The article describes a workflow for generating lane‑level connection relationships in high‑precision navigation data using the NDS standard, detailing how lane groups and connector IDs are assigned uniquely within tiles and across a 9‑tile neighborhood for both NDS 2.5.2 and 2.5.4 versions.
The article reviews the essential infrastructure for autonomous driving, covering massive sensor data storage strategies, the role of metadata, offline and real‑time computing platforms, basic micro‑service components, and various business scenarios, highlighting why robust big‑data handling is critical.
The article traces the evolution of autonomous driving from early concepts to modern implementations, highlighting Pony.ai’s technical innovations in sensor fusion, high‑definition mapping, simulation, data processing, software iteration, and the challenges of scaling vehicle fleets for commercial deployment.
The article explains how visual‑inertial sensor fusion, combined with GNSS and LiDAR, enables large‑scale production of high‑precision maps, detailing hardware choices, processing pipelines, Gaode's implementation, current challenges, and future directions toward multi‑source data integration.
This article outlines the high‑precision positioning requirements for autonomous vehicles and reviews the core technologies—including INS, GNSS/RTK, high‑definition maps, wheel sensors, and multi‑sensor fusion with Kalman filtering—detailing their principles, challenges, and typical deployment scenarios.
This article outlines Pony.ai's perception pipeline, comparing traditional and deep‑learning approaches for 2D and 3D object detection, detailing sensor fusion, detection methods, challenges such as occlusion and distance estimation, and how hybrid techniques improve accuracy for autonomous driving.
At CVPR 2019, Didi’s technology team co‑hosted an autonomous‑driving workshop, showcased the D²‑City dataset, and secured second place in the AI City Challenge by introducing a modular multi‑camera tracking framework, a CNN‑based single‑camera tracker, and a staged aggregation strategy, while outlining its hybrid dispatch commercial plan.
The talk explains how massive, well‑annotated data fuels autonomous‑driving AI, covering data annotation metrics, team structure, efficiency‑boosting techniques, system stability, and how data‑driven development and decision‑making improve model training, evaluation, and product priorities.
The article explains cloud computing fundamentals, highlights its limitations, introduces edge and mobile edge computing (MEC) as complementary solutions, and details how MEC reduces latency and bandwidth usage to enable advanced use cases such as autonomous driving in the 5G era.
The article presents a comprehensive overview of Pony.ai's autonomous driving infrastructure, covering the core infrastructure team’s responsibilities, vehicle onboard systems, simulation platform, data architecture, and supporting services, while discussing the technical challenges and engineering practices employed to achieve scalability, reliability, and high performance.
This article presents a comprehensive overview of high‑definition mapping for autonomous vehicles, covering topological and 3D grid maps, the data‑collection and processing pipeline, key challenges such as cost and scalability, and detailed discussions of SLAM, pose‑graph optimization, ICP, and multi‑sensor localization techniques.
This article presents a comprehensive overview of autonomous driving perception, covering system fundamentals, sensor setups and fusion techniques, onboard processing architecture, and the key technical challenges such as precision‑recall balance, adverse weather, and small‑object detection.
This article reviews the evolution of autonomous‑driving research, explains how to design end‑to‑end deep‑neural‑network models such as PilotNet, and outlines a reinforcement‑learning based decision system, highlighting key architectures, performance metrics, and future challenges.
This article presents a comprehensive overview of autonomous‑vehicle trajectory planning, covering its fundamental concepts, optimization formulation, decision‑making strategies, lateral and longitudinal planning methods, and the practical challenges faced in real‑world deployments.
This comprehensive article explains reinforcement learning fundamentals, compares it with supervised learning, surveys Baidu's industrial RL applications such as recommendation, dialogue, prosthetics, and autonomous driving, introduces the open‑source PARL platform, and discusses current challenges and future research directions.
Alibaba DAMO Academy outlines ten 2019 technology trends—from smart city real‑time simulation and voice AI passing Turing tests to AI‑specific chips, massive graph neural networks, re‑architected computing, 5G‑driven applications, digital identity, autonomous driving, blockchain rationalization, and emerging data‑security technologies.
This article explores how autonomous driving, driven by artificial intelligence, can dramatically improve safety, convenience, efficiency, and reduce congestion, outlines the five SAE levels, describes the three-layer control architecture, and explains key AI tools such as occupancy grids and cones of uncertainty that enable precise trajectory planning.
The 2018 AI Challenger, co‑hosted by Meituan, Innovation Works, Sogou and Meitu, launched with over 3 million RMB in prizes, featured two flagship tracks—fine‑grained restaurant review sentiment analysis and autonomous‑driving visual perception—offering massive new datasets, multi‑task learning challenges, and concrete applications that illustrate how AI can reshape everyday services.
This article examines the rapid growth of cloud computing in China and globally, analyzing public cloud market share, major providers, investment trends, sector-specific forecasts, and the impact of emerging technologies such as AI, IoT, big data, and 5G on future cloud services.
In an interview, Intel senior VP Mike Mayberry explains that quantum computers are not a universal replacement for classical CPUs, outlines the next decade of commercialization, highlights material simulation and cryptography as key applications, and discusses challenges, AI data efficiency, and safety in autonomous driving.
In early 2018, twelve Alibaba scientists forecast how frontier technologies such as AI, quantum computing, IoT, edge computing, blockchain, autonomous driving, computer vision and speech interaction will impact society, industry, and daily life, highlighting key challenges, opportunities, and expected breakthroughs across these fields.
This article provides a detailed overview of autonomous driving, covering its evolution from electric and shared vehicles, major industry players, technical definitions, SAE level classifications, core modules such as perception, localization, decision and control, key datasets like KITTI, and emerging business opportunities in the sector.
The article traces the departure of over twenty senior AI experts from Baidu, detailing their backgrounds, the roles they held, and the startups or companies they joined, illustrating how their moves have shaped China’s AI landscape across autonomous driving, computer vision, speech, and other emerging technologies.
