Workflow
自动驾驶数据集
icon
Search documents
从最初的2D方案到当前的VLA大框架,一代又一代的自驾路线是怎么样演变的?
自动驾驶之心· 2025-08-22 04:00
Core Viewpoint - The article emphasizes the importance of creating an engaging learning environment in the field of autonomous driving and AI, aiming to bridge the gap between industry and academia while providing resources for career development and technical knowledge sharing [1][3]. Group 1: Community and Resources - The "Autonomous Driving Heart Knowledge Planet" has evolved through multiple iterations, providing a comprehensive platform for academic and industry exchanges, including job opportunities and technical discussions [1]. - The community has compiled over 40 technical routes and resources, significantly reducing the time needed for information retrieval in the autonomous driving sector [1]. - Members include individuals from renowned universities and leading companies in the autonomous driving field, fostering a rich environment for knowledge sharing [12]. Group 2: Technical Learning and Development - The community offers a structured learning path for newcomers, including foundational knowledge in mathematics, computer vision, and deep learning, as well as practical programming skills [12][20]. - Various learning routes are available, such as end-to-end learning, multi-modal large models, and simulation frameworks, catering to different levels of expertise [12][34]. - The platform provides access to numerous open-source projects and datasets relevant to autonomous driving, enhancing practical learning and application [30][32]. Group 3: Job Opportunities and Networking - The community has established a job referral mechanism with multiple autonomous driving companies, facilitating direct connections between job seekers and employers [6]. - Regular job postings and sharing of internship opportunities are available, helping members stay informed about the latest openings in the industry [11][22]. - Members can engage in discussions about career choices and research directions, receiving guidance from experienced professionals in the field [89]. Group 4: Technical Discussions and Innovations - The community hosts discussions on cutting-edge topics such as VLA (Vision Language Architecture), world models, and diffusion models, keeping members updated on the latest advancements [44][48]. - Regular live sessions with industry experts are conducted, allowing members to learn about new technologies and methodologies in autonomous driving [85]. - The platform encourages collaboration and knowledge exchange, aiming to cultivate future leaders in the autonomous driving industry [3].
4000人了,死磕技术的自动驾驶黄埔军校到底做了哪些事情?
自动驾驶之心· 2025-07-31 06:19
Core Viewpoint - The article emphasizes the importance of creating an engaging learning environment in the field of autonomous driving and AI, aiming to bridge the gap between industry and academia while providing valuable resources for students and professionals [1]. Group 1: Community and Resources - The community has established a closed loop across various fields including industry, academia, job seeking, and Q&A exchanges, focusing on what type of community is needed [1][2]. - The platform offers cutting-edge academic content, industry roundtables, open-source code solutions, and timely job information, streamlining the search for resources [2][3]. - A comprehensive technical roadmap with over 40 technical routes has been organized, catering to various interests from consulting applications to the latest VLA benchmarks [2][14]. Group 2: Educational Content - The community provides a series of original live courses and video tutorials covering topics such as automatic labeling, data processing, and simulation engineering [4][10]. - Various learning paths are available for beginners, as well as advanced resources for those already engaged in research, ensuring a supportive environment for all levels [8][10]. - The community has compiled a wealth of open-source projects and datasets related to autonomous driving, facilitating quick access to essential materials [25][27]. Group 3: Job Opportunities and Networking - The platform has established a job referral mechanism with multiple autonomous driving companies, allowing members to submit their resumes directly to desired employers [4][11]. - Continuous job sharing and position updates are provided, contributing to a complete ecosystem for autonomous driving professionals [11][14]. - Members can freely ask questions regarding career choices and research directions, receiving guidance from industry experts [75]. Group 4: Technical Focus Areas - The community covers a wide range of technical focus areas including perception, simulation, planning, and control, with detailed learning routes for each [15][29]. - Specific topics such as 3D target detection, BEV perception, and online high-precision mapping are thoroughly organized, reflecting current industry trends and research hotspots [42][48]. - The platform also addresses emerging technologies like visual language models (VLM) and diffusion models, providing insights into their applications in autonomous driving [35][40].
本来决定去具身,现在有点犹豫了。。。
自动驾驶之心· 2025-07-05 09:12
Core Insights - The article discusses the evolving landscape of embodied intelligence, highlighting its transition from a period of hype to a more measured approach as the technology matures and is not yet at a productivity stage [2]. Group 1: Industry Trends - Embodied intelligence has gained significant attention over the past few years, but the industry is now recognizing that it is still in the early stages of development [2]. - There is a growing demand for skills in multi-sensor fusion and robotics, particularly in areas like SLAM and ROS, which are crucial for engaging with embodied intelligence [3][4]. - Many companies in the robotics sector are rapidly developing, with numerous startups receiving substantial funding, indicating a positive outlook for the industry in the coming years [3][4]. Group 2: Job Market and Skills Development - The job market for algorithm positions is competitive, with a focus on cutting-edge technologies such as end-to-end models, VLA, and reinforcement learning [3]. - Candidates with a background in robotics and a solid understanding of the latest technologies are likely to find opportunities, especially as traditional robotics remains a primary product line [4]. - The article encourages individuals to enhance their technical skills in robotics and embodied intelligence to remain competitive in the job market [3][4]. Group 3: Community and Resources - The article promotes a community platform that offers resources for learning about autonomous driving and embodied intelligence, including video courses and job postings [5]. - The community aims to gather a large number of professionals and students interested in smart driving and embodied intelligence, fostering collaboration and knowledge sharing [5]. - The platform provides access to the latest industry trends, technical discussions, and job opportunities, making it a valuable resource for those looking to enter or advance in the field [5].