Core Viewpoint - Guangdong Province has officially issued a set of policies aimed at promoting high-quality development in transportation through artificial intelligence, establishing a comprehensive support system for smart transportation development [2] Group 1: Policy Measures - The policy includes 14 concrete measures to create a full-chain system of "core element supply - innovative scenario empowerment - work mechanism guarantee" [2] - It aims to accelerate the pilot application of intelligent connected vehicles and support the establishment of demonstration application recognition in free trade zones and administrative regions [2][5] Group 2: Data and Technology Support - The measures focus on three main areas: data supply, computing power and algorithm support, and key technology breakthroughs [3] - Guangdong will enhance data governance in the transportation sector, creating a standardized resource directory that includes key areas such as highways, railways, and ports [3] - The province will utilize data centers to support the orderly construction of computing power and deploy edge computing nodes in critical locations to form a collaborative system [3][4] Group 3: Application Scenarios - Eight innovative application scenarios are outlined to transition AI technology from the laboratory to practical use, including remote driving and intelligent decision-making [5][6] - The policy supports the construction of a national "vehicle-road-cloud integration" application pilot city in Guangzhou and Shenzhen, promoting the testing of autonomous driving in logistics scenarios [6] Group 4: Smart Infrastructure Development - The measures propose the integration of AI and domestic BIM technology in smart road construction, enhancing maintenance through unmanned inspections and data analysis [6] - The policy aims to transform road network management from "visible and measurable" to "intelligent research and prevention" [6] Group 5: Enhancing Transportation Efficiency - The integration of AI in rail transport will facilitate the optimization of passenger flow prediction and dynamic capacity allocation [7] - The development of a comprehensive monitoring system for waterways will enhance navigation efficiency and information exchange [7] Group 6: Emergency and Logistics Applications - AI will be applied in civil aviation for flight flow management and airport operations, optimizing resource allocation and enhancing safety [8] - The logistics sector will benefit from AI in demand integration and capacity allocation, improving multi-modal transport efficiency [9]

股票研究/[Table_Date] 2026.01.18 [Table_Industry] 计算机 上海发布"模速智行"行动计划，自动驾 驶产业驶入加速赛道 [Table_Invest] 评级： 增持 | [姓名table_Authors] | 电话 | 邮箱 | 登记编号 | | --- | --- | --- | --- | | 杨林(分析师) | 021-23183969 | yanglin2@gtht.com | S0880525040027 | | 魏宗(分析师) | 021-23180000 | weizong@gtht.com | S0880525040058 | | 吕浦源(分析师) | 021-23183822 | lvpuyuan@gtht.com | S0880525050002 | | 朱瑶(分析师) | 021-23187261 | zhuyao@gtht.com | S0880526010002 | 本报告导读： 行 业 跟 踪 报 告 证 券 研 究 报 告 研 究 请务必阅读正文之后的免责条款部分 股 票 1 月 7 日三部门联合印发《上海高级别自动驾驶引领区"模速智行"行动计 ...

Core Viewpoint - The article emphasizes the need for collaboration and innovation in the L2 intelligent driving sector, highlighting the importance of engaging more talented individuals to address industry challenges and contribute to advancements in technology [2]. Group 1: Industry Dynamics - The L2 intelligent driving sector is entering a critical phase where overcoming existing difficulties requires collective effort from industry professionals [2]. - The company aims to enhance its platform by providing various outputs such as roundtable discussions, practical and industrial-grade courses, and consulting services to add value to the industry [2]. Group 2: Key Directions - The main focus areas for development include but are not limited to: autonomous driving product management, 4D annotation/data closure, world models, VLA, large models for autonomous driving, reinforcement learning, and end-to-end solutions [4]. Group 3: Job Descriptions - The company is targeting training collaborations in autonomous driving, primarily focusing on B-end partnerships with enterprises, universities, and research institutions, as well as C-end offerings for students and job seekers [5].

Core Viewpoint - The article emphasizes the need for collaboration and innovation in the autonomous driving industry, highlighting the importance of engaging more talented individuals to address the challenges and pain points in the sector [2]. Group 1: Industry Direction - The main focus areas in the autonomous driving field include but are not limited to: product management, 4D annotation/data loop, world models, VLA, large models for autonomous driving, reinforcement learning, and end-to-end solutions [4]. Group 2: Job Description - The positions are primarily aimed at training collaborations in autonomous driving, targeting both B-end (enterprises, universities, research institutes) and C-end (students, job seekers) audiences for course development and original content creation [5]. Group 3: Contact Information - For discussions regarding compensation and collaboration methods, interested parties are encouraged to add the WeChat contact provided for further communication [6].

Core Viewpoint - The article discusses the limitations of current large visual language models (VLMs) in autonomous driving, emphasizing the need for enhanced spatial perception and geometric understanding to support robust decision-making in real-world scenarios [2][3]. Group 1: Model Introduction - A new model named Percept-WAM (Perception-Enhanced World–Awareness–Action Model) has been proposed, aiming to integrate perception, world awareness, and vehicle action into a cohesive framework for autonomous driving [3][4]. - Percept-WAM is designed to create a complete link from perception to decision-making, addressing the shortcomings of existing models that struggle with real-world complexities [3][4]. Group 2: Model Architecture - The architecture of Percept-WAM incorporates a general reasoning VLM backbone while introducing World-PV and World-BEV tokens to unify 2D/3D perception representations [5]. - The model employs a grid-conditioned prediction mechanism and IoU-aware confidence outputs to enhance the accuracy and efficiency of its outputs, along with a lightweight action decoding head for efficient trajectory prediction [5][6]. Group 3: Training Tasks - Percept-WAM is trained using multi-view streaming video, LiDAR point clouds (optional), and text queries, optimizing various tasks such as 2D detection, instance segmentation, semantic segmentation, and 3D detection [6][9]. - The model's training approach allows for joint optimization across multiple tasks, enhancing the overall performance through shared geometric and semantic information [23]. Group 4: Performance Evaluation - In public benchmarks, Percept-WAM demonstrates competitive performance in PV perspective perception, BEV perspective perception, and end-to-end trajectory planning compared to existing models [21][30]. - Specifically, in the PV perspective, Percept-WAM achieves a 49.9 mAP in 2D detection, surpassing the performance of specialized models like Mask R-CNN [22][24]. - In the BEV perspective, the model achieves a 58.9 mAP in 3D detection, outperforming traditional BEV detection methods [27][28]. Group 5: Confidence Prediction - The introduction of IoU-based confidence prediction significantly improves the alignment between predicted confidence scores and actual localization quality, enhancing the reliability of dense detection [25]. Group 6: Decision-Making Integration - Percept-WAM integrates World–Action tokens for action and trajectory prediction, allowing for a seamless transition from world modeling to decision output, thus aligning perception and planning in a unified representation space [16][17]. - The model employs a query-based trajectory prediction method that leverages multiple feature groups, enhancing the efficiency and accuracy of trajectory planning [19]. Group 7: Future Implications - Percept-WAM represents a forward-looking evolution in autonomous driving, emphasizing the importance of a unified model that can perceive, understand, and act within the world, moving beyond traditional models that merely process language [41].

Core Viewpoint - The article emphasizes the need for collaboration and innovation in the autonomous driving industry, highlighting the importance of engaging more talented individuals to address the challenges and pain points in the sector [2]. Group 1: Industry Direction - The main focus areas in the autonomous driving field include but are not limited to: product management, 4D annotation/data loop, world models, VLA, large models for autonomous driving, reinforcement learning, and end-to-end systems [4]. Group 2: Job Description - The positions are primarily aimed at training collaborations in the autonomous driving sector, targeting both B-end (enterprises, universities, research institutes) and C-end (students, job seekers) audiences for course development and original content creation [5]. Group 3: Contact Information - For inquiries regarding compensation and collaboration methods, interested parties are encouraged to add the WeChat contact provided for further communication [6].

Core Viewpoint - The article emphasizes the need for deeper technical exploration and collaboration in the autonomous driving industry, highlighting recent technological advancements from companies like Tesla, Xiaopeng, and Li Auto [2]. Group 1: Industry Direction - The main focus areas for the autonomous driving sector include but are not limited to: autonomous driving product management, 4D annotation/data loop, world models, VLA, autonomous driving large models, reinforcement learning, and end-to-end systems [4]. Group 2: Recruitment and Collaboration - The company is seeking to expand its team with talented individuals, particularly for roles related to autonomous driving training partnerships targeting both enterprises and educational institutions, as well as content creation for students and job seekers [5]. - Interested parties are encouraged to reach out for discussions regarding compensation and collaboration methods [6].

Core Viewpoint - The article emphasizes the growing demand for skills in end-to-end and VLA (Vision-Language-Action) autonomous driving, highlighting the saturation of job opportunities in these areas and the urgency for newcomers to acquire relevant knowledge and skills quickly [1]. Course Offerings - The "End-to-End and VLA Autonomous Driving Course" is designed to provide comprehensive training in VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA, and current mainstream inference-enhanced VLA [1]. - The "Autonomous Driving VLA and Large Model Practical Course" focuses on foundational theories and practical applications, including Vision/Language/Action modules, reinforcement learning, and diffusion models, with a special section on building VLA models and datasets from scratch [1]. Instructor Team - The course is led by experts from both academia and industry, including individuals with extensive research and practical experience in multimodal perception, autonomous driving VLA, and large model frameworks [6][8][11]. Target Audience - The courses are aimed at individuals with a foundational understanding of autonomous driving, familiarity with key technologies such as transformer models and reinforcement learning, and a basic knowledge of probability and linear algebra [12][13].

Group 1 - The article emphasizes the increasing demand for corporate training and job counseling in the autonomous driving sector, highlighting the need for diverse training programs ranging from technology updates to industry development summaries [2] - There is a notable interest from individuals seeking guidance, particularly those struggling with resume enhancement and project experience [3] - The company is actively seeking collaboration with professionals in the autonomous driving field to enhance training services, course development, and research guidance [4] Group 2 - The company offers competitive compensation and access to extensive industry resources, focusing on various areas such as autonomous driving product management, data annotation, world models, and reinforcement learning [5] - The primary target for training collaborations includes enterprises, universities, and research institutions, as well as students and job seekers [6] - Interested parties are encouraged to reach out for further consultation via WeChat [7]

Group 1 - The article emphasizes the increasing demand for corporate training and job counseling in the autonomous driving sector, highlighting the need for various training programs and industry insights [2][4] - There is a specific focus on assisting individuals who struggle with their resumes and require project experience and guidance [3] - The company is inviting professionals in the autonomous driving field to collaborate on technical services, training, course development, and research guidance [4][5] Group 2 - The main areas of collaboration include roles such as autonomous driving product managers, 4D annotation/data closure, world models, VLA, autonomous driving large models, reinforcement learning, and end-to-end solutions [5] - The job description targets both B-end (corporate and academic training) and C-end (students and job seekers) for training cooperation, course development, and original article creation [6] - Interested parties are encouraged to reach out for further consultation via WeChat [7]