Core Insights - Embodied intelligence is a hot topic this year, transitioning from previous years' silence to last year's frenzy, and now gradually cooling down as the industry realizes that embodied robots are far from being productive [1] Group 1: Industry Trends - The demand for multi-sensor fusion and positioning in robotics is significant, with a focus on SLAM and ROS technologies [3] - Many robotics companies are rapidly developing and have secured considerable funding, indicating a promising future for the sector [3] - Traditional robotics remains the main product line, despite the excitement around embodied intelligence [3] Group 2: Community and Resources - The community has established a closed loop across various fields including industry, academia, and job seeking, aiming to create a valuable exchange platform [4][6] - The community offers access to over 40 technical routes and invites industry leaders for discussions, enhancing learning and networking opportunities [6][20] - Members can freely ask questions regarding job choices or research directions, receiving guidance from experienced professionals [83] Group 3: Educational Content - Comprehensive resources for beginners and advanced learners are available, including technical stacks and learning roadmaps for autonomous driving and robotics [13][16] - The community has compiled a list of notable domestic and international research labs and companies in the autonomous driving and robotics sectors, aiding members in their academic and career pursuits [27][29]

Core Viewpoint - End-to-End Autonomous Driving (E2E) is identified as the core algorithm for intelligent driving mass production, with significant advancements and competition emerging in the industry following the recognition of UniAD at CVPR [2][3] Group 1: E2E Autonomous Driving Overview - E2E systems directly model the relationship between sensor inputs and vehicle control information, avoiding error accumulation seen in traditional modular approaches [2] - The introduction of BEV perception has bridged gaps between modular methods, leading to a significant technological leap [2] - The emergence of various algorithms indicates that UniAD is not the ultimate solution for E2E, highlighting the rapid development in this field [2] Group 2: Learning Challenges in E2E - The fast-paced development in E2E technology has made previous educational resources inadequate, necessitating a comprehensive understanding of multiple domains such as multimodal large models, BEV perception, and reinforcement learning [3][4] - Beginners face challenges due to fragmented knowledge and the overwhelming volume of literature, often leading to abandonment before mastering the concepts [3] Group 3: Course Development - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address learning challenges, focusing on practical and theoretical integration [4][5][6] - The course aims to provide a structured framework for understanding E2E research and enhance research capabilities by categorizing papers and extracting innovative points [5] Group 4: Course Structure - The course includes five chapters covering topics from the introduction of E2E algorithms to practical applications involving RLHF fine-tuning [9][10][11][12][13] - Key areas of focus include the evolution of E2E paradigms, the significance of VLA in the current landscape, and practical implementations of diffusion models [11][12] Group 5: Expected Outcomes - Participants are expected to achieve a level equivalent to one year of experience as an E2E autonomous driving algorithm engineer, mastering various methodologies and key technologies [18] - The course aims to facilitate the application of learned concepts in real-world projects, enhancing employability in the autonomous driving sector [18]

Core Viewpoint - The article emphasizes the ongoing evolution and critical phase of the autonomous driving industry, highlighting the transition from modular approaches to end-to-end/VLA methods, and the community's commitment to fostering knowledge and collaboration in this field [2][4]. Group 1: Industry Development - Since Google's initiation of autonomous driving technology research in 2009, the industry has progressed significantly, now entering a crucial phase of development [2]. - The community aims to integrate intelligent driving into daily transportation, reflecting a growing expectation for advancements in autonomous driving capabilities [2]. Group 2: Community Initiatives - The community has established a knowledge-sharing platform, offering resources across various domains such as industry insights, academic research, and job opportunities [2][4]. - Plans to enhance community engagement include monthly online discussions and roundtable interviews with industry and academic leaders [2]. Group 3: Educational Resources - The community has compiled over 40 technical routes to assist individuals at different levels, from beginners to those seeking advanced knowledge in autonomous driving [4][16]. - A comprehensive entry-level technical stack and roadmap have been developed for newcomers to the field [9]. Group 4: Job Opportunities and Networking - The community has established internal referral mechanisms with multiple autonomous driving companies, facilitating job placements for members [7][14]. - Continuous job sharing and networking opportunities are provided to create a complete ecosystem for autonomous driving professionals [14][80]. Group 5: Research and Technical Focus - The community has gathered extensive resources on various research areas, including 3D target detection, BEV perception, and multi-sensor fusion, to support practical applications in autonomous driving [16][30][32]. - Detailed summaries of cutting-edge topics such as end-to-end driving, world models, and visual language models (VLM) have been compiled to keep members informed about the latest advancements [34][40][42].

Core Viewpoint - The article highlights the launch of the Li Auto i8, which features significant upgrades in its driver assistance capabilities, particularly through the integration of the VLA (Vision-Language-Action) model, marking a milestone in the mass production of autonomous driving technology [2][3]. Summary by Sections VLA Model Capabilities - The VLA model enhances understanding of semantics through multimodal input, improves reasoning with a thinking chain, and aligns more closely with human driving intuition. Its four core capabilities include spatial understanding, reasoning ability, communication and memory, and behavioral ability [3][6]. Industry Development - The VLA represents a new milestone in the mass production of autonomous driving, with many companies investing in human resources for research and development. The transition from E2E (End-to-End) and VLM (Vision-Language Model) to VLA indicates a progressive technological evolution [5][8]. Educational Initiatives - In response to the growing interest in transitioning to VLA-related roles, the industry has launched a specialized course titled "End-to-End and VLA Autonomous Driving Small Class," aimed at providing in-depth knowledge of the algorithms and technical development in this field [7][15]. Course Structure and Content - The course covers various aspects of end-to-end algorithms, including historical development, background knowledge, and specific methodologies such as two-stage and one-stage end-to-end approaches. It emphasizes practical applications and theoretical foundations [21][22][23][24]. Job Market Insights - The demand for VLA/VLM algorithm experts is high, with salary ranges for positions varying based on experience and educational background. For instance, positions for VLA/VLM algorithm engineers typically offer salaries between 35K to 70K for those with 3-5 years of experience [11]. Learning Outcomes - Participants in the course are expected to achieve a level of understanding equivalent to that of an autonomous driving algorithm engineer with one year of experience, covering key technologies such as BEV perception, multimodal models, and reinforcement learning [32].

Core Viewpoint - The article discusses the development of FastDriveVLA, a novel framework for visual token pruning in autonomous driving, achieving a 50% compression rate while maintaining 97.3% performance [2][3][43]. Group 1: End-to-End Autonomous Driving - Recent advancements in end-to-end autonomous driving research have led to the adoption of end-to-end methods that complete perception to planning in a single model, reducing information loss between modules [3]. - The introduction of Visual-Language-Action (VLA) models enhances decision-making in complex scenarios, making them increasingly popular in autonomous driving systems [3][10]. Group 2: Visual Token Pruning - Existing VLM/VLA models encode images into numerous visual tokens, resulting in high computational costs. Current research explores two main directions for visual token pruning: attention mechanism-based methods and similarity-based methods [4][14]. - FastDriveVLA proposes a reconstruction-based visual token pruning framework that focuses on retaining tokens related to foreground information, significantly reducing computational costs while maintaining performance [5][13]. Group 3: FastDriveVLA Framework - FastDriveVLA includes a plug-and-play pruner called ReconPruner, trained using a pixel reconstruction task to focus on foreground areas and assign higher significance scores to key tokens [6][17]. - The framework utilizes a large-scale dataset, nuScenes-FG, containing 241,000 image-mask pairs for training, enhancing the model's ability to distinguish between foreground and background [6][12]. Group 4: Experimental Results - FastDriveVLA achieved state-of-the-art results on the nuScenes closed-loop planning benchmark, demonstrating its effectiveness and practicality [13][34]. - The framework shows superior performance compared to existing methods, with improvements in L2 error and collision rates at various pruning ratios [30][34]. Group 5: Efficiency Analysis - FastDriveVLA significantly reduces FLOPs by approximately 7.5 times and decreases prefill and decode latencies, enhancing inference efficiency for real-time deployment [36][40]. - The lightweight design of ReconPruner allows for lower CUDA latency compared to several similar methods, making it suitable for practical applications [36][40].

Core Viewpoint - The article discusses the ReAL-AD framework, which integrates human-like reasoning into end-to-end autonomous driving systems, enhancing decision-making processes through a structured approach that mimics human cognitive functions [3][43]. Group 1: Framework Overview - ReAL-AD employs a reasoning-enhanced learning framework based on a three-layer human cognitive model: driving strategy, decision-making, and operation [3][5]. - The framework incorporates a visual-language model (VLM) to improve environmental perception and structured reasoning capabilities, allowing for a more nuanced decision-making process [3][5]. Group 2: Components of ReAL-AD - The framework consists of three main components: 1. **Strategic Reasoning Injector**: Utilizes VLM to generate insights for complex traffic situations, forming high-level driving strategies [5][11]. 2. **Tactical Reasoning Integrator**: Converts strategic intentions into executable tactical choices, bridging the gap between strategy and operational decisions [5][14]. 3. **Hierarchical Trajectory Decoder**: Simulates human decision-making by establishing rough motion patterns before refining them into detailed trajectories [5][20]. Group 3: Performance Evaluation - In open-loop evaluations, ReAL-AD demonstrated significant improvements over baseline methods, achieving over 30% better performance in L2 error and collision rates [36]. - The framework achieved the lowest average L2 error of 0.48 meters and a collision rate of 0.15% on the nuScenes dataset, indicating enhanced learning efficiency in driving capabilities [36]. - Closed-loop evaluations showed that the introduction of the ReAL-AD framework significantly improved driving scores and successful path completions compared to baseline models [37]. Group 4: Experimental Setup - The evaluation utilized the nuScenes dataset, which includes 1,000 scenes sampled at 2Hz, and the Bench2Drive dataset, covering 44 scenarios and 23 weather conditions [34]. - Metrics for evaluation included L2 error, collision rates, driving scores, and success rates, providing a comprehensive assessment of the framework's performance [35][39]. Group 5: Ablation Studies - Ablation studies indicated that removing the Strategic Reasoning Injector led to a 12% increase in average L2 error and a 19% increase in collision rates, highlighting its importance in guiding decision-making [40]. - The Tactical Reasoning Integrator was shown to reduce average L2 error by 0.14 meters and collision rates by 0.05%, emphasizing the value of tactical commands in planning [41]. - Replacing the Hierarchical Trajectory Decoder with a multi-layer perceptron resulted in increased L2 error and collision rates, underscoring the necessity of a hierarchical decoding process for trajectory prediction [41].

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLM/VLA systems leading to high demand for related positions and salaries reaching up to 1 million annually [2][11]. Group 1: Industry Trends - The concept of E2E has evolved significantly, with various technical schools emerging, yet many still struggle to understand its workings and distinctions between single-stage and two-stage approaches [2][4]. - The introduction of VLA (Vision-Language Architecture) is seen as a new frontier in autonomous driving, with companies actively researching and developing new generation mass production solutions [21][22]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been launched to address the challenges faced by newcomers in the field, focusing on practical applications and theoretical foundations [14][27]. - The course aims to provide a comprehensive understanding of E2E autonomous driving, covering various models and methodologies, including diffusion models and reinforcement learning [6][19][21]. Group 3: Job Market Insights - The job market for VLA/VLM algorithm experts is robust, with salaries for positions requiring 3-5 years of experience ranging from 40K to 70K monthly, indicating a strong demand for skilled professionals [11][12]. - Positions such as VLA model quantization deployment engineers and multi-modal VLA model direction experts are particularly sought after, reflecting the industry's shift towards advanced algorithmic solutions [11][12].

Core Viewpoint - The article discusses the launch of the Li Auto i8, highlighting its significant upgrades in assisted driving features and the introduction of the VLA (Vision-Language-Action) model, marking a milestone in the development of end-to-end autonomous driving technology [2][4]. Summary by Sections VLA Model Capabilities - The VLA model enhances three main capabilities: better semantic understanding (multimodal input), improved reasoning (thinking chains), and closer alignment with human driving intuition. It focuses on four core abilities: spatial understanding, reasoning ability, communication and memory ability, and behavioral ability [4][5]. Industry Outlook - The demand for VLA/VLM model algorithm experts is projected to be high, with salaries ranging from 40K to 70K for those with 3-5 years of experience and a master's degree. Top technical talents, especially PhD graduates, can expect salaries between 90K to 120K [13]. Learning Challenges - The article outlines the challenges faced by newcomers in the field of end-to-end autonomous driving, including the complexity of the technology stack and the fragmented nature of available knowledge. It emphasizes the need for a structured learning path to navigate the vast amount of literature and practical applications [16][17]. Course Introduction - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the learning challenges. The course aims to provide a quick entry into the field, build a framework for research capabilities, and combine theory with practical applications [17][18][19]. Course Structure - The course consists of several chapters covering the history and development of end-to-end autonomous driving, background knowledge on relevant technologies, and detailed discussions on various paradigms such as one-stage and two-stage end-to-end methods. It also includes practical assignments to reinforce learning [23][24][25][26]. Expected Outcomes - Upon completion of the course, participants are expected to reach a level equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, with a solid understanding of key technologies and the ability to apply learned concepts to real-world projects [33].

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in the intelligent driving sector, with significant advancements in VLM (Vision-Language Model) and VLA (Vision-Language Architecture) systems driving the industry forward [2][3]. Group 1: Industry Trends - The E2E approach has become a competitive focus for domestic new energy vehicle manufacturers, with the emergence of VLA concepts leading to a new wave of production scheme iterations [2]. - Salaries for positions related to VLM/VLA are reported to reach up to one million annually, with monthly salaries around 70K [2]. - The rapid development of technology has made previous solutions inadequate, necessitating a comprehensive understanding of various technical fields such as multimodal large models, BEV perception, reinforcement learning, and diffusion models [3][4]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the challenges faced by learners in this complex field, focusing on practical applications and theoretical foundations [4][5][6]. - The course aims to provide a structured learning path, helping students build a framework for research and enhance their research capabilities by categorizing papers and extracting innovative points [5]. - Practical components are included to ensure a complete learning loop from theory to application, addressing the gap between academic knowledge and real-world implementation [6]. Group 3: Course Structure - The course is divided into several chapters, covering topics such as the history and evolution of E2E algorithms, background knowledge on relevant technologies, and detailed explorations of both one-stage and two-stage E2E methods [9][10][11]. - Key areas of focus include the introduction of various E2E paradigms, the significance of world models, and the application of diffusion models in trajectory prediction [11][12]. - The final chapter includes a major project on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, allowing students to apply their knowledge in practical scenarios [13]. Group 4: Target Audience and Outcomes - The course is designed for individuals with a foundational understanding of autonomous driving and related technologies, aiming to elevate their expertise to a level comparable to that of an E2E autonomous driving algorithm engineer within a year [20]. - Participants will gain a comprehensive understanding of E2E frameworks, including one-stage, two-stage, world models, and diffusion models, as well as deeper insights into key technologies like BEV perception and multimodal large models [20].

Core Viewpoint - The article discusses the current development status of end-to-end autonomous driving algorithms, comparing them with traditional algorithms and highlighting their advantages and limitations [1][3][53]. Summary by Sections Traditional vs. End-to-End Algorithms - Traditional autonomous driving algorithms follow a pipeline of perception, prediction, and planning, where each module has distinct inputs and outputs [3]. - End-to-end algorithms take raw sensor data as input and directly output path points, simplifying the process and reducing error accumulation [3][5]. - Traditional algorithms are easier to debug and have some level of interpretability, but they suffer from cumulative error issues due to the inability to ensure complete accuracy in perception and prediction modules [3][5]. Limitations of End-to-End Algorithms - End-to-end algorithms face challenges such as limited ability to handle corner cases, as they rely heavily on data-driven methods [7][8]. - The use of imitation learning in these algorithms can lead to difficulties in learning optimal ground truth and handling exceptional cases [53]. - Current end-to-end paradigms include imitation learning (behavior cloning and inverse reinforcement learning) and reinforcement learning, with evaluation methods categorized into open-loop and closed-loop [8]. Current Implementations - The ST-P3 algorithm is highlighted as an early work focusing on end-to-end autonomous driving, utilizing a framework that includes perception, prediction, and planning modules [10][11]. - Innovations in the ST-P3 algorithm include a perception module that uses a self-centered cumulative alignment technique and a prediction module that employs a dual-path prediction mechanism [11][13]. - The planning phase of ST-P3 optimizes predicted trajectories by incorporating traffic light information [14][15]. Advanced Techniques - The UniAD system employs a full Transformer framework for end-to-end autonomous driving, integrating multiple tasks to enhance performance [23][25]. - The TrackFormer framework focuses on the collaborative updating of track queries and detect queries to improve prediction accuracy [26]. - The VAD (Vectorized Autonomous Driving) method introduces vectorized representations for better structural information and faster computation in trajectory planning [32][33]. Future Directions - The article suggests that end-to-end algorithms still primarily rely on imitation learning frameworks, which have inherent limitations that need further exploration [53]. - The introduction of more constraints and multi-modal planning methods aims to address trajectory prediction instability and improve model performance [49][52].