End-to-End Autonomous Driving Research Report, 2025

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End-to-End Autonomous Driving Research: E2E Evolution towards the VLA Paradigm via Synergy of Reinforcement Learning and World Models??
The essence of end-to-end autonomous driving lies in mimicking driving behaviors through large-scale, high-quality human driving data. From a technical perspective, while imitation learning-based approaches can approach human-level driving performance, they struggle to transcend human cognitive limits. Additionally, the scarcity of high-quality scenario data and uneven data quality in driving datasets make it extremely challenging for end-to-end solutions to reach human-level capabilities. The high scalability threshold further complicates progress, as these systems typically require millions of high-quality driving clips for training.

Following the industry buzz around the DeepSeek-R1 model in early 2025, its innovative reinforcement learning (RL)-only technical path demonstrated unique advantages. This approach achieves cold startup with minimal high-quality data and employs a multi-stage RL training mechanism, effectively reducing dependency on data scale for large model training. This extension of the "scaling laws" enables continuous model expansion. Innovations in RL can also be transferred to end-to-end autonomous driving, enhancing environmental perception, path planning, and decision-making with greater precision. This lays the foundation for building larger, more capable intelligent models.

Crucially, RL frameworks excel at autonomously generating reasoning chains in interactive environments, enabling large models to develop Chain-of-Thought (CoT) capabilities. This significantly improves logical reasoning efficiency and even unlocks potential beyond human cognitive constraints. By interacting with simulation environments generated by world models, end-to-end autonomous driving models gain deeper understanding of real-world physical rules. This RL-driven technical path offers a novel approach to algorithm development, promising to break traditional imitation learning limitations.

I Transition of End-to-End Models towards the VLA Paradigm

End-to-end models directly map visual inputs to driving trajectory outputs via neural networks. However, lacking intrinsic understanding of physical world dynamics, these models operate without explicit semantic comprehension or logical reasoning. They fail to interpret verbal commands, traffic rules, or textual information. Furthermore, their limited 3D spatial perception restricts generalization in long-tail scenarios.

The Visual-Language-Action (VLA) paradigm introduces critical improvements by integrating Large Language Models (LLMs) into the architecture. This transforms the original single-modality vision-action system into a multimodal framework combining vision, language, and action. The inclusion of LLMs injects human-like common sense and logical reasoning into autonomous driving systems, transitioning from data-driven "weak AI" to cognitive intelligence-driven "generalist systems."

VLA Input: Signals received from cameras, navigation systems, maps, and other devices. These signals are processed by two encoders:
Vision Encoder: Encodes image data to extract high-level features of the road environment.
Text Encoder: Processes text information generated from human-vehicle interactions, such as voice commands or parameter settings.
??
VLA Output:
Trajectory Decoder: Converts model-generated information into specific trajectory signals, outlining the vehicle’s driving plan for the next 10 to 30 seconds, including speed control and route details.
Text Decoder: Simultaneously generates natural language explanations for the decisions. For example, when detecting a pedestrian crossing the road, the system not only plans a deceleration-and-stop trajectory but also outputs a textual explanation like “Pedestrian crossing detected; slowing down and stopping.” This ensures transparency in decision-making.

Core Breakthroughs of VLA?
World Model Construction: VLA extracts rich environmental information from sensor data, leverages language models to interpret human instructions, and generates explainable decision-making processes. It then translates multimodal inputs into actionable driving commands.
Chain-of-Thought (CoT) Reasoning: VLA autonomously generates reasoning chains in interactive environments, enabling logical inference capabilities that surpass human cognitive limits. With the support of large models, VLA enhances visual and spatial understanding beyond traditional end-to-end approaches.

By 2030, VLA-centric end-to-end solutions are projected to dominate over 50% of L3/L4 autonomous driving markets, reshaping the value chain of traditional Tier-1 suppliers.

In 2025, Li Auto integrated End-to-End (E2E) and Visual Language Model (VLM) approaches into an advanced Vision-Language-Action (VLA) architecture. This shift addressed critical limitations in the previous dual-system framework:
Constraint 1: The dual-system pipeline processed inputs (cameras, LiDAR, vehicle pose, navigation) through a 3D encoder and directly output trajectories via an action decoder. However, its discriminative AI-based E2E model lacked generalization and common-sense reasoning, struggling with long-tail scenarios. While simple, it failed in complex spatial understanding and language interactions.
Constraint 2: System 2 (semantic assistant) supported System 1 (E2E) but suffered from technical shortcomings. Existing VLMs relied on 2D front-camera data, neglecting omnidirectional inputs from surround-view and rear cameras. Additionally, System 2 lacked robust 3D spatial understanding—a core requirement for trajectory planning.

Under the VLA architecture, V-Spatial Intelligence is responsible for 3D data processing and spatial understanding, equipped with a 3D word segmentation device; L-Linguistic Intelligence uses MindGPT large-scale language models to fuse spatial markers and process semantic information; and A-Action Policy integrates decisions through collective action generators to generate action trajectories. The MindVLA architecture enhances the tokenization of spatial information (3D Tokenizer), scene understanding of language models (MindGPT), and collective action generation capabilities (Collective Action Generator), enabling VLA to maintain strong spatial language reasoning capabilities while realizing the collective modeling and alignment of the characteristics of the three modes of vision, language, and action in a unified space, which is expected to solve the intelligent decision-making requirements in future complex scenarios.

II Training Process of VLA Models and Application of Reinforcement Learning??

In post-training of Large Language Models (LLMs), reinforcement learning (RL) has become increasingly prevalent. For instance, DeepSeek-R1, a standout model this year, leveraged RL as a core training method. By designing appropriate reward mechanisms, RL effectively activated the foundational model’s reasoning capabilities. This technical advantage, initially proven in language models, has now drawn attention in the autonomous driving industry, with multiple manufacturers integrating RL into their ADAS technologies.

The training of the VLA model is divided into two stages: "pre-training of the base model" and "domain fine-tuning". In the pre-training stage, the model is given general cognitive abilities through massive data, such as understanding context, logical reasoning, etc. In the fine-tuning stage of the intelligent driving field, basic driving rules (such as lane keeping and obstacle recognition) need to be established through supervised learning, and then key upgrades need to be completed with the help of reinforcement learning (RL). Reinforcement learning draws on the successful experience in natural language processing (such as RLHF alignment of human preferences), and optimizes decision-making in driving scenarios through the "open-loop + closed-loop" mechanism: the open-loop stage uses historical takeover data to calibrate safety logic, and the closed-loop stage simulates extreme working conditions through virtual scene generation technology (such as world model), allowing the model to actively trial and error and iterate strategies, breaking through the limitations of traditional end-to-end models relying on massive amounts of labeled data.

Imitation Learning (IL)
The scenario cloning strategy (BC) in imitation learning, the core of which is to formulate a strategy by learning driving trajectory of experts such as human drivers. In the field of intelligent driving, this method mainly relies on analyzing large amounts of driving data to imitate human driving behavior. Its advantage is that it is simple to implement and computationally efficient, but its shortcomings are also obvious - it is difficult to deal with unseen special scenarios or abnormal situations.

From the perspective of the training mechanism, the scenario cloning strategy adopts an open-loop approach and relies on the driving demonstration data of the distribution law. However, real driving is a typical closed-loop process, and the subtle deviations of each step may accumulate over time, forming compound errors and triggering unknown scenarios. As a result, the strategy trained by scenario cloning often performs poorly in unfamiliar situations, and its robustness has attracted industry attention.

The principle of reinforcement learning (RL) is to optimize action strategies through reward functions:

The reinforcement learning model continuously interacts in the simulated traffic scene and relies on the reward mechanism to adjust and optimize the driving strategy. This way, the model can learn more reasonable decisions in the complex and dynamic traffic environment. However, reinforcement learning has obvious shortcomings in practical applications: on the one hand, the training efficiency is not high, and a lot of trial and error is required to obtain the usable model; on the other hand, it cannot be trained directly in the real road environment - after all, the real driving scene cannot afford frequent trial and error, and the cost is too high. Most of the current simulation training is based on the sensor data generated by the game engine, and the real environment relies on the information of the object itself rather than the sensor input, resulting in the gap between the simulation results and the actual scene.

Another problem is the human behavior alignment: the reinforcement learning exploration process may cause the model strategy to deviate from human driving habits and act incoherently. To address this, imitation learning is often integrated as a regularization term during RL training, incorporating human driving data to align policies with human behavior.

??Li Auto’s MindVLA Training Methodology?

Stage I: The training process of Li Auto's VLA model is divided into four stages: VL visual language base model pre-training, assisted driving post-training, assisted driving reinforcement learning, and driver agent construction. Among them, the pre-training of VL base model is the core link of the entire training system. In the early dual-system stage, Li Auto used Ali Tongyi Qianwen's Qwen-VL visual language model, but when developing the latest VL base model, by partially integrating DeepSeek language model capability, Li Xiang said that the 9-month research and development cycle was effectively shortened, saving hundreds of millions of yuan in development costs.
Based on the pre-trained base model, Li Auto further optimizes the technology, and generates a small vehicle end-specific model with 3.60 billion parameters through model distillation technology to meet the deployment requirements of the vehicle computing platform.

Stage II & III:
The final goal of the VLA model trained in the cloud is to be applied on the vehicle platform. Due to the difference between the computing power of the vehicle and the cloud, the cloud model needs to be distilled and optimized by model compression techniques such as pruning and quantization. The specific method of Li Auto is: after completing the training of the VL base model with 32 billion parameters, it is first distilled into a 4 billion parameter model adapted to the computing power conditions of the vehicle. On this basis, reinforcement learning training is carried out to ensure that the model can not only meet the operation requirements of the vehicle computing platform, but also maintain sufficient decision-making ability.

The second and third stages of Li Auto VLA model training - assisted driving post-training and reinforcement learning can be seen as fine-tuning the base model in the field of intelligent driving. Among them, the post-training stage adopts the open-loop imitation learning method of the traditional end-to-end solution, while the reinforcement learning stage combines the open-loop and closed-loop modes to become the core improvement point of VLA model training.

Specifically:
Open-loop reinforcement learning: Using RLHF's reinforcement learning mechanism based on human feedback, the main goal is to adapt driving strategies to human driving habits and safety standards.Li Auto uses the accumulated human takeover vehicle data for training, so that the model clearly distinguishes between "reasonable operation" and "dangerous behavior", and completes the calibration of basic driving logic.
Closed-loop reinforcement learning (RL): High-intensity iterative training of the model by building a world model to generate a large number of virtual training and simulation scenarios. This method breaks the limitation of traditional reliance on real road condition data, greatly reduces the time and cost of actual road testing, and achieves an improvement in training efficiency.

These two phases complete the crucial transition from the basic model to the dedicated driving model by combining "first alignment of human preferences and then deep optimization through virtual scenarios".

III Synergistic Applications of World Models and RL
???
World models are pivotal for end-to-end autonomous driving training, evaluation, and simulation. They generate realistic synthetic videos from sensor inputs and vehicle states, enabling safe, controlled virtual environments for strategy assessment and physical rule comprehension.

RL Training Mechanism:
The essence of the world model is a model based on neural networks, which can establish a correlation model between environmental states, action choices, and feedback rewards, and directly guide the behavioral decision-making of the agent. In intelligent driving scenarios, this model can generate optimal action strategies based on real-time environmental states. More importantly, it can build a virtual interactive environment close to real dynamics, providing a closed-loop training platform for reinforcement learning - the system continuously receives reward feedback in the simulation environment and continuously optimizes the strategy.

Through this mechanism, the two core capabilities of the end-to-end model are expected to be significantly improved: one is the perception ability, the recognition accuracy and understanding ability of environmental elements such as vehicles, pedestrians, obstacles, etc., and the other is the predictive ability, the predictive accuracy of other traffic participants' behavioral intentions. This whole chain optimization from perception to decision-making is the core value of empowering intelligent driving by the world model.

Huawei's recently released "Qiankun Smart Driving ADS 4" also applies world model technology. In its "World Engine + World Behavior Model (WEWA) " technical architecture, the "World Engine" in the cloud is responsible for generating various extremely rare driving scenarios and converting these scenarios into "training questions" for the intelligent driving system, just like the "question-setting examiner" for the simulation test. The "World Behavior Model" on the vehicle side has full-modal perception and multi-MoE expert decision-making capabilities, acting as a "practical instructor", allowing the intelligent driving system to accumulate experience in dealing with complex scenarios in the simulation environment, and realize the ability to advance from theory to practice.

The cloud world base model recently released by Xpeng takes large language model as the core architecture, completes training through massive high-quality multi-modal driving data, and has the ability of visual semantic understanding, logical chain reasoning, and driving action generation. At present, the team is focusing on the development of a super-large-scale world base model with 72 billion parameter scale. The cloud model constructs the whole process technology link from base model pre-training, reinforcement learning post-training, model distillation, vehicle model pre-training, and deployment on the car. The whole system adopts the technical route of combining reinforcement learning and model distillation, which can efficiently produce end-side deployment models with small volume and high intelligence level.

Li Auto’s World Model Application:
In the field of intelligent driving, reinforcement learning (RL) faces the problem of training bias due to insufficient environmental authenticity. MindVLA relies on its self-developed cloud unified world model, which integrates reconstruction and generation technologies. Among them, the reconstruction model has the ability to restore 3D scenes, while the generative model can realize new perspective completion and unseen perspective prediction. By combining these two technical paths, MindVLA constructs a simulation environment that is close to the real world and conforms to the laws of the physical world, providing an effective solution to solve the problem of training bias.

The world model covers various traffic participants and environmental elements to build a virtual real traffic world. It uses a self-supervised learning framework to realize dynamic 3D scene reconstruction based on multi-perspective RGB images to generate scene representations containing multi-scale geometric features and semantic information. The scene is modeled in the form of a 3D Gaussian point cloud, and each Gaussian point integrates parameters such as position, color, transparency, and covariance matrix to efficiently render light and shadow and spatial structures in complex traffic environments.

Relying on the strong simulation capabilities of the world model, MindVLA can carry out millions of kilometers of driving simulations in the cloud virtual 3D environment, replacing some real vehicle road tests, and accurately verifying real-world problems at low cost, which significantly improves the efficiency and effectively responds to many challenges posed by model black box. Through massive simulation testing and optimization in the world model, the VLA can continuously improve its own decision-making and behavior, and truly "learn from mistakes". Ensure safety and reliability in actual driving.

1 Foundation of End-to-end Intelligent Driving Technology
1.1 Terminology and Concept of End-to-end Intelligent Driving
Terminology Explanation of End-to-end Intelligent Driving
Terminology Explanation of End-to-end Intelligent Driving
Connection and Difference between End-to-end Related Concepts

1.2 Introduction to and Status Quo of End-to-end Intelligent Driving
1.2.1 Overview
Background of End-to-end Intelligent Driving
Deduction of Impacts of AI Foundation Models on Intelligent Driving Industry Pattern
Reason for End-to-end Intelligent Driving: Business Value
Transformer Empowers Autonomous Driving
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
End-to-end Architecture Evolution
End-to-end Autonomous Driving Evolution Path
Progress in End-to-end Intelligent Driving (1)
Progress in End-to-end Intelligent Driving (2)
Progress in End-to-end Intelligent Driving (2)
Comparison between One Model and Two Model End-to-end Intelligent Driving
Performance Parameter Comparison between One Model/segmented End-to-end Systems
Significance of Introducing Multi-modal Models to End-to-end Intelligent Driving
Problems and Solutions for End-to-end Mass Production: Computing Power Supply/Data Acquisition
Problems and Solutions for End-to-end Mass Production: Team Building/Explainability
Progress and Challenges of E2E-AD: Generative World Model + Neural Network Simulator + RL Will Accelerate Innovation
Perception Layer under End-To-End Architecture

1.2.2 End-to-end Model Implementation Modes
Two Implementation Modes for End-To-End
End-To-End Implementation Modes: Imitation Learning
End-to-end Implementation Method: Reinforcement Learning
Mainstream reinforcement learning algorithms

1.2.3 End-to-end Model Verification Modes
Evaluation Methods for End-to-End Autonomous Driving Datasets
Closed Loop Benchmarking Platform for Evaluating End-To-End Autonomous Driving Systems (E2E-AD) Capabilities
Three Major Simulation Tests for End-to-end Autonomous Driving Models (2) — HUGSIM
Three Major Simulation Tests for End-to-end Autonomous Driving Models (3) — DriveArena

1.3 Typical End-to-End Intelligent Driving Cases
SenseTime UniAD: Path Planning-oriented AI Foundation Model Provides E2E Commercial Scenario Applications (1)
SenseTime UniAD: Path Planning-oriented AI Foundation Model Provides E2E Commercial Scenario Applications(2)
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
Shanghai Qi Zhi Institute’s E2E-AD Model SGADS: A Safe and Generalized E2E-AD System Based on Reinforcement Learning and Imitation Learning
Shanghai Jiao Tong University’s E2E Active Learning ActiveAD Case: Breaking the Data Labeling Bottleneck of Intelligent Driving, and Data-driven
End-to-end Intelligent Driving System Based on Foundation Models

1.4 Foundation Models
1.4.1 Foundation Models Introduction
Core of End-to-end System - Foundation Models
Foundation Models (1) - Large Language Models: Application Case in Intelligent Driving
Foundation Models (2) - Vision Foundation Models: Application in Intelligent Driving
Foundation Models (2) - Vision Foundation Models: Latent Diffusion Model Framework
Foundation Models (2) - Vision Foundation Models: Wayve GAIA-1
Foundation Models (2) - Vision Foundation Models: DriveDreamer Framework
Foundation Models (3) - Large Multimodal Models: MFM
Foundation Models (3) - Large Multimodal Models: Application of GPT-4V in Intelligent Driving

1.4.2 Foundation Models: Large Multimodal Models
Development of and Introduction to Large Multimodal Models
Large Multimodal Models VS Single-modal Foundation Models (1)
Large Multimodal Models VS Single-modal Foundation Models (2)
Technology Panorama of Large Multimodal 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 Intelligent Driving
Clip Model
LLaVA Model

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 Intelligent Driving
Application of VLMs in End-to-end Intelligent Driving
从VLM→VLA
VLA (Vision-Language-Action Model)
VLA Principle
Classification of VLA Models
VLA Is Not Yet Mature, and Waymo EMMA Is Relatively Complete Overall
Core Functions of End-to-end Multimodal Model for Intelligent Driving (EMMA)

1.6  World Models
Definition and Application
Basic Architecture
Difficulties in Framework Setting and Implementation
Video Generation Methods based on Transformer and Diffusion Model
WorldDreamer's Technical Principle and Path
World Model May Be One of the Ideal Ways to Achieve End-to-end
Generation of Virtual Training Data
Tesla’s World Model
Nvidia
InfinityDrive: Breaking Time Limits in Driving World Models
Parameter Performance of SenseAuto InfinityDrive
SenseAuto InfinityDrive Pipeline
SenseTime DiT Architecture and Main Indicators (FID/FV) for Evaluating Video Generation

1.7 Comparison between E2E-AD Motion Planning Models
E2E-AD Trajectory Planning: Comparison between Several Classical Models in Industry and Academia
Tesla: Perception and Decision Full Stack Integrated Model
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
GenAD: Generative End-to-end Model

1.8 VLA Model
VLA Concept
One Core of End-to-end: Large Language Models
VLA Technical Architecture and Key Technologies
Advantages of VLA (1)
Advantages of VLA (2)
Advantages of VLA (2)
Challenges in VLA Model Deployment (1)
Real-time and Memory Usage Challenges for VLA Model Deployment
Challenges in VLA Model Deployment (2)
Challenges of End-to-end Deployment - Data ( 2 )
Challenges of VLA Model Deployment - Planning Capabilities for Long Time Series Tasks

1.9 Diffusion Models
Four Mainstream Generation Models
Diffusion Model Principle
Diffusion Model Optimizes the Core Links of Autonomous Driving Trajectory Generation
Diffusion Model Optimizes Autonomous Driving Trajectory Generation
Application of Diffusion Model in the Field of Assisted Driving
Practical Application Cases of Diffusion Model

1.10 Embodied Language Models (ELM)
ELMs Accelerate the Implementation of End-to-end Solutions
ELMs Accelerate the Implementation of End-to-end Solutions
Application Scenarios (1)
Application Scenarios (2) Data Close Loop
Application Scenarios (3) Data Collecting
Application Scenarios (4) Location/ Description Labeling
Application Scenarios (5) Token Selection
Application Scenarios (6) Benchmark
Application Scenarios (7) Experiments
Application Scenarios (7) Experiments
Limitations and Positive Impacts

2 Technology Roadmap and Development Trends of E2E-AD
2.1 Technology Trends of End-to-End Intelligent Driving
Trend 1 - Paradigm Revolution in Intelligent Driving ADS: 2024 Can be Considered as the First Year of E2E-AD (E2E-AD)
Trend 2 - Major Development Frameworks of AGI: Robot and Intelligent Driving will be the Two Mainstream E2E Application Scenarios (1)
Trend 2 - Major Development Frameworks of AGI: Robot and Intelligent Driving will be the Two Mainstream E2E Application Scenarios (2)
Trend 2 - Major Development Frameworks of AGI: Robot and Intelligent Driving will be the Two Mainstream E2E Application Scenarios (3)
Trend 3: Development Direction of E2E-AD Is to Achieve Humanized Driving
Trend 4: Generative AI and E2E-AD Fused and Innovate, Scale of Data and Model Parameters Further Unleashes Potential of Basic Models (1)
Trend 4: Generative AI and E2E-AD Fused and Innovate, Scale of Data and Model Parameters Further Unleashes Potential of Basic Models (2)
Trend 5: E2E-AD Requires Higher Costs and Computing Power
Trend 6: General World Model is One of the Best Implementation Paths for Intelligent Driving (1)
Trend 6: General World Model is One of the Best Implementation Paths for Intelligent Driving (2)
Trend 6: General World Model is One of the Best Implementation Paths for Intelligent Driving (3)
Trend 7 - End-to-end Test Begins to Move from Open to Closed Loop
Trend 8: Application Ideas and Implementation Pace of Foundation Model in E2E-AD
End-to-end Trend 2: Foundation Model
End-to-end Trend 3: Zero-shot Learning

2.2 Market Trends of End-to-End Intelligent Driving
Layout of Mainstream E2E 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 Tier 1 Suppliers (3)
Comparison of End-to-end System Solution Layout between Other Intelligent 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 End-to-end System Solution Layout between OEMs (3)
Comparison of End-to-end System Solution Layout between Other Intelligent 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 End-to-end System Solution Layout between OEMs (3)
E2E System Enables Leading OEMs to Implement Map-free City NOA on a Large Scale
Comparison 1 of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs: Changan, GWM, BYD
Comparison 2 of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs ：FAW, GAC and Geely
Comparison 3 of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs : BAIC, SAIC, Chery and Dongfeng
Comparison 4 of NOA and End-to-end Implementation Schedules between Sub-brands of Domestic Mainstream OEMs: NIO, Xpeng Motors, Li Auto, Xiaomi and Leapmotor

2.3 End-to-end Intelligent 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)
Leading People in End-to-end Intelligent Driving For Domestic OEMs and Suppliers
E2E-AD Team Building of Domestic OEMs (1): XIAOMI
End-to-end Intelligent Driving Team Building of Domestic OEMs: Baidu /Li Auto
End-to-end Intelligent Driving Team Building of Domestic OEMs: Xiaomi Auto /Baidu
End-to-end Intelligent Driving Team Building of Domestic OEMs: Li Auto
E2E-AD Team Building of Domestic OEMs (4): Li Auto
E2E-AD Team Building of Domestic OEMs (5): XPeng
End-to-end Intelligent Driving Team Building of Domestic OEMs: BYD
End-to-end Intelligent Driving Team Building of Domestic OEMs: NIO
Team Building of End-to-end Intelligent Driving Suppliers: Momenta
Team Building of End-to-end Intelligent Driving Suppliers: DeepRoute.ai
End-to-end Intelligent Driving Team Building of Domestic OEMs: Huawei
Team Building of End-to-end Intelligent Driving Suppliers: Zhuoyu Technology
Team Building of End-to-end Intelligent Driving Suppliers: Horizon Robotics

3 End-to-end Intelligent Driving Suppliers
3.1 MOMENTA
Profile
R6 Flywheel Large Model  to be Released in 2025H2
One-model End-to-end Solutions (1)
One-model End-to-end Solutions (2)
End-to-end Planning Architecture
One-model 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
Mass Production and Clients

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
DeepRoute.ai  Announces Deep Cooperation with Volcano Engine
RoadAGI Platform—AI Spark
DeepRoute.ai VLA Model Architecture
End-to-end VLA Model Analysis
Designated End-to-end Mass Production Projects and VLA Model Features
Mass Production of DeepRoute.ai's End-to-end Solutions
Hierarchical Hint Tokens
End-to-end Training Solution - DINOv2
Application Value of DINOv2 in the Field of Computer Vision
Intelligent Driving VQA Task Evaluation Data Sets
BLEU (Bi-Lingual Evaluation Understudy) and Consensus-based Image Description Evaluation (CIDEr)
Score Comparison between HoP and Huawei

3.3 Huawei
End-to-end Evolution Path
ADS 4's All-New WEWA Architecture
Deep Integration of ADS 4 with XMC and Cloud-based Simulation Validation
ADS 4: Highway L3 Commercial Solution
End-to-End Mass Production Status
Development History of Huawei's Intelligent Automotive Solution Business Unit
ADS 2.0 (1): End-to-end Concept and Perception Algorithm of ADS
ADS 2.0 (2): End-to-end Concept and Perception Algorithm of ADS
Summary of Huawei ADS 2.0
ADS 3.0 (1)
ADS 3.0 (2): End-to-end
ADS 3.0 (3): 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): STELATO S9
End-to-end Solution Application Cases of ADS 3.0 (2): LUXEED R7
End-to-end Solution Application Cases of ADS 3.0 (3): AITO
End-to-end Intelligent 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
Urban Driving Assistance System HSD
Journey 6 Series Chips
UMGen: Unified Multimodal Driving Scene Generation Framework
GoalFlow: Goal Point Driven, Unlocking Future End-To-End Generative Policies
MomAD: Momentum-Aware Planning for End-To-End Autonomous Driving
DiffusionDrive: Towards Generative Multimodal End-To-End Autonomous Driving
RAD: End-To-End Reinforcement Learning Post-Training Paradigm Based On 3DGS Digital Twin World
Mass Production
End-to-end Super Drive and Its Advantages
Architecture and Technical Principle of Super Drive
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
Evolution of ClixPilot End-To-End Algorithm
Architecture of End-To-End World Model
Two-Phase Training Model of End-To-End World Model
Core Functions of Generative Intelligent Driving GenDrive
Key Technologies of Generative Intelligent Driving
Mass Production of End-To-End
Two-model End-to-end Parsing
One-model Explainable End-to-end Parsing
End-to-end Mass Production Customers

3.6 NVIDIA
Profile
Hydra-MDP++: NVIDIA's End-to-End Autonomous Driving Framework Combining Human Demonstrations and Rule-Based Expert Knowledge
World Foundation Model Development Platform — Cosmos
Cosmos Training Paradigm
Intelligent Driving Solution
DRIVE Thor
Basic Platform for Intelligent Driving: NVIDIA DriveOS
Core Design Philosophy of NVIDIA Multicast
Drive Thor
Latest End-to-end Intelligent Driving Framework: Hydra-MDP
Self-developed Model Architecture: Model Room
IM, NVIDIA and Momenta Collaborate to Create a Production-ready Intelligent Driving Solution Based on Thor
Latest End-to-end Intelligent Driving Framework: Hydra-MDP

3.7 Bosch
Vertical and Horizontal Assisted Driving Solution
Urban Assisted Driving Solution Based on End-to-End Model
End-to-End Mass Production Status
Intelligent Driving China Strategic Layout (1)
Based on the End-to-end Development Trend, Bosch Intelligent Driving initiates the Organizational Structure Reform
After Launching the BEV+Transformer High-level Intelligent Driving Solution, Bosch Accelerates Its End-to-end Intelligent Driving Layout
Intelligent Driving Algorithm Evolution Planning

3.8 Baidu
DriVerse: Novel Navigation World Model Enabled by Multimodal Trajectory Prompts and Motion Alignment
Profile of Apollo
Strategic Layout in the Field of Intelligent Driving
Two-model End-to-end: Adopting Strategy of Segmented Training before Joint Training
Production Models Based on Two-model End-to-end Technology Architecture: Jiyue 07
Baidu Auto Cloud 3.0 Enables End-to-end Systems from Three Aspects

3.9 SenseAuto
Profile
Evolution of Intelligent Driving End-To-End Algorithm
Architecture of R-UniAD
R-UniAD Practical Demonstration: Complex Scenario Mining, 4D Simulation Reproduction, Reinforcement Learning, Generalization Verification
Kaiwu World Model 2.0
Mass Production
UniAD End-to-end Solution
DriveAGI: The Next-generation Intelligent Driving Foundation Model and Its Advantages
DiFSD: SenseAuto’s End-to-end Intelligent Driving System That Simulates Human Driving Behavior
DiFSD: Technical Interpretation

3.10 QCraft
Profile
"Safe End-to-End" Architecture
"Safe End-to-End" Data and Model Training Closed Loop
Driven-by-QCraft Mid-to-High Level ADAS Solutions
End-to-End Mass Production Status
"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
Waymo Foundation Model
End-to-end Multimodal Model for Intelligent 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 GigaAI
Profile
World Model Evolution
4D Generative World Model Layered Architecture
World Model Implementation
ReconDreamer
DriveDreamer
DriveDreamer4D

3.14 LightWheel AI
Profile
Data Requirements for End-to-End Architecture
Tsinghua x LightWheel AI: Autonomous Driving World Model for Generating and Understanding Accident Scenarios
Core Technology Stack: Real2Sim2Real + Realism Validation
Data Annotation and Synthetic Data

3.15 PhiGent Robotics
Profile
Full-Domain End-To-End Driving Assistance Solution PhiGo Max
Detailed Explanation of Progressive One-Model End-To-End Solution Based on End-To-End Technology Paradigm
Detailed Explanation of End-To-End Technology

3.16 Nullmax
Profile
MaxDrive Driving Assistance Solution
Next-Generation Autonomous Driving Technology - Nullmax Intelligence
End-To-End Technology Architecture
End-To-End Data Platform
HiP-AD: Nullmax's End-To-End Autonomous Driving Framework Based on Multi-Granularity Planning and Deformable Attention
Mass Production

3.17 Mobileye
Profile
CAIS (Composite AI System) Route
Surround ADAS?
SuperVision?/Chauffeur
Production

3.18 Motovis
Profile
Full-stack End-to-end Intelligent Driving System
Production

4 End-to-end Intelligent Driving Layout of OEMs
4.1 Xpeng's End-to-end Intelligent Driving Layout
End-to-end System Evolution Path
World Foundation Model
Core Technical Approach of the World Foundation Model
Cloud Model Factory
Three Phased Achievements in World Foundation Model Development
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 Evolution
From E2E+VLM Dual System to MindVLA
Architecture of MindVLA Model
Key Technology Point 1 of MindVLA: Great 3D Physical Space Understanding Capability
Key Technology Point 2 of MindVLA: Combination with LLM
Key Technology Point 3 of MindVLA: Combination of Diffusion and RLHF
Key Technology Point 4 of MindVLA: World Model and INVADE-Accelerated Reinforcement Learning
End-to-end Solutions (1): Iteration of System 1
End-to-end Solutions (2): System 1 (end-to-end model) + System 2 (VLM)
End-to-end Solutions (3): Next-generation Intelligent Driving Technology Architecture
End-to-end Solutions (4): DriveMLM: Architecture
End-to-end Solutions (5): DriveMLM: Rendering Effects
End-to-end Solutions (6): DriveVLM: Processing of BEV and Text Features
End-to-end Solutions (7)：L3 Intelligent Driving
End-to-end Solutions (8): Build a Complete Foundation Model from AD Max 3.0
Technical Layout: Chip
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)：Focus on Perception
Development History of AD Algorithms (2): Shadow Mode
Development History of AD Algorithms (3): Introduction of Occupancy Network
Development History of AD Algorithms (4): Occupancy Network
Development History of AD Algorithms (5): HydraNet
Development History of AD Algorithms (6)：FSD V12
Tesla: Core Elements of the Full-stack Perception and Decision Integrated Model
"End-to-end" Algorithms
World Models
Data Engines
Dojo Supercomputing Center: Overview
Dojo Supercomputing Center: D1 Chip-integrated Training Tile
Dojo Supercomputing Center: Computing Power Development Planning
Zeron Won Runner-up in International End-to-End Ground Challenge

4.4 Zeron’s End-to-end Intelligent Driving Layout
Profile
End-to-end Intelligent Driving System Based on Foundation Models
Advantages of End-to-end Driving System

4.5 Geely & ZEEKR’s End-to-end Intelligent Driving Layout
One-Model End-to-End Large Model
L3 Intelligent Driving Technology Architecture
“Qianli Haohan” Advanced Intelligent Driving Solutions
Geely’s ADAS Technology Layout: Geely Xingrui Intelligent Computing Center
Xingrui AI Foundation Model
Application of Geely’s Intelligent Driving Foundation Model Technology
Zeekr Overview
ZEEKR’s End-to-end System: Two-model Solution
ZEEKR Officially Released 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 VLA Autonomous Driving Solution Orion
Analysis of the ORION Framework
Physical World Modeling Architecture
Multi-stage End-to-end Modeling Of Three Layers
Long Video Generation Framework—MiLA
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：end-to-end +VLM

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 – NWM (NIO World Model)
Intelligent Driving Architecture: NADArch 2.0
End-to-end R&D Tool Chain
Imagination, Reconstruction and Group Intelligence of World Models
Nsim (NIO Simulation)
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
Beidou Tianshu 2.0 - Tianshu Intelligent Driving
Tianshu Intelligent Driving Software Architecture
Brand Layout
ADAS Strategy: "Beidou Tianshu"
End-to-end System (1)：BEV+LLM+GoT
Production Models with End-to-end System: Changan NEVO E07
Production Models with End-to-end System: Changan NEVO E07

4.9 Mercedes-Benz's End-to-end Intelligent Driving Layout
Mercedes-Benz and Momenta team up to develop driver assistance systems
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
Five Intelligent Driving Technologies
E2E Autonomous Driving Architecture and Advantages
E2E Autonomous Driving Digital Assessment Technology
Falcon Intelligence Driving  E2E  Solution
Falcon Solution Models and Future Planning
Chery’s End-to-end System Development Planning

4.11 GAC
X-Soul Zhixing E2E Embodied Reasoning Model Architecture
Core Technologies
Intelligent Diviring Product Platform

4.12 Leapmotor
E2E Intelligent Driving
E2E Intelligent Driving Application Scenario

4.13 IM Motors
Iteration History of Intelligent Driving System
Cooperation with Momenta
IM AD E2E 2.0 Large Model
IM AD E2E 2.0 Intelligent Driving Large Model Technology
IM AD E2E 2.0 Intelligent Driving Large Model  Application Scenario

4.14 Hongqi
Hongqi Sinan Intelligent Driving Technology Architecture
Sinan E2E  Large Model Technology
Sinan Intelligent Driving Solution
Sinan Intelligent Driving Solution Models and Future Planning

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