The "Autonomous Driving Data Closed Loop Research Report, 2024" has been added to ResearchAndMarkets.com's offering.

In a significant leap towards fully autonomous driving technologies, the industry is experiencing an evolutionary shift from a data-driven approach to a cognition-driven framework. This transformation aims to pave the way for artificial general intelligence (AGI) in intelligent driving systems, enhancing the systems' reasoning, decisions, and interaction capabilities to parallel human cognition.

Data Collection Evolution

The era of large-scale data collection by specialized vehicles is giving way to a focus on quality over quantity. Production vehicles now collect highly specific scenario data, bringing to the fore intricate driving behaviors and real-world experiences. This refined data, propelled by proprietary processing and analysis technologies, serves as a robust foundation for the next generation of AI algorithm training.

Advances in Data Labeling

With the proliferation of deep learning and foundation models, the landscape of data labeling is undergoing a transformation towards AI-automated procedures, showcasing an industry trend towards high-precision labeling. Innovations in labeling tools are optimizing the processing of voluminous data and refining the perception capabilities of autonomous driving systems.

The Role of Simulation Testing

As autonomous driving technology pushes towards higher levels of intelligence, simulation testing grows ever more critical. The focus on high accuracy and fidelity in simulation scenarios ensures that systems can cope with a diverse array of driving conditions, ultimately enhancing the safety and reliability of autonomously driven vehicles.

OEMs’ Emergent Self-Development Capabilities

The progression of full-stack self-development capabilities within Original Equipment Manufacturers (OEMs) is prompting data closed-loop technology providers to continuously elevate their offerings. Through advanced perception and intricate planning and control systems, data closed loop technology is steering the industry towards a more streamlined, efficient, and safe autonomous driving future.

The developments in the autonomous driving field are not just confined to technological breakthroughs but also extend to improved strategies and models for data handling and utilization, which ultimately leads to heightened user experiences and safety standards.

The community eagerly anticipates the continued evolution and real-world applications of these advanced autonomous driving solutions.

Key Topics Covered:

1 Overview of Autonomous Driving Data Closed Loop

1.1 Evolution of Data Closed Loop

1.2 Difficulties in Building An Autonomous Driving Data Closed Loop

1.3 Solution Case 1

1.4 Solution Case 2

1.5 Autonomous Driving Data Closed Loop Industry Chain Map

1.6 Foundation of Data Closed Loop: Data Security

2 Data Collection

2.1 Summary of Diverse Intelligent Driving Data Collection Modes

2.2 Typical Data Collection/Data Compression Solutions

2.2.1 Case 1: TZTEK Technology

2.2.2 Case 2: Kunyi Electronics

2.2.3 Case 3: EXCEEDDATA

3 Data Annotation

Summary: Comparison between Intelligent Data Annotation Platforms

3.1 Haitian Ruisheng

3.2 MindFlow

3.3 DataBaker Technology

3.4 Molar Intelligence

3.5 Magic Data

3.6 Jinglianwen Technology

3.7 Appen

3.8 Scale AI

4 Data Processing

4.1 Autonomous Driving Data Closed-Loop Processing Process

4.2 Classification and Grading of Autonomous Driving Data

4.3 Data Compliance

4.4 Data Transmission

4.5 Intelligent Computing Center

4.6 Data Closed-Loop Cloud Platform

5 Data Closed-Loop Technology Suppliers

Summary: Comparison between Data Closed-Loop Technology Suppliers

5.1 JueFX Technology

5.2 QCraft

5.3 Zhuoyu

5.4 Haomo.ai

5.5 SenseAuto

5.6 Momenta

5.7 Freetech

5.8 Nullmax

5.9 DeepRoute.ai

5.10 Bosch

5.11 EXCEEDDATA

5.12 Yoocar

5.13 Mxnavi

5.14 NavInfo

6 Data Closed Loop of Typical OEMs

Summary: Data Closed Loop Capabilities of OEMs

6.1 BYD

6.2 Chery

6.3 Great Wall Motor

6.4 Geely

6.5 Li Auto

6.6 Xpeng

6.7 NIO

7 Data Closed Loop Development Trends

Companies Featured

• Haitian Ruisheng
• MindFlow
• DataBaker Technology
• Molar Intelligence
• Magic Data
• Jinglianwen Technology
• Appen
• Scale AI
• JueFX Technology
• QCraft
• Zhuoyu
• Haomo.ai
• SenseAuto
• Momenta
• Freetech
• Nullmax
• DeepRoute.ai
• Bosch
• EXCEEDDATA
• Yoocar
• Mxnavi
• NavInfo
• BYD
• Chery
• Great Wall Motor
• Geely
• Li Auto
• Xpeng
• NIO

For more information about this report visit https://www.researchandmarkets.com/r/nf1mka

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