驾公司联系苹果姐要求一周后线上机试，苹果姐在没有准备的前提下一周内高强度刷 leetcode最终成功过了机试。也得益于20年自动驾驶扩招，苹果姐也成功转行。 二是先转行再一步步提升，找准赛道： 起初苹果姐从评测开始，虽然不是算法岗但积累了一定的 coding能力。同时借着评测的契机学习了静态感知，之后跳槽也顺利到了感知岗位，然后一步步到 BEV感知再到如今的端到端主动安全，这背后是持续的学习进步和对行业趋势的把握。 最近也有很多同学咨询柱哥方向选择的问题，所以我也是邀请到苹果姐和大家分享这个主题， 直播 回访已经上传到自动驾驶之心知识星球，欢迎大家一 ...

Core Viewpoint - The article discusses the current state and future directions of autonomous driving technology, highlighting the maturity of certain technologies, the challenges that remain, and the emerging trends in the industry. Group 1: Current Technology Maturity - The introduction of BEV (Bird's Eye View) and OCC (Occupancy) perception methods has matured, with no major players claiming that BEV is unusable [2][13] - The main challenge remains corner cases, where 99% of scenarios are manageable, but complex situations like rural roads and large intersections still pose difficulties [13] - E2E (End-to-End) models have not yet demonstrated clear advantages over two-stage models in practical applications, despite their theoretical appeal [4][5] Group 2: Emerging Technologies - VLA (Vision-Language Alignment) is gaining attention as it simplifies tasks and potentially addresses corner cases more effectively than traditional methods [5][6] - The efficiency of models is a critical issue, with discussions around using smaller models to achieve performance close to larger ones [6][30] - Reinforcement learning has not yet proven to be significantly impactful in autonomous driving, with a need for better simulation environments to validate its effectiveness [7][51] Group 3: Future Directions - There is a consensus that VLA and VLM (Vision-Language Model) will be key areas for future development, focusing on enhancing reasoning capabilities and safety [45][48] - The industry is moving towards a more data-driven approach, where the efficiency of data collection, cleaning, and training will determine competitive advantage [28][40] - The integration of world models and closed-loop simulations is seen as essential for advancing autonomous driving technologies [47][50] Group 4: Industry Perspectives - The shift towards VLA/VLM is viewed as a necessary evolution, with the potential to improve user experience and safety in autonomous vehicles [28][45] - The debate between deepening expertise in autonomous driving versus transitioning to embodied intelligence reflects the industry's evolving landscape and personal career choices [22][27] - The current focus on safety and robustness in L4 (Level 4) autonomous driving indicates a divergence in technical approaches between L2+ and L4 players [25][36]

Core Insights - The article highlights the rapid advancements in AI technologies, particularly in autonomous driving and embodied intelligence, which have significantly influenced the industry and attracted substantial investment [2] - A new platform, AutoRobo Knowledge Community, has been launched to assist job seekers in the fields of robotics, autonomous driving, and embodied intelligence, facilitating connections and providing resources [2][3] Group 1: Community and Resources - AutoRobo Knowledge Community has nearly 1,000 members, including professionals from companies like Horizon Robotics, Li Auto, Huawei, and Xiaomi, as well as students preparing for upcoming job fairs [2] - The community offers a variety of resources, including interview questions, industry reports, salary negotiation tips, and resume optimization services [3][4] Group 2: Interview Preparation - The community has compiled a list of 100 common interview questions related to autonomous driving and embodied intelligence, covering various technical aspects [6][7] - Specific topics include sensor fusion, lane detection algorithms, and multi-modal 3D object detection, providing comprehensive preparation materials for job seekers [7][11] Group 3: Industry Insights - The community provides access to industry reports that detail the current state, development trends, and market opportunities within the autonomous driving and embodied intelligence sectors [12][15] - Reports include insights into the Chinese humanoid robot market and the overall landscape of embodied intelligence, helping members understand the industry's dynamics [15]