End-to-end Autonomous Driving (E2E AD) Research Report, 2024
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End-to-end Autonomous Driving Research: status quo of End-to-end (E2E) autonomous driving

1. Status quo of end-to-end solutions in China
An end-to-end autonomous driving system refers to direct mapping from sensor data inputs (camera images, LiDAR, etc.) to control command outputs (steering, acceleration/deceleration, etc.). It first appeared in the ALVINN project in 1988. It uses cameras and laser rangefinders as input and a simple neural network to generate steering as output.

In early 2024, Tesla rolled out FSD V12.3, featuring an amazing intelligent driving level. The end-to-end autonomous driving solution garners widespread attention from OEMs and autonomous driving solution companies in China. 

Compared with conventional multi-module solutions, the end-to-end autonomous driving solution integrates perception, prediction and planning into a single model, simplifying the solution structure. It can simulate human drivers making driving decisions directly according to visual inputs, effectively cope with long tail scenarios of modular solutions and improve the training efficiency and performance of models.

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Li Auto's end-to-end solution
Li Auto believes that a complete end-to-end model should cover the whole process of perception, tracking, prediction, decision and planning, and it is the optimal solution to achieve L3 autonomous driving. In 2023, Li Auto pushed AD Max3.0, with overall framework reflecting the end-to-end concept but still a gap with a complete end-to-end solution. In 2024, Li Auto is expected to promote the system to become a complete end-to-end solution. 

Li Auto's autonomous driving framework is shown below, consisting of two systems:
Fast system: System 1, Li Auto’s existing end-to-end solution which is directly executed after perceiving the surroundings.
Slow system: System 2, a multimodal large language model that logically thinks and explores unknown environments to solve problems in unknown L4 scenarios.

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In the process of promoting the end-to-end solution, Li Auto plans to unify the planning/forecast model and the perception model, and accomplish the end-to-end Temporal Planner on the original basis to integrate parking with driving.

2. Data becomes the key to the implementation of end-to-end solutions.
The implementation of an end-to-end solution requires processes covering R&D team building, hardware facilities, data collection and processing, algorithm training and strategy customization, verification and evaluation, promotion and mass production. Some of the sore points in scenarios are as shown in the table:

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The integrated training in end-to-end autonomous driving solutions requires massive data, so one of the difficulties it faces lies in data collection and processing.
First of all, it needs a long time and may channels to collect data, including driving data and scenario data such as roads, weather and traffic conditions. In actual driving, the data within the driver's front view is relatively easy to collect, but the surrounding information is hard to say.
During data processing, it is necessary to design data extraction dimensions, extract effective features from massive video clips, make statistics of data distribution, etc. to support large-scale data training.

DeepRoute

As of March 2024, DeepRoute.ai's end-to-end autonomous driving solution has been designated by Great Wall Motor and involved in the cooperation with NVIDIA. It is expected to adapt to NVIDIA Thor in 2025. In the planning of DeepRoute.ai, the transition from the conventional solution to the "end-to-end" autonomous driving solution will go through sensor pre-fusion, HD map removal, and integration of perception, decision and control.

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GigaStudio
DriveDreamer, an autonomous driving model of GigaStudio, is capable of scenario generation, data generation, driving action prediction and so forth. In the scenario/data generation, it has two steps:
When involving single-frame structural conditions, guide DriveDreamer to generate driving scenario images, so that it can understand structural traffic constraints easily.
Extend its understanding to video generation. Using continuous traffic structure conditions, DriveDreamer outputs driving scene videos to further enhance its understanding of motion transformation.

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3. End-to-end solutions accelerate the application of embodied robots.
In addition to autonomous vehicles, embodied robots are another mainstream scenario of end-to-end solutions. From end-to-end autonomous driving to robots, it is necessary to build a more universal world model to adapt to more complex and diverse real application scenarios. The development framework of mainstream AGI (General Artificial Intelligence) is divided into two stages:
Stage 1: the understanding and generation of basic foundation models are unified, and further combined with embodied artificial intelligence (embodied AI) to form a unified world model;
Stage 2: capabilities of world model + complex task planning and control, and abstract concept induction gradually evolve into the era of the interactive AGI 1.0.

In the landing process of the world model, the construction of an end-to-end VLA (Vision-Language-Action) autonomous system has become a crucial link. VLA, as the basic foundation model of embodied AI, can seamlessly link 3D perception, reasoning and action to form a generative world model, which is built on the 3D-based large language model (LLM) and introduces a set of interactive markers to interact with the environment.

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As of April 2024, some manufacturers of humanoid robots adopting end-to-end solutions are as follows:

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For example, Udeer·AI's Large Physical Language Model (LPLM) is an end-to-end embodied AI solution that uses a self-labeling mechanism to improve the learning efficiency and quality of the model from unlabeled data, thereby deepening the understanding of the world and enhancing the robot's generalization capabilities and environmental adaptability in cross-modal, cross-scene, and cross-industry scenarios.

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LPLM abstracts the physical world and ensures that this kind of information is aligned with the abstract level of features in LLM. It explicitly models each entity in the physical world as a token, and encodes geometric, semantic, kinematic and intentional information.

In addition, LPLM adds 3D grounding to the encoding of natural language instructions, improving the accuracy of natural language to some extent. Its decoder can learn by constantly predicting the future, thus strengthening the ability of the model to learn from massive unlabeled data.

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)
 

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