Key Highlights & Growth - Pony AI produced over 200 Gen-7 vehicles as of August 11, 2025 [7] - The company aims to produce over 1,000 vehicles by the end of 2025 [7, 22] - Registered user growth increased by 136% year-over-year from 2Q24 to 2Q25 [7, 32] - Total revenue grew by 76% in 2Q25 [7] - Fare-charging revenue experienced a growth of over 300% in 2Q25 [7, 35, 70] Commercialization & Operations - Pony AI is the only company with fully driverless commercial licenses in all four Tier-1 cities in China (Beijing, Shanghai, Guangzhou, Shenzhen) [20, 31] - Robotaxis receive approximately 15 average daily orders [20] - Accumulated autonomous driving kilometers reached 488 million+ as of June 30, 2025 [36] - Accumulated autonomous driverless kilometers reached 87 million+ as of June 30, 2025 [36] Financial Performance - Robotaxi services revenue increased by 1578% from $06 million in 2Q24 to $15 million in 2Q25 [65] - Total revenue increased by 9018% from $122 million in 2Q24 to $104 million in 2Q25 [69]

Core Viewpoint - The article discusses the ongoing relevance of trajectory prediction in the context of end-to-end models, highlighting that many companies still utilize layered approaches where trajectory prediction remains a key algorithmic focus. The article emphasizes the significance of multi-agent trajectory prediction methods based on diffusion models, which are gaining traction in various applications such as autonomous driving and intelligent monitoring [1][2]. Group 1: Trajectory Prediction Research - Despite the rise of end-to-end models, trajectory prediction continues to be a hot research area, with significant output in conferences and journals [1]. - Multi-agent trajectory prediction aims to forecast future movements based on historical trajectories of multiple interacting agents, which is crucial in fields like autonomous driving and robotics [1]. - Traditional methods often struggle with the uncertainty and multimodality of human behavior, while generative models like GANs and CVAEs, although capable of simulating multimodal distributions, lack efficiency [1]. Group 2: Diffusion Models - Diffusion models have emerged as a new class of models that achieve complex distribution generation through gradual denoising, showing significant breakthroughs in image generation and other fields [2]. - The Leapfrog Diffusion Model (LED) enhances real-time prediction by reducing denoising steps, achieving a 19-30 times speedup while improving accuracy on various datasets [2]. - Mixed Gaussian Flow (MGF) and Pattern Memory-based Diffusion Model (MPMNet) are also highlighted for their advanced performance in trajectory prediction by better matching multimodal distributions and utilizing human motion patterns, respectively [2]. Group 3: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping students integrate theoretical knowledge with practical coding skills [6]. - It addresses common challenges faced by students, such as lack of direction and difficulties in reproducing research papers, by offering a structured approach to model development and academic writing [6]. - The course includes a comprehensive curriculum that covers classic and cutting-edge papers, coding implementations, and writing methodologies, ultimately guiding students to produce a draft of a research paper [6][9]. Group 4: Target Audience and Requirements - The course is designed for graduate students and professionals in trajectory prediction and autonomous driving, aiming to enhance their research capabilities and resume value [8]. - Participants are expected to have a foundational understanding of deep learning and familiarity with Python and PyTorch [10]. - The course emphasizes the importance of academic integrity and active participation, with specific requirements for attendance and assignment completion [15]. Group 5: Course Highlights and Outcomes - The program features a "2+1" teaching model with experienced instructors providing comprehensive support throughout the learning process [16][17]. - Students will gain access to datasets, baseline codes, and essential papers, facilitating a deeper understanding of the subject matter [20][21]. - Upon completion, students will have produced a research paper draft, a project completion certificate, and potentially a recommendation letter based on their performance [19].

Core Insights - The article discusses advancements in autonomous driving technologies, highlighting various frameworks and their contributions to improving safety, efficiency, and robustness in real-world scenarios. Group 1: DRIVE Framework - The DRIVE framework proposed by Stanford University and Microsoft integrates dynamic rule inference and verified evaluation for constraint-aware autonomous driving, achieving a 0.0% soft constraint violation rate and enhancing trajectory smoothness and generalization capabilities [2][6]. Group 2: Hybrid Learning-Optimization Framework - A hybrid learning-optimization trajectory planning framework developed by Beijing Jiaotong University and Hainan University achieves a 97% success rate and real-time planning performance of 54 milliseconds in highway scenarios [11][12]. Group 3: RoboTron-Sim - The RoboTron-Sim framework, developed by Meituan and Sun Yat-sen University, enhances the robustness of autonomous driving in extreme scenarios, achieving a 51.3% reduction in collision rates and a 51.5% improvement in trajectory accuracy on the nuScenes test [18][20]. Group 4: SAV Framework - The SAV framework proposed by Anhui University achieves high-precision vehicle part segmentation with an 81.23% mean Intersection over Union (mIoU) on the VehicleSeg10K dataset, surpassing previous best methods by 4.33% [34][40].

Core Viewpoint - The article discusses the advancements in autonomous driving technology, particularly focusing on the LLaMA Factory framework and the Qwen2.5-VL model, which enhance the capabilities of vision-language-action models for autonomous driving applications [4][5]. Group 1: LLaMA Factory Overview - LLaMA Factory is an open-source low-code framework for fine-tuning large models, gaining popularity in the open-source community with over 40,000 stars on GitHub [3]. - The framework integrates widely used fine-tuning techniques, making it suitable for developing autonomous driving assistants that can interpret traffic conditions through natural language [3]. Group 2: Qwen2.5-VL Model - The Qwen2.5-VL model serves as the foundational model for the project, achieving significant breakthroughs in visual recognition, object localization, document parsing, and long video understanding [4]. - It offers three model sizes, with the flagship Qwen2.5-VL-72B performing comparably to advanced models like GPT-4o and Claude 3.5 Sonnet, while smaller versions excel in resource-constrained environments [4]. Group 3: CoVLA Dataset - The CoVLA dataset, comprising 10,000 real driving scenes and over 80 hours of video, is utilized for training and evaluating vision-language-action models [5]. - This dataset surpasses existing datasets in scale and annotation richness, providing a comprehensive platform for developing safer and more reliable autonomous driving systems [5]. Group 4: Model Training and Testing - Instructions for downloading and installing LLaMA Factory and the Qwen2.5-VL model are provided, including commands for setting up the environment and testing the model [6][7]. - The article details the process of fine-tuning the model using the SwanLab tool for visual tracking of the training process, emphasizing the importance of adjusting parameters to avoid memory issues [11][17]. - After training, the fine-tuned model demonstrates improved response quality in dialogue scenarios related to autonomous driving risks compared to the original model [19].

据了解，萝卜快跑是百度Apollo自动驾驶出行服务平台。2022年6月10日，萝卜快跑正式在重庆永川区 投入运营。截至2025年3月，百度"萝卜快跑"自动驾驶出行服务平台，已在永川建立近4000多个站点， 运营面积超130平方公里。 凤凰网科技讯 8月7日，多名网友发布视频称，重庆永川区试运营的"萝卜快跑"无人驾驶网约车在行驶 中坠入市政施工沟槽。视频显示，车辆部分陷入坑内，车内女乘客在工作人员和群众帮助下脱困。截至 目前，萝卜快跑方面尚未就此事作出回应。 ...

[Music] Good evening everyone. So today I'll be talking about learning for a world that does not exist. Of course I'm coming it from the technical point of view.Today we are living in a world which is technically very dynamic. We are producing a lot of techni technological advancement and for young engineers and aspirants of engineering. Uh this can be a very challenging moment because um by the time we finish our education new industries emerge, lots of industries saturate and we need to be prepared by the ...

Core Viewpoint - The article emphasizes the importance of preparing for non-technical interview questions in the autonomous driving industry, highlighting the need for candidates to articulate their interests, communication skills, and learning abilities effectively [6][10][11]. Group 1: Interview Preparation - Candidates should reflect on their interests and experiences to answer open-ended questions, as interviewers are often looking for personal insights and opinions [6][10]. - Communication skills are crucial; candidates should demonstrate their ability to engage with mentors and express their thought processes when seeking guidance [6][10]. - Learning ability is assessed through candidates' approaches to acquiring new technical knowledge, emphasizing the importance of establishing a comprehensive understanding before diving into specifics [7][10]. Group 2: Community and Resources - The "Autonomous Driving Heart Knowledge Planet" community provides a platform for technical exchange, featuring members from renowned universities and leading companies in the autonomous driving sector [23][11]. - The community offers a wealth of resources, including over 40 technical routes and numerous open-source projects, aimed at facilitating learning and career development in the autonomous driving field [11][19]. - Members can access job opportunities and industry insights, fostering a complete ecosystem for autonomous driving professionals [21][22]. Group 3: Learning and Development - The community has curated a comprehensive learning path for beginners and advanced researchers, covering various aspects of autonomous driving technology [17][19]. - Regular discussions and Q&A sessions are held to address common industry challenges and share knowledge on emerging technologies [24][87]. - The platform also features live sessions with industry experts, providing members with direct access to cutting-edge research and practical applications [86][11].

Core Viewpoint - WeRide has received approval for late-night testing of its Robotaxi in Beijing, marking a significant step towards establishing a 24/7 autonomous ride-hailing network in the city [1][6] Group 1: Testing and Technology - The approval allows testing from 10pm to 7am in the Beijing High-Level Autonomous Driving Demonstration Zone, which is crucial for developing all-weather, all-day autonomous mobility services [1][6] - WeRide's Robotaxi is equipped with over 20 sensors, including high-precision cameras and LiDARs, achieving 360-degree coverage with a detection range of up to 200 meters, ensuring stable perception and rapid decision-making in low-light conditions [3][4] - The company has conducted Robotaxi testing or operations in 10 cities across four countries, accumulating over 2,200 days of safe open-road experience [5] Group 2: Challenges and Solutions - Night-time road conditions in Beijing present challenges such as low lighting and environmental interference, which require advanced perception and decision-making capabilities [2] - WeRide addresses visibility issues with a proprietary multi-sensor fusion algorithm and a high-performance computing platform, ensuring effective sensor fusion under adverse conditions [3] - The company employs automotive-grade sensors and a smart sensor cleaning system to maintain reliable perception in extreme weather conditions [4] Group 3: Future Outlook - The launch of 24/7 Robotaxi testing in Beijing validates WeRide's technology and safety systems, enhancing public transportation availability during off-peak hours [6] - WeRide aims to leverage its full-stack autonomous driving technology to expand its ride-hailing services and contribute to smarter, more sustainable urban transportation [7] - The company is recognized as a leader in the autonomous driving industry, being the first publicly traded Robotaxi company and having received permits in six markets [9]

Core Insights - The article discusses advancements in trajectory prediction using a generative active learning framework called GALTraj, which applies controllable diffusion models to address long-tail issues in data [1][2]. Group 1: GALTraj Framework - GALTraj is the first framework to apply generative active learning to trajectory prediction tasks, enhancing long-tail learning without modifying the model structure [2]. - The framework employs a tail-aware generation method that differentiates the diffusion guidance for tail, head, and related agents, producing realistic and diverse scenarios while preserving tail characteristics [2][3]. Group 2: Experimental Results - In experiments on WOMD and Argoverse2 datasets, GALTraj significantly improved long-tail sample prediction performance, reducing the long-tail metric FPR₅ by 47.6% (from 0.42 to 0.22) and overall prediction error minFDE₆ by 14.7% (from 0.654 to 0.558) [1][6]. - The results indicate that GALTraj outperforms traditional methods across various metrics, showcasing its effectiveness in enhancing prediction accuracy for rare scenarios [7][8]. Group 3: TopoLiDM Framework - The article also highlights the TopoLiDM framework developed by Shanghai Jiao Tong University and Twente University, which integrates topology-aware diffusion models for high-fidelity LiDAR point cloud generation [13][15]. - TopoLiDM achieved a 22.6% reduction in the Fréchet Range Image Distance (FRID) and a 9.2% reduction in Minimum Matching Distance (MMD) on the KITTI-360 dataset while maintaining a real-time generation speed of 1.68 samples per second [13][15]. Group 4: FastDriveVLA Framework - FastDriveVLA, developed by Peking University and Xiaopeng Motors, introduces a reconstruction-based visual token pruning framework that maintains 99.1% trajectory accuracy with a 50% pruning rate and reduces collision rates by 2.7% [21][22]. - The framework employs a novel adversarial foreground-background reconstruction strategy to enhance the identification of valuable tokens, achieving state-of-the-art performance on the nuScenes open-loop planning benchmark [27][28]. Group 5: PLA Framework - The article presents a unified Perception-Language-Action (PLA) framework proposed by TUM, which integrates multi-sensor fusion and GPT-4.1 enhanced visual-language-action reasoning for adaptive autonomous driving [34][35]. - The framework demonstrated a mean absolute error (MAE) of 0.39 m/s in speed prediction and an average displacement error (ADE) of 1.013 meters in trajectory tracking within urban intersection scenarios [42].

Core Viewpoint - The article emphasizes the convergence of autonomous driving technology, highlighting the shift from numerous diverse approaches to a more unified model, which indicates higher technical barriers in the industry [1] Group 1 - The industry is moving towards a unified solution with models like one model, VLM, and VLA, suggesting a reduction in the need for numerous algorithm engineers [1] - The article encourages the establishment of a large community to support industry professionals, facilitating growth and collaboration among peers [1] - A new job-related community is being launched to discuss industry trends, company developments, product research, and job opportunities [1]