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autonomous driving

136 articles · Page 2 of 2
DataFunTalk
DataFunTalk
Feb 27, 2020 · Artificial Intelligence

Technical Challenges in Planning and Control for Autonomous Heavy Trucks

The article reviews the complex system model of autonomous heavy trucks, outlines traditional and modern planning and control methods—including rule‑based FSM, POMDP, learning‑based and optimization techniques—highlights safety, efficiency, fuel‑economy, and dynamic modeling challenges specific to heavy‑truck and trailer configurations, and shares practical attempts such as lane‑changing, merging, and trailer‑aware trajectory planning.

ControlPOMDPautonomous driving
0 likes · 13 min read
Technical Challenges in Planning and Control for Autonomous Heavy Trucks
DataFunTalk
DataFunTalk
Feb 20, 2020 · Artificial Intelligence

Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations

This article reviews perception technologies used in autonomous heavy‑truck systems—including lane‑line detection, obstacle detection, and LiDAR sensing—detailing traditional and deep‑learning approaches, practical challenges on high‑speed highways, and the cost, performance, and reliability issues faced when moving these solutions to mass production.

LiDARautonomous drivingdeep learning
0 likes · 16 min read
Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations
DataFunTalk
DataFunTalk
Feb 13, 2020 · Artificial Intelligence

Deep Learning Techniques and Challenges in Autonomous Driving

This article reviews the rapid development of deep learning, its pivotal role in autonomous driving, outlines end‑to‑end perception‑to‑control pipelines, discusses the strengths and limitations of deep models, and proposes practical strategies such as task decomposition, multi‑method fusion, and sensor integration to improve safety and interpretability.

End-to-Endautonomous drivingcomputer vision
0 likes · 8 min read
Deep Learning Techniques and Challenges in Autonomous Driving
DataFunTalk
DataFunTalk
Feb 6, 2020 · Artificial Intelligence

L4 Autonomous Driving Heavy Truck: Architecture, Data Platform, and Production Challenges

This article presents a comprehensive overview of L4 autonomous driving heavy trucks, covering system architecture, sensor and computing hardware, data and model platforms, production challenges, safety considerations, and strategies for achieving reliable, high‑performance mass‑produced autonomous trucks.

AI safetyL4 trucksautonomous driving
0 likes · 12 min read
L4 Autonomous Driving Heavy Truck: Architecture, Data Platform, and Production Challenges
21CTO
21CTO
Dec 31, 2019 · Artificial Intelligence

Why Zhang Yaqin’s Move to Tsinghua Signals a New Era for AI Research

Zhang Yaqin, a distinguished AI pioneer and former Baidu and Microsoft executive, has joined Tsinghua University as an Intelligent Science Chair Professor, where he will lead research on autonomous driving, AI‑IoT integration, and the establishment of the university’s Intelligent Industry Research Institute.

Artificial IntelligenceTsinghua UniversityZhang Yaqin
0 likes · 8 min read
Why Zhang Yaqin’s Move to Tsinghua Signals a New Era for AI Research
DataFunTalk
DataFunTalk
Dec 3, 2019 · Artificial Intelligence

Hardware Technology Challenges and Solutions for Autonomous Driving

This article reviews the evolution of autonomous‑driving hardware, discusses key sensor technologies such as LiDAR and GNSS/IMU, outlines mechanical and electronic challenges—including size, weight, temperature, vibration, and electromagnetic interference—and presents Pony.ai’s PonyAlpha platform as a practical solution.

GNSSHardwarePonyAlpha
0 likes · 10 min read
Hardware Technology Challenges and Solutions for Autonomous Driving
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 19, 2019 · Artificial Intelligence

How Visual AI Powers Real-World Mapping and AR Navigation at Amap

This article explains how Amap leverages computer vision to collect, process, and enhance map data and to deliver low‑cost, real‑time AR navigation, detailing the technical challenges, algorithmic solutions, and the broader mission of connecting the physical world.

AIAR navigationautonomous driving
0 likes · 12 min read
How Visual AI Powers Real-World Mapping and AR Navigation at Amap
DataFunTalk
DataFunTalk
Nov 14, 2019 · Artificial Intelligence

Building the Most Reliable Autonomous Driving Infrastructure at Pony.ai

This article outlines Pony.ai's comprehensive autonomous driving infrastructure, describing traditional internet back‑end components, additional vehicle‑mounted systems, large‑scale simulation, data challenges, and the reliability, performance, and flexibility practices needed to support rapid growth and safe robotaxi operations.

AI systemsInfrastructurePony.ai
0 likes · 15 min read
Building the Most Reliable Autonomous Driving Infrastructure at Pony.ai
DataFunTalk
DataFunTalk
Nov 8, 2019 · Artificial Intelligence

Balancing Safety and Comfort in Autonomous Driving: Planning and Control Optimization

This article explores how autonomous driving systems can simultaneously ensure safety and passenger comfort by optimizing planning and control modules, defining safety and comfort metrics, formulating constraints and cost functions, and employing models such as the bicycle model for lateral and longitudinal control.

ControlSafetyautonomous driving
0 likes · 12 min read
Balancing Safety and Comfort in Autonomous Driving: Planning and Control Optimization
Architects' Tech Alliance
Architects' Tech Alliance
Oct 14, 2019 · Industry Insights

From ECU CPUs to ASICs: The Evolution of Automotive Chips for Autonomous Driving

This article traces the development of automotive electronic control units from early CPU‑centric ECUs to centralized domain controllers, examines the rise of GPU‑based AI accelerators for assisted driving, and explains why ASICs are expected to dominate future autonomous‑driving chips, while profiling key industry players and their strategies.

AI acceleratorsASICFPGA
0 likes · 21 min read
From ECU CPUs to ASICs: The Evolution of Automotive Chips for Autonomous Driving
DataFunTalk
DataFunTalk
Aug 13, 2019 · Artificial Intelligence

From L0 to L5: Building and Testing an Autonomous Driving System

This article explains how a conventional vehicle can be progressively upgraded through hardware retrofits, sensor integration, mapping, perception, control, and planning modules to achieve SAE Level 4/5 autonomy, using a step‑by‑step analogy with driver training and iterative testing.

AISoftware Engineeringautonomous driving
0 likes · 14 min read
From L0 to L5: Building and Testing an Autonomous Driving System
DataFunTalk
DataFunTalk
Aug 12, 2019 · Artificial Intelligence

Multi‑Sensor Fusion in Autonomous Driving: Challenges, Prerequisites, and Methods

Pony.ai shares its extensive experience on multi‑sensor perception for autonomous trucks, explaining why sensor fusion is needed, the essential motion‑compensation and calibration steps, and practical camera‑lidar and radar‑lidar fusion techniques that improve detection range and robustness.

CalibrationCameraLiDAR
0 likes · 15 min read
Multi‑Sensor Fusion in Autonomous Driving: Challenges, Prerequisites, and Methods
Amap Tech
Amap Tech
Jul 16, 2019 · Fundamentals

Lane-Level Connection Relationship in High‑Precision Navigation Data Based on NDS

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.

High Precision NavigationNDSautonomous driving
0 likes · 7 min read
Lane-Level Connection Relationship in High‑Precision Navigation Data Based on NDS
DataFunTalk
DataFunTalk
Jul 15, 2019 · Big Data

Key Infrastructure Considerations for Autonomous Driving: Storage, Computing, and Services

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.

Big DataReal-Time Computingautonomous driving
0 likes · 14 min read
Key Infrastructure Considerations for Autonomous Driving: Storage, Computing, and Services
DataFunTalk
DataFunTalk
Jul 2, 2019 · Artificial Intelligence

From Zero to Autonomous Driving: Pony.ai’s Technical Journey

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.

AIBig DataPony.ai
0 likes · 12 min read
From Zero to Autonomous Driving: Pony.ai’s Technical Journey
Amap Tech
Amap Tech
Jun 28, 2019 · Industry Insights

How Visual‑Inertial Fusion Powers High‑Precision Maps for Autonomous Driving

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.

Industry Insightsautonomous drivinghigh-precision maps
0 likes · 10 min read
How Visual‑Inertial Fusion Powers High‑Precision Maps for Autonomous Driving
DataFunTalk
DataFunTalk
Jun 26, 2019 · Artificial Intelligence

Pony.ai Perception System: Combining Traditional and Deep Learning Methods for 2D and 3D Object Detection

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.

3D detectionautonomous drivingobject detection
0 likes · 11 min read
Pony.ai Perception System: Combining Traditional and Deep Learning Methods for 2D and 3D Object Detection
Didi Tech
Didi Tech
Jun 22, 2019 · Artificial Intelligence

Didi’s Achievements and Innovations at CVPR 2019 AI City Challenge

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.

AI City ChallengeCVPRautonomous driving
0 likes · 6 min read
Didi’s Achievements and Innovations at CVPR 2019 AI City Challenge
DataFunTalk
DataFunTalk
May 30, 2019 · Artificial Intelligence

Data Annotation, Data‑Driven Development, and Decision‑Making in Autonomous Driving

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.

Artificial IntelligenceEfficiencyautonomous driving
0 likes · 9 min read
Data Annotation, Data‑Driven Development, and Decision‑Making in Autonomous Driving
DataFunTalk
DataFunTalk
May 10, 2019 · Artificial Intelligence

Pony.ai Infrastructure Overview: Vehicle Systems, Simulation Platform, and Data Architecture

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.

AIBig DataInfrastructure
0 likes · 14 min read
Pony.ai Infrastructure Overview: Vehicle Systems, Simulation Platform, and Data Architecture
DataFunTalk
DataFunTalk
May 9, 2019 · Artificial Intelligence

High‑Definition Maps and Localization for Autonomous Driving: Concepts, Pipeline, and Challenges

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.

3D grid mapHD mapICP
0 likes · 18 min read
High‑Definition Maps and Localization for Autonomous Driving: Concepts, Pipeline, and Challenges
DataFunTalk
DataFunTalk
May 8, 2019 · Artificial Intelligence

Perception System Overview: Sensors, Fusion, Onboard Architecture, and Technical Challenges in Autonomous Driving

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.

autonomous drivingcomputer visionperception
0 likes · 12 min read
Perception System Overview: Sensors, Fusion, Onboard Architecture, and Technical Challenges in Autonomous Driving
Hulu Beijing
Hulu Beijing
Apr 25, 2019 · Artificial Intelligence

How to Build End-to-End Deep Learning Models for Self-Driving Cars

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.

End-to-EndPilotNetautonomous driving
0 likes · 9 min read
How to Build End-to-End Deep Learning Models for Self-Driving Cars
DataFunTalk
DataFunTalk
Apr 4, 2019 · Artificial Intelligence

Exploring Trajectory Planning: Concepts, Decision‑Making, and Challenges in Autonomous Driving

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.

Optimizationautonomous drivingdecision making
0 likes · 17 min read
Exploring Trajectory Planning: Concepts, Decision‑Making, and Challenges in Autonomous Driving
DataFunTalk
DataFunTalk
Feb 13, 2019 · Artificial Intelligence

Reinforcement Learning: Principles, Applications, and the PARL Framework

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.

AIDialogue SystemsPARL
0 likes · 18 min read
Reinforcement Learning: Principles, Applications, and the PARL Framework
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 4, 2019 · Artificial Intelligence

What Are Alibaba DAMO Academy’s 2019 Tech Trends Shaping the Future?

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.

5GAIBlockchain
0 likes · 9 min read
What Are Alibaba DAMO Academy’s 2019 Tech Trends Shaping the Future?
Hulu Beijing
Hulu Beijing
Sep 14, 2018 · Artificial Intelligence

Why Autonomous Driving Could Save Millions of Lives and Transform Transportation

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.

AI Algorithmsautonomous drivingoccupancy grid
0 likes · 10 min read
Why Autonomous Driving Could Save Millions of Lives and Transform Transportation
Meituan Technology Team
Meituan Technology Team
Sep 6, 2018 · Industry Insights

Inside the 2018 AI Challenger: Datasets, Tracks, and Real‑World Impact

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.

AI competitionMeituanSentiment Analysis
0 likes · 12 min read
Inside the 2018 AI Challenger: Datasets, Tracks, and Real‑World Impact
Architects' Tech Alliance
Architects' Tech Alliance
Aug 4, 2018 · Cloud Computing

Public Cloud Market Landscape and Future Trends

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.

5GIaaSMarket Analysis
0 likes · 16 min read
Public Cloud Market Landscape and Future Trends
21CTO
21CTO
Jun 1, 2018 · Fundamentals

Why Quantum Computing Won’t Replace Classical CPUs—and What It Can Actually Solve

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.

AIIntelautonomous driving
0 likes · 10 min read
Why Quantum Computing Won’t Replace Classical CPUs—and What It Can Actually Solve
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 8, 2018 · Artificial Intelligence

Top 2018 Tech Predictions: AI, Quantum Computing, IoT, and Blockchain

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.

IoTautonomous drivingquantum computing
0 likes · 12 min read
Top 2018 Tech Predictions: AI, Quantum Computing, IoT, and Blockchain
Architecture Digest
Architecture Digest
Aug 1, 2017 · Artificial Intelligence

Comprehensive Overview of Autonomous Driving Technologies, Companies, and Industry Trends

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.

AIIndustry Trendsautonomous driving
0 likes · 19 min read
Comprehensive Overview of Autonomous Driving Technologies, Companies, and Industry Trends
21CTO
21CTO
Jun 24, 2017 · Artificial Intelligence

Where Did Baidu’s AI Stars Go? Inside the Exodus of China’s Top AI Talent

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.

AIBaiduStartups
0 likes · 41 min read
Where Did Baidu’s AI Stars Go? Inside the Exodus of China’s Top AI Talent