Table of Content

1. Foundation of End-to-end Autonomous Driving Technology
1.1 Terminology and Concept of End-to-end Autonomous Driving
1.2 Introduction to and Status Quo of End-to-end Autonomous Driving
Background of End-to-end Autonomous Driving
Reason for End-to-end Autonomous Driving: Business Value
Difference between End-to-end Architecture and Traditional Architecture (1)
Difference between End-to-end Architecture and Traditional Architecture (2)
End-to-end Architecture Evolution
Progress in End-to-end Intelligent Driving (1)
Progress in End-to-end Intelligent Driving (2)
Comparison between One-stage and Two-stage End-to-end Autonomous Driving
Mainstream One-stage/Segmented End-to-end System Performance Parameters
Significance of Introducing Multi-modal models to End-to-end Autonomous Driving
Problems and Solutions for End-to-end Mass Production (1)
Problems and Solutions for End-to-end Mass Production (2)
Progress and Challenges in End-to-end Systems

1.3 Classic End-to-end Autonomous Driving Cases
SenseTime UniAD
Technical Principle and Architecture of SenseTime UniAD
Technical Principle and Architecture of Horizon Robotics VAD
Technical Principle and Architecture of Horizon Robotics VADv2
VADv2 Training
Technical Principle and Architecture of DriveVLM
Li Auto Adopts MoE
MoE and STR2
E2E-AD Model: SGADS
E2E Active Learning Case: ActiveAD
End-to-end Autonomous Driving System Based on Foundation Models

1.4 Foundation Models
1.4.1 Introduction
Core of End-to-end System - Foundation Models
Foundation Models (1) - Large Language Models: Examples of Applications in Autonomous Driving
Foundation Models (2) - Vision Foundation (1)
Foundation Models (2) - Vision Foundation (2)
Foundation Models (2) - Vision Foundation (3)
Foundation Models (2) - Vision Foundation (4)
Foundation Models (3) - Multimodal Foundation Models (1)
Foundation Models (3) - Multimodal Foundation Models (2)
1.4.2 Foundation Models: Multimodal Foundation Models
Development of and Introduction to Multimodal Foundation Models
Multimodal Foundation Models VS Single-modal Foundation Models (1)
Multimodal Foundation Models VS Single-modal Foundation Models (2)
Technology Panorama of Multimodal Foundation Models
Multimodal Information Representation
1.4.3 Foundation Models: Multimodal Large Language Models
Multimodal Large Language Models (MLLMs)
Architecture and Core Components of MLLMs
MLLMs - Mainstream Models
Application of MLLMs in Autonomous Driving

1.5 VLM & VLA
Application of Vision-Language Models (VLMs)
Development History of VLMs
Architecture of VLMs
Application Principle of VLMs in End-to-end Autonomous Driving
Application of VLMs in End-to-end Autonomous Driving
VLM?VLA
VLA Models
VLA Principle
Classification of VLA Models
Core Functions of End-to-end Multimodal Model for Autonomous Driving (EMMA)

1.6 World Models
Definition and Application
Basic Architecture
Generation of Virtual Training Data
Tesla’s World Model
Nvidia
InfinityDrive: Breaking Time Limits in Driving World Models

1.7 Comparison between E2E-AD Motion Planning Models
Comparison between Several Classical Models in Industry and Academia
Tesla: Perception and Decision Full Stack Integrated Model
Momenta: End-to-end Planning Architecture Based on BEV Space
Horizon Robotics 2023: End-to-end Planning Architecture Based on BEV Space
DriveIRL: End-to-end Planning Architecture Based on BEV Space
GenAD: Generative End-to-end Model

1.8 Embodied Language Models (ELMs)
ELMs Accelerate the Implementation of End-to-end Solutions
Application Scenarios
Limitations and Positive Impacts


2 Technology Roadmap and Development Trends of End-to-end Autonomous Driving
2.1 Technology Trends of End-to-end Autonomous Driving
Trend 1
Trend 2
Trend 3
Trend 4
Trend 5
Trend 6
Trend 7

2.2 Market Trends of End-to-end Autonomous Driving
Layout of Mainstream End-to-end System Solutions
Comparison of End-to-end System Solution Layout between Tier 1 Suppliers (1)
Comparison of End-to-end System Solution Layout between Tier 1 Suppliers (2)
Comparison of End-to-end System Solution Layout between Other Autonomous Driving Companies
Comparison of End-to-end System Solution Layout between OEMs (1)
Comparison of End-to-end System Solution Layout between OEMs (2)
Comparison of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs (1)
Comparison of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs (2)
Comparison of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs (3)
Comparison of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs (4)

2.3 End-to-end Autonomous Driving Team Building
Impacts of End-to-end Foundation Models on Organizational Structure (1)
Impacts of End-to-end Foundation Models on Organizational Structure (2)
End-to-end Autonomous Driving Team Building of Domestic OEMs (1)
End-to-end Autonomous Driving Team Building of Domestic OEMs (2)
End-to-end Autonomous Driving Team Building of Domestic OEMs (3)
End-to-end Autonomous Driving Team Building of Domestic OEMs (4)
End-to-end Autonomous Driving Team Building of Domestic OEMs (5)
End-to-end Autonomous Driving Team Building of Domestic OEMs (6)
End-to-end Autonomous Driving Team Building of Domestic OEMs (7)
Team Building of End-to-end Autonomous Driving Suppliers (1)
Team Building of End-to-end Autonomous Driving Suppliers (2)
Team Building of End-to-end Autonomous Driving Suppliers (3)
Team Building of End-to-end Autonomous Driving Suppliers (4)


3. End-to-end Autonomous Driving Suppliers
3.1 MOMENTA
Profile
One-stage End-to-end Solutions (1)
One-stage End-to-end Solutions (2)
End-to-end Planning Architecture
One-stage End-to-end Mass Production Empowers the Large-scale Implementation of NOA in Mapless Cities
High-level Intelligent Driving and End-to-end Mass Production Customers

3.2 DeepRoute.ai
Product Layout and Strategic Deployment
End-to-end Layout
Difference between End-to-end Solutions and Traditional Solutions
Implementation Progress in End-to-end Solutions
End-to-end VLA Model Analysis
Designated End-to-end Mass Production Projects and VLA Model Features
Hierarchical Prompt Tokens
End-to-end Training Solutions
Application Value of DINOv2 in the Field of Computer Vision
Autonomous Driving VQA Task Evaluation Data Sets
Score Comparison between HoP and Huawei

3.3 Huawei
Development History of Huawei’s Intelligent Automotive Solution Business Unit
End-to-end Concept and Perception Algorithm of ADS
ADS 3.0 (1)
ADS 3.0 (2): End-to-end
ADS 3.0 (3): ASD 3.0 VS. ASD 2.0
End-to-end Solution Application Cases of ADS 3.0 (1)
End-to-end Solution Application Cases of ADS 3.0 (2)
End-to-end Solution Application Cases of ADS 3.0 (3)
End-to-end Autonomous Driving Solutions of Multimodal LLMs
End-to-end Testing?VQA Tasks
Architecture of DriveGPT4
End-to-end Training Solution Examples
The Training of DriveGPT4 Is Divided Into Two Stages
Comparison between DriveGPT4 and GPT4V

3.4 Horizon Robotics
Profile
Main Partners
End-to-end Super Drive and Its Advantages
Architecture and Technical Principle of Super Drive
Journey 6 and Horizon SuperDrive? All-scenario Intelligent Driving Solution
Senna Intelligent Driving System (Foundation Model + End-to-end)
Core Technology and Training Method of Senna
Core Module of Senna

3.5 Zhuoyu Technology
Profile
R&D and Production
Two-stage End-to-end Parsing
One-stage Explainable End-to-end Parsing
End-to-end Mass Production Customers

3.6 NVIDIA
Profile
Autonomous driving solution
DRIVE Thor
Basic Platform for Autonomous Driving
Next-generation Automotive Computing Platform
Latest End-to-end Autonomous Driving Framework: Hydra-MDP
Self-developed Model Architecture

3.7 Bosch
Intelligent Driving China Strategic Layout (1)
Based on the End-to-end Development Trend, Bosch Intelligent Driving initiates the Organizational Structure Reform
Intelligent Driving Algorithm Evolution Planning

3.8 Baidu
Profile of Apollo
Strategic Layout in the Field of Intelligent Driving
Two-stage End-to-end
Production Models Based on Two-stage End-to-end Technology Architecture
Baidu Auto Cloud 3.0 Enables End-to-end Systems from Three Aspects

3.9 SenseAuto
Profile
UniAD End-to-end Solution
DriveAGI: The Next-generation Autonomous Driving Foundation Model and Its Advantages
DiFSD: SenseAuto’s End-to-end Autonomous Driving System That Simulates Human Driving Behavior
DiFSD: Technical Interpretation

3.10 QCraft
Profile
"Driven-by-QCraft" High-level Intelligent Driving Solution
End-to-end Layout
Advantages of End-to-end Layout

3.11 Wayve
Profile
Advantages of AV 2.0
GAIA-1 World Model - Architecture
GAIA-1 World Model - Token
GAIA-1 World Model - Generation Effect
LINGO-2

3.12 Waymo
End-to-end Multimodal Model for Autonomous Driving (EMMA)
EMMA Analysis: Multimodal Input
EMMA Analysis: Defining Driving Tasks as Visual Q&A
EMMA Analysis: Introducing Thinking Chain Reasoning to Enhance Interpretability
Limitations of EMMA

3.13 GigaStudio
Introduction
DriveDreamer
DriveDreamer 2
DriveDreamer4D
3.14 LightWheel AI
Profile
Core Technology
Core Technology Stack
Data Annotation and Synthetic Data


4. End-to-end Autonomous Driving Layout of OEMs
4.1 Xpeng’s End-to-end Intelligent Driving Layout
End-to-end System (1): Architecture
End-to-end System (2): Intelligent Driving Model
End-to-end System (3): AI+XNGP
End-to-End System (4): Organizational Transformation
Data Collection, Annotation and Training

4.2 Li Auto’s End-to-end Intelligent Driving Layout
End-to-end Solutions (1)
End-to-end Solutions (2)
End-to-end Solutions (3)
End-to-end Solutions (4)
End-to-end Solutions (5)
End-to-end Solutions (6)
End-to-end Solutions: L3 Autonomous Driving
End-to-end Solutions: Building of a Complete Foundation Model
Technical Layout: Data Closed Loop

4.3 Tesla’s End-to-end Intelligent Driving Layout
Interpretation of the 2024 AI Conference
Development History of AD Algorithms
End-to-end Process 2023-2024
Development History of AD Algorithms (1)
Development History of AD Algorithms (2)
Development History of AD Algorithms (3)
Development History of AD Algorithms (4)
Development History of AD Algorithms (5)
Tesla: Core Elements of the Full-stack Perception and Decision Integrated Model
"End-to-end" Algorithms
World Models
Data Engines
Dojo Supercomputing Center

4.4 Zeron’s End-to-end Intelligent Driving Layout
Profile
End-to-end Autonomous Driving System Based on Foundation Models (1)
End-to-end Autonomous Driving System Based on Foundation Models (2) - Data Training
Advantages of End-to-end Driving System

4.5 Geely & ZEEKR’s End-to-end Intelligent Driving Layout
Geely’s ADAS Technology Layout: Geely Xingrui Intelligent Computing Center (1)
Geely’s ADAS Technology Layout: Geely Xingrui Intelligent Computing Center (2)
Geely’s ADAS Technology Layout: Geely Xingrui Intelligent Computing Center (3)
Xingrui AI foundation model
Application of Geely’s Intelligent Driving Foundation Model Technology
ZEEKR’s End-to-end System: Two-stage Solution
ZEEKR Officially Released End-to-end Plus
ZEEKR’s End-to-end Plus
Examples of Models with ZEEKR’s End-to-end System

4.6 Xiaomi Auto’s End-to-end Intelligent Driving Layout
Profile
End-to-end Technology Enables All-scenario Intelligent Driving from Parking Spaces to Parking Spaces
Road Foundation Models Build HD Maps through Road Topology
New-generation HAD Accesses End-to-end System
End-to-end Technology Route

4.7 NIO’s End-to-end Intelligent Driving Layout
Intelligent Driving R&D Team Reorganization with an Organizational Structure Oriented Towards End-to-end System
From Modeling to End-to-end, World Models Are the Next
World Model End-to-end System
Intelligent Driving Architecture: NADArch 2.0
End-to-end R&D Tool Chain
Imagination, Reconstruction and Group Intelligence of World Models
NSim
Software and Hardware Synergy Capabilities Continue to Strengthen, Moving towards the End-to-end System Era

4.8 Changan Automobile’s End-to-end Intelligent Driving Layout
Brand Layout
End-to-end System (1)
End-to-end System (2)
Production Models with End-to-end System

4.9 Mercedes-Benz’s End-to-end Intelligent Driving Layout
Brand New "Vision-only Solutions without Maps, L2++ All-scenario High-level Intelligent Driving Functions"
Brand New Self-developed MB.OS
Cooperation with Momenta

4.10 Chery’s End-to-end Intelligent Driving Layout
Profile of ZDRIVE.AI
Chery’s End-to-end System Development Planning