Table of Content

1. Foundation of End-to-end Autonomous Driving Technology

1.1 Terminology and Concept of End-to-end Autonomous Driving
1.1.1 Terminology Explanation of End-to-end Autonomous Driving
1.1.2 Development History of End-to-end Autonomous Driving (1)
1.1.3 Development History of End-to-end Autonomous Driving (2)

1.2 Status Quo of End-to-end Autonomous Driving
1.2.1 Development History of Autonomous Driving Algorithm Industrialization
1.2.2 Status Quo of E2E-AD Model Mass Production
1.2.3 Progress and Challenges of E2E-AD

1.3 Comparison among End-to-end E2E-AD Motion Planning Models
1.3.1 End-to-end E2E-AD Trajectory Planning of Autonomous Driving: Comparison among Several Classical Models in Industry and Academia
1.3.2 Tesla: Perception and Decision-making Full Stack Integrated Model
1.3.3 Model 2
1.3.4 Model 3
1.3.5 Model 4
1.3.6 Model 5

1.4 Comparison among End-to-end E2E-AD Models
1.4.1 Horizon Robotics VADv2: An End-to-end Driving Model Based on Probability Programming
1.4.2 Model 2
1.4.3 Model 3
1.4.4 Model 4
1.4.5 Model 5

1.5 Typical Cases of End-to-end Autonomous Driving E2E-AD Models
1.5.1 Case 1 - SenseTime’s E2E-AD Model: UniAD
1.5.2 Case 2
1.5.3 Case 3

1.6 Embodied Language Models (ELMs)
1.6.1 ELMs accelerate the landing of End-to-end Solutions
1.6.2 Foundation Model Application scenarios of ELMs (1)
1.6.2 Foundation Model Application scenarios of ELMs (2)
1.6.2 Foundation Model Application scenarios of ELMs (3)
1.6.2 Foundation Model Application scenarios of ELMs (4)
1.6.2 Foundation Model Application scenarios of ELMs (5)
1.6.2 Foundation Model Application scenarios of ELMs (6)
1.6.2 Foundation Model Application scenarios of ELMs (7)
1.6.3 Limitations and Positive Effects of ELMs


2 Technology Roadmap and Development Trends of End-to-end Autonomous Driving

2.1 Scenario Difficulties
2.1.1 Scenario Difficulties and Solutions: Computing Power Supply/Data Acquisition
2.1.2 Scenario Difficulties and Solutions: Team Building/Interpretability

2.2 Development Trends
2.2.1 Trend 1
2.2.2 Trend 2
2.2.3 Trend 3
2.2.4 Trend 4
2.2.5 Trend 5: Universal World Model: Three Paradigms and System Construction of AGI
2.2.6 Trend 6
2.2.7 Trend 7


3 Application of End-to-end Autonomous Driving in the Field of Passenger Cars

3.1 Dynamics of Domestic End-to-end Autonomous Driving Companies
3.1.1 Comparison among End-to-End Foundation Model Technologies of OEMs
3.1.2 Comparison among End-to-End Foundation Model Technologies of?Major?Suppliers
3.1.3 Patents on End-to-End Autonomous Driving of Intelligent Vehicles

3.2 DeepRoute.ai
3.2.1 Implementation Progress of End-to-end Solutions
3.2.2 Difference between End-to-end Solutions and Traditional Solutions

3.3 Haomo.AI
3.3.1 End-to-end Solution Construction Strategy
3.3.2 Reinforcement Learning/Imitation Learning Techniques
3.3.3 Training Methods of End-to-end Solutions

3.4 PhiGent Robotics
3.4.1 Interactive Scenario Diagrams for Agents
3.4.2 GraphAD Construction Path
3.4.3 GraphAD Test Results

3.5 Enterprise 5
3.6 Enterprise 6
3.7 Enterprise 7
3.8 Enterprise 8
3.9 Enterprise 9
3.10 Enterprise 10
3.11 Enterprise 11
3.12 NIO
3.13 Xpeng
3.14 Li Auto
3.14.1 Li Auto’s End-to-end Solution
3.14.2 Li Auto’s Current Autonomous Driving Solution
3.14.3 Li Auto’s DriveVLM
3.15 Enterprise 15
3.16 Enterprise 16
3.17 XX University
3.18 XX University


4 Application of End-to-end Autonomous Driving in the Field of Robots

4.1 Progress of End-to-end Technology for Humanoid Robots
4.1.1 Humanoid Robots Are the Carrier of Embodied Artificial Intelligence
4.1.2 NVIDIA GTC 2024: Several Core Humanoid Robot Companies Participating in the Conference
4.1.3 Global Demand for Humanoid Robots
4.1.4 Comparison among Global Humanoid Robot Features

4.2 Humanoid Robot: Figure 01
4.2.1 Features of Figure 01
4.2.2 Working Principle of Figure 01
4.2.3 Functions of Figure 01
4.2.4 Development of Figure 01

4.3 Zero Demonstration Autonomous Robot Open Source Model: O Model
4.3.1 Implementation Principle of O Model

4.4 Nvidia’s Project GR00T
4.4.1 Project GR00T - Robot Foundation Model Development Platform
4.4.2 Project GR00T - Robot Learning and Scaling Development Workflow
4.4.3 Project GR00T - Robot Isaac Simulation Platform
4.4.4 Project GR00T - Omniverse Replicator Platform

4.5 Robot Case 5

4.6 Robot Case 6

4.7 Robot Case 7

4.8 Robot Case 8

4.9 Robot Case 9

4.10 Status Quo and Future of Foundation Models+Robots
4.10.1 Application of Foundation Models in the Robot Field
4.10.2 End-to-end Application and Future Prospect of Foundation Models in the Robot Field
4.10.3 Future Trends of Embodied Artificial Intelligence


5 How to Implement End-to-end Autonomous Driving Projects?

5.1 E2E-AD Project Implementation Case: Tesla
5.1.1 Development History of Autopilot Hardware and Solutions
5.1.2 Evolution of Self-developed Autopilot Hardware and Computing Power Requirements of FSD v12.3
5.1.3 Autopilot: Multi-task E2E Learning Technical Solutions
5.1.4 E2E Team
5.1.5 Description of Most Key AI Jobs in Recruitment
5.1.6 E2E R&D Investment

5.2 E2E-AD Project Implementation Case: Wayve
5.2.1 Profile
5.2.2 Data Generation Cases of E2E
5.2.3 How to Build an E2E-AD System
5.2.4 Team layout

5.3 Team Building and Project Budget
5.3.1 Autonomous Driving Project: Comparison between Investment and Team Size
5.3.2 E2E-AD Project: Top-level System Design and Organizational Structure Design
5.3.3 E2E-AD Project: Development Team Layout Budget and Competitiveness Construction
5.3.4 E2E-AD Project: Job Design and Description
5.3.5 Cases of End-to-end Autonomous Driving Team Building of Domestic OEMs

5.9 Automotive E2E Autonomous Driving System Design
5.4.1 E2E-AD Project Development Business Process
5.4.2 Project Business Process Reference (1)
5.4.3 Project Business Process Reference (2)

5.5 Cloud E2E Autonomous Driving System Design
5.5.1 E2E-AD Project Business Process Reference
5.5.2 E2E-AD Project Cloud Design (1)
5.5.3 E2E-AD Project Cloud Design (2)