Intelligent Driving End-to-End Large Model Research Report, 2026

Description

Research on Intelligent Driving Large Models: A Critical Period for Technological Competition and Paradigm Integration

As autonomous driving technology rapidly iterates from L2 to L3-L4, intelligent driving systems are shifting profoundly from traditional rule-driven architectures to the new generation of data-driven + cognition-driven architectures. As the underlying core enabler, intelligent driving large models have become the core track in industry competition. As the accelerated arrival of the Physical AI era, autonomous driving stands as its first large-scale application scenario, promoting automobiles to evolve rapidly into super agents that transcend the nature of traditional transportation tools and become all-scenario intelligent hubs connecting mobility, mobile office, home life, and third-party ecosystems.

From an industrial perspective, Physical AI remains in the early stage of technological fission, and the global autonomous driving market holds massive untapped potential. According to the data, there is a global ownership of about 1.5 billion passenger cars, 280 million commercial vehicles and trucks, and 18 million operating taxis. The total annual global driving mileage reaches 13 trillion kilometers, while the autonomous driving mileage is only 700 million kilometers, accounting for only about 0.006%. The future incremental potential is significant.

Judging further from the pace of technological implementation, intelligent driving large models are ushering in a critical technological iteration window period. The segmented end-to-end solution has come into mass production during 2024-2025, and the one-model end-to-end and VLA technologies are intensively implemented during 2025-2026. Coupled with the continuous upgrading of intelligent driving experience and the accelerated maturation of L3-L4 high-level autonomous driving technology, physical AI is accelerating. ResearchInChina predicts three major evolution trends of intelligent driving large models.

Trend 1: The Core Focus of Autonomous Driving Large Model Evolution in 2026 Will Be Competition and Deep Integration of Multiple Technical Routes.

Bosch,Momenta Integration Mode 1: One-model End-to-End + World Model + Reinforcement Learning, Representative Suppliers: WeRide, Bosch and Momenta

Features: The one-model end-to-end model serves as the core neural network of intelligent driving, directly connecting sensor input and driving output with zero information loss and extremely high performance ceiling; the world model is responsible for future deduction of road conditions and can generate massive long-tail scenarios at low cost for simulation training; reinforcement learning iterates and optimizes in the deduction space relying on the reward mechanism, outputs the optimal driving strategy, and copes with various sudden working conditions. The combination of the three forms a powerful closed loop of "data generation (world model) -> policy training (reinforcement learning) -> decision and execution (end-to-end model)". This enables intelligent driving systems to learn from massive driving data and keep evolving.

Integration Mode 2: E2E + Foundation Model (VLM/VLA) + Reinforcement Learning + World Model, Representative Suppliers: Horizon Robotics and Afari Technology

Features: The vision-language large model acts as the "cerebrum" responsible for cognitive reasoning, and the small end-to-end model acts as the "cerebellum" responsible for rapid execution.

Horizon Robotics adopts the one-model E2E + VLM + reinforcement learning + world model. Horizon Robotics' "fast thinking + slow thinking" dual-track intelligent driving architecture takes reinforcement learning as the hub. On the one hand, it empowers the end-to-end intuition model through the world model and simulation training, enabling it to respond in milliseconds while complementing the ability to handle rare short-time-sequence long-tail scenarios. On the other hand, it empowers the VLM cognitive model through reasoning enhancement, strengthening its semantic understanding and logical reasoning capabilities for long-time-sequence complex scenarios. It finally realizes the migration of VLM capabilities to the vehicle model, and completes lightweight deployment by quantization and distillation, building a balanced closed loop of "millisecond-level fast response + long-time-sequence slow reasoning".

Afari Technology adopts the VLA + E2E + world model architecture, in which the VLA model is responsible for reasoning similar to the high-level decision by the slow system, and the E2E end-to-end algorithm is responsible for mapping actions similar to the fast system. The 32B-parameter large model is used for large-scale multimodal pre-training (VLM) -> distilled into a 7B lightweight model, balances performance and deployment (VLM) -> aligning perception and driving actions, introduces driving domain knowledge (VLA) -> supervised fine-tuning, and learns high-level driving strategies and behavioral norms -> reinforcement learning aligning human driving styles and safety constraints, realizing perception-decision-control closed-loop optimization.

Integration Mode 3: VLA + World Model, Representative Suppliers: Zhuoyu Technology and XPeng

Features: VLA is responsible for perceiving the current environment, learning historical driving patterns, and determining the next action. The world model is responsible for deducing how each target on the road will interact in the next 5 to 10 seconds. VLA is good at understanding the present but not predicting the future; the world model is good at prediction but does not reflect on and reason about the prediction results. The combination of the two constitutes a complete brain.

Trend 2: The VLA and world model fusion paradigm is expected to become one of the main ways for the implementation of Physical AI.

The core of the future evolution of intelligent driving large models is the fundamental reconstruction of the underlying paradigm from "imitating human driving" to "understanding the physical world". VLA and world model are not an either-or choice. The future intelligent driving large model will be a fusion of the two. At present, the divergence between the two routes lies in that VLA advocates believe that "understanding" is the premise of driving, while world model advocates believe that "prediction" is the key.

World model advocates believe that changes in the physical world are continuous and high-dimensional. Language is a discrete, low-dimensional symbolic system - the transformation from physics to language is inevitably accompanied by information loss. The world model directly operates physical representations with higher bandwidth. VLA advocates believe that the biggest advantage of VLA is that it can be fine-tuned with the world model or model-based reinforcement learning. It can absorb the advantages of the world model, while the world model cannot utilize the advantages of VLM/VLA. Language brings strong generalization capability for it is a compressed package of human common sense. VLA possesses "common sense reasoning" capability and Chain-of-Thought (CoT) via language, thus gaining self-explanation capability.

Based on the advantages and divergences of the two routes, the industry has begun to explore the fusion path of the two. At present, there are three mainstream fusion modes for VLA and world model: latent space unified fusion, in-depth fusion at the architectural level, and modular collaborative fusion (cloud simulator type).

Fusion Mode 1: Latent Space Unified Fusion, Representatives: Xiaomi OneVL and Huawei DriveVLA-W0

The core is to embed the prediction capability of the world model into the training objectives of VLA, rather than adding additional modules in the reasoning stage. Specifically, it adds a future image prediction task to the training process of the VLA model, allowing the model to not only learn to predict actions, but also the environmental state (i.e., future images) at future moments. This design forces the model to learn the underlying dynamic laws of the driving environment, rather than just fitting sparse action supervision signals.

Case 1 of Latent Space Unified Fusion: Xiaomi OneVL Autonomous Driving Model

On May 13, 2026, Xiaomi officially released Xiaomi OneVL, a fully open-sourced autonomous driving model which unifies the three technical routes of VLA, world model and latent space reasoning into the same framework. The core breakthrough of this model is the in-depth unification of multiple technical paradigms through latent space reasoning. Differing from traditional solutions that decompose the reasoning process into human-readable natural language and generate deduction logic word by word, Xiaomi OneVL directly completes end-to-end logical operations in the high-dimensional vectorized latent space. This latent space integrates both the scenario perception and understanding capability of VLA and the environmental time-series prediction capability of the world model, and all reasoning operations are carried out at the vector level rather than the text level, achieving a significant leap in reasoning efficiency compared with traditional VLA solutions.

In terms of implementation mechanism, firstly, two types of latent variables are introduced inside the model: visual latent token and language latent token. The former is responsible for encoding physical relationships and time-series changes in the scene, carrying the prediction capability of the world model. The latter is responsible for expressing driving intentions and semantic logic, carrying the understanding capability of VLA.

Secondly, OneVL introduces two auxiliary decoders, which are only used in the training stage. The language auxiliary decoder is responsible for restoring human-readable CoT text from the language latent token, explaining why the model makes a certain driving decision. The visual auxiliary decoder is responsible for predicting future frame visual tokens (images after 0.5 seconds and 1.0 seconds) from the visual latent token, allowing the model to predict scene changes. During inference, both decoders are removed, and the model directly outputs planning results, realizing one-step reasoning and completely eliminating the delay accumulation caused by autoregression.

Case 2 of Latent Space Unified Fusion: Huawei DriveVLA-W0 Predicts Future Images Through World Modeling Tasks

Traditional VLA models face a fundamental problem: Supervision Deficit. The input of VLA models is high-dimensional multimodal data (front-view image sequences, language instructions, historical actions, etc.), but the supervision signal is only low-dimensional action tokens. Most of the model's representation capacity is wasted, resulting in its inability to fully learn the complex dynamics of the driving environment, and the huge potential of VLA models cannot be effectively released.

As can be seen from the figure below, as the amount of training data increases from 700,000 frames to 7 million frames and then to 70 million frames (ever more data), the collision rate shows a downward trend, that is, the more training data, the better the safety. However, for the traditional VLA technical paradigm without the world model, when the data increases from 7 million frames to 70 million frames, the decline in collision rate slows down, indicating that data has limited effect on improving the safety performance of VLA.

To solve the sore points of VLA such as sparse supervision, failure of data scaling law, and lack of physical time-series prediction capability, Huawei proposed the DriveVLA-W0 training paradigm in its paper, introducing the world model to predict future images as dense self-supervision signals during the training stage, so as to increase future time-series prediction while maintaining the ability to understand environmental dynamics. Compared with traditional VLA, DriveVLA-W0 adds world modeling (predicting future road conditions): the more data, the greater the advantage is magnified, and the data scaling law is strengthened.

Specifically, it adds a future image prediction task to the training process of the VLA model, allowing the model to not only learn to predict actions, but also the environmental state (i.e., future images) at future moments. This design forces the model to learn the underlying dynamic laws of the driving environment, rather than just fitting sparse action supervision signals.

Fusion Mode 2: In-depth Fusion at the Architectural Level, Representative: VLA-World

Differing from pre-training fusion (external reinforcement), where the world model acts as an external tool to generate first and then transmit, in-depth fusion at the architectural level internalizes the world model capability into the native capability of VLA, with planning and generation growing together in the same architecture.

VLA-World, jointly proposed by Shanghai Jiao Tong University and Huawei Central Research Institute in April 2026, is an integrated VLA architecture with deeply embedded world model capabilities. In traditional solutions, the world model and VLA are independent of each other, with the former responsible for generating simulation videos and the latter for perception reasoning and decision output. VLA-World adopts a single VLA backbone network for feature sharing between visual generation and decision reasoning. It integrates trajectory prediction and visual generation into continuous links of the same decision chain, and follows the causal logic of predicting motion trajectory first and then deducing future images based on the trajectory, realizing deep module coupling and highly coherent reasoning chain.

Working Mechanism:

Trajectory Perception Conditioning: VLA-World predicts the trajectory first, and then generates future frames conditioned on the trajectory: the trajectory prediction result directly serves as the conditioning signal for visual generation to guide the generation process. In this way, the trajectory determines "where to go", and the image presents "what to see when arriving there", forming a causal dependency.

Unified Generation and Reasoning: Differing from the past when the world model and VLA were two independent modules, VLA-World enables the two to share the same VLA backbone, that is, unifying visual generation and reasoning in the same VLA structure.

GRPO End-to-End Alignment: GRPO (Group Relative Policy Optimization) is used to optimize the model during the reinforcement learning stage. The model generates multiple candidate trajectories and corresponding future images, and rewards those results where the "imagined future" is consistent with the "real safe decision". This mechanism makes visual generation no longer an independent task, but always serves the quality of downstream decisions.

Trend 3: The Evolution of Intelligent Driving AI Towards Foundation Models Accelerates, and the Industry Will Enter A Competition Period of General Cognitive and Reasoning Capabilities of Foundation Models.

2026 is the first year of the launch of autonomous driving foundation models. DeepRoute.ai, Afari Technology, Zhuoyu Technology, Li Auto, and XPeng have launched related products. The core of foundation models is to build a universal and reusable cognitive base for the physical world, realizing full-level intelligent driving compatibility and cross-scenario capability migration.

Firstly, autonomous driving is essentially a typical scaling problem, and current implementation is mainly restricted by insufficient model capacity and low efficiency of data closed-loop. First of all, the existing foundation models have limited scale and insufficient generalization capability for long-tail complex scenarios; secondly, high-value data mining relies on manual screening and review, with fragmentation and low automation, limiting long-term iterative capabilities.

To address the two bottlenecks of insufficient model capacity and inefficient data closed-loop, DeepRoute.ai proposed a solution, a unified 40B-parameter VLA foundation model. The core innovation lies in the "trinity" model role design, allowing the same model to play three roles simultaneously: driver (visual input -> real-time driving decision), analyst (diagnostic understanding of key scenarios), and critic/ referee (evaluating the safety and rationality of driving behavior), upgrading the driving system from a simple execution system to an intelligent system with cognitive capabilities.

In the pre-training stage, DeepRoute.ai abandons the traditional approach of the end-to-end model relying on trajectory supervision (data utilization rate is only 0.001%), and instead adopts the video prediction task, enabling the model to learn the dynamic structure of the real world by predicting video sequences, turning every pixel into a supervision signal and increasing the data utilization rate to nearly 100%.

In the core training stage (Mid-train), the model conducts joint training around three tasks: V+A (vision + action) to learn conventional end-to-end driving, V+A->L (explanation after action) to activate the analyst and critic roles, and V->L+A (multimodal logical reasoning) to train a driver with reasoning capability, using Chain-of-Thought to let the model first output language descriptions and decision logic of key events, and then output specific driving trajectories.

In terms of engineering implementation, DeepRoute.ai controls the single-step processing latency of 1,000 visual tokens and dozens of reasoning tokens within 60-85 milliseconds using optimization methods such as KV Cache, Multi-Token Prediction (MTP), model quantization, and self-developed reasoning engine, realizing 10-15Hz real-time closed-loop control capability. Moreover, the foundation model can be flexibly distilled according to the computing power of vehicle chips, and deploy a pure driving VA model on a 100 TOPS platform, and a VLA model with logical reasoning capability on a 500 TOPS platform.

Then the foundation model pre-trains to learn the physical laws and spatial logic of the real world, with native zero-shot migration capability. With a universal cognitive base, it adapts to all levels from L2 assisted driving to L4 autonomous driving through model distillation, computing power tailoring, and capability fine-tuning. It is first applied to autonomous driving, and will migrate to multiple tracks such as humanoid robots and industrial robots in the future, realizing "one foundation making all things intelligent".

In 2026, Zhuoyu Technology fully transforms its strategy. Taking the native multimodal foundation model as the technical base, it aims to upgrade from an "intelligent driving Tier 1 supplier" to a "mobile physical AI company", focusing on mass production expansion across all scenarios and vertical domains covering passenger cars, commercial vehicles, L4 products and overseas layout, and extending to the field of embodied robots.

Zhuoyu launched VLA (VLA World Model, native multimodal FM): it uses a unified Backbone to process visual, text, and sensor data, completes physical reasoning in the latent space, and directly outputs driving actions. From the pre-training stage, it conducts joint training with image/video/text/driving/robot data, and performs prediction and reasoning of the physical world in a unified latent space, understanding both semantics and physical laws.

In 2026, a critical year for the technological iteration and paradigm fusion of intelligent driving large models, the competition and integration of multiple technical routes, the collaborative implementation of VLA and world model, and the large-scale launch of foundation models will jointly promote the intelligent driving industry to accelerate from "technological exploration" to "large-scale implementation". Whether it is technological innovation of multi-route integration or generalized layout of foundation models, the core is to revolve around the goal of "safer, more efficient, and more adaptable to real driving scenarios". The trend of "physical AI" implementation will further drive intelligent driving systems to evolve from "imitating humans" to "understanding the world", realizing true intelligent driving.

In the future, with the continuous iteration of technologies and the coordinated improvement of the industry chain, intelligent driving large models will gradually break through existing bottlenecks, become the core support for the large-scale implementation of autonomous driving, reshape the development pattern of the mobility sector, and also facilitate the extension and application of mobile physical AI in more scenarios.

Product Code: DTT011

1 Fundamentals of End-to-End Autonomous Driving Technology

1.1 Terms and Concepts of End-to-End Autonomous Driving
Explanation of End-to-End Autonomous Driving Terminologies
Correlation and Differences of End-to-End Related Concepts
1.2 Introduction to End-to-End Autonomous Driving and Development Status
- 1.2.1 Overview
- Emerging Background of End-to-End Autonomous Driving
- Deduced Impacts of Large AI Models on the Pattern of Autonomous Driving Industry
- Reasons for the Emergence of End-to-End Autonomous Driving: Commercial Value
- Transformer Enables Autonomous Driving
- Differences between End-to-End and Traditional Architectures (1)
- Differences between End-to-End and Traditional Architectures (2)
- Evolution of End-to-End Architecture
- Evolution Route of End-to-End Autonomous Driving
- Comparison between One-Model and Two-Model End-to-End
- Performance Parameter Benchmarking of Mainstream One-Model/Segmented End-to-End Systems
- Challenges and Solutions for Large-Scale Mass Production of End-to-End: Computing Power Supply/Data Acquisition
- Challenges and Solutions for Large-Scale Mass Production of End-to-End: Team Building/Interpretability
- Progress and Challenges in End-to-End Systems: World Model Generation + Neural Network Simulator + RL Accelerating Innovation
- Perception Layer under End-to-End Architecture
- 1.2.2 Implementation Methods of End-to-End Models
- Two Implementation Approaches for End-to-End
- End-to-End Implementation Method: Imitation Learning
- End-to-End Implementation Method: Reinforcement Learning
- Basic Architecture and Definition of Reinforcement Learning
- Mainstream Reinforcement Learning Algorithms
- 1.2.3 Verification Methods of End-to-End Models
- Dataset Evaluation Methods for End-to-End Autonomous Driving
- Three Major Simulation Tests for End-to-End Autonomous Driving Models (1) - Bench2Drive
- 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 Classic End-to-End Autonomous Driving Cases
SenseTime UniAD: Path Planning-Oriented Large AI Model Provides E2E Commercial Scenario Applications
Technical Principles and Architecture of SenseTime UniAD
Technical Principles and Architecture of Horizon VAD
Technical Principles and Architecture of Horizon VADv2
Training of VADv2
Technical Principles and Architecture of DriveVLM
Li Auto Adopts Mixture-of-Experts (MoE) Architecture
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 ActiveAD Active Learning Case: Solving Data Labeling Bottleneck from A Data-centric Perspective
Most End-to-End Autonomous Driving Systems Are Developed Based on Foundation Models
1.4 Foundation Models
- 1.4.1 Introduction to Foundation Models
- Significance of Introducing Multimodal Models into End-to-End Autonomous Driving
- Core of End-to-End Systems - Foundation Models
- Foundation Model 1: Large Language Model (LLM) - Application Cases in Autonomous Driving
- Foundation Model 2: Vision Foundation - Application in Intelligent Driving
- Foundation Model 2: Vision Foundation - Latent Diffusion Models Framework
- Foundation Model 2: Vision Foundation - Wayve GAIA-1
- Foundation Model 2: Vision Foundation - DriveDreamer Framework
- Foundation Model 3: Multimodal Foundation Model - MFM
- Foundation Model 3: Multimodal Foundation Model - Application of GPT-4V in Intelligent Driving
- 1.4.2 Foundation Models - Multimodal Foundation Model
- Development and Overview of Multimodal Foundation Model
- Multimodal Foundation Model vs. Single-Modal Foundation Model (1)
- Multimodal Foundation Model vs. Single-Modal Foundation Model (2)
- Technical Panorama of Multimodal Foundation Model
- Multimodal Information Representation
- 1.4.3 Foundation Models - MLLM
- Multimodal Large Language Model (MLLM)
- Architecture and Core Components of Multimodal Large Language Model
- Mainstream Multimodal Large Language Models
- Application of Multimodal Large Language Model in Intelligent Driving
- CLIP Model
- LLaVA Model
1.5 Vision-Language Model (VLM)
Application of Vision-Language Model (VLM) in Intelligent Driving
Application of Foundation Models in Autonomous Driving
Application of Vision-Language Model (VLM)
Development History of Vision-Language Model (VLM)
Architecture of Vision-Language Model (VLM)
Application Principles of VLM in End-to-End Autonomous Driving
Application of VLM in End-to-End Autonomous Driving
Challenges Faced by VLM Models in Intelligent Driving
1.6 Vision-Language-Action Model (VLA)
VLM->VLA
VLM +E2E ->VLA
Analysis of VLA Architecture
Typical VLA Architectures
VLA Architecture Analysis Case: Disassembling Li Auto MindVLA Architecture (1)
VLA Architecture Analysis Case: Disassembling Li Auto MindVLA Architecture (2)
Concept of VLA Large Models
Principles of VLA Model
Classification of VLA Models
Interpretation of VLA Technology Evolution
Large Language Model as One of the Cores of End-to-End
Technical Architecture and Key Technologies of VLA
Advantages of VLA (1)
Advantages of VLA (2)
Advantages of VLA (3)
Deployment Challenges of VLA Model - Real-Time Response Capability
Real-Time Performance and Memory Occupancy Challenges of VLA Model Deployment
Deployment Challenges of VLA Model - Data (1)
Deployment Challenges of VLA Model - Data (2)
Deployment Challenges of VLA Model - Long-Term Task Planning Capability
Evolution Route of VLA Large Models
Representative Models of VLA Technical Paradigms
VLA Datasets and Benchmarks
1.7 World Model
World Model Prototype: Mental Model (1)
World Model Prototype: Mental Model (2)
Key Definitions and Application Development of World Model
Basic Architecture of World Model
Three Core Values of World Model Empowering Autonomous Driving
Two Major Technical Routes of World Model
Generative World Model DIAMOND: Diffusion Model + Real-Time RL Adaptation + Long-Term Stability
Generative Interactive World Model Genie: Unsupervised Learning of Real-World Physical Laws from Unlabeled Internet Videos
Technical Principles and Paths of WorldDreamer
Implicit World Model: Technical Principles and Paths of V-JEPA2
Implicit World Model: Technical Principles and Paths of Comma.ai
Framework Setting and Implementation Difficulties of World Model
Video Generation Methods Based on Transformer and Diffusion Models
World Model May be One of the Ideal Approaches to Realize End-to-End Autonomous Driving
World Model - Generation of Virtual Training Data
World Model - Tesla World Model
World Model - NVIDIA
InfinityDrive: Breaking Time Limits in Driving World Models
Parameter Performance of SenseAuto InfinityDrive
Pipeline of SenseAuto InfinityDrive
SenseTime DiT Architecture and Main Video Generation Evaluation Metrics FID/FV
Deployment Challenges of World Model in Autonomous Driving
1.8 Comparison between End-to-End Large Model Technical Paradigms
- 1.8.1 Technical Paradigm Comparison: Modular End-to-End vs. One-Model End-to-End vs. VLM/VLM+E2E/VLA
- Summary of Comparison between Three Mainstream Intelligent Driving Models (1): Modular / One-Model End-to-End / Foundation Model-Based Autonomous Driving Paradigm
- Summary of Comparison between Three Mainstream Intelligent Driving Models (2): Modular / One-Model End-to-End / Foundation Model-Based Autonomous Driving Paradigm
- Summary of Comparison between Three Mainstream Intelligent Driving Models (3): Modular / One-Model End-to-End / Foundation Model-Based Autonomous Driving Paradigm
- Definition and Classification of Generalized End-to-End (GE2E)
- Comparison of Different GE2E Autonomous Driving Paradigms: Planning-Only E2E vs. Multi-Task E2E
- Comparison of Different GE2E Autonomous Driving Paradigms: VLM-Driven Cognitive End-to-End Driving
- Comparison between Two Technical Paradigms: VLM + Traditional E2E
- Architecture Summary of Various GE2E Autonomous Driving Models
- Performance Comparison between Various GE2E Autonomous Driving Models
- 1.8.2 Technical Paradigm Comparison: VLA vs. World Model
- VLA vs. World Model: Who will Win?
- Performance Competition between VLA and World Model
- Summary of Comparison between VLM/VLA/World Models
1.9 Diffusion Models
Four Mainstream Generative Models
Principles of Diffusion Models
Diffusion Models Optimize Core Links of Intelligent Driving Trajectory Generation
Diffusion Models Optimize Intelligent Driving Trajectory Generation
Application of Diffusion Models in Intelligent Driving
Practical Application Cases of Diffusion Model

2 Technical Routes and Development Trends of End-to-End Autonomous Driving

2.1 Technical Trends of End-to-End Autonomous Driving
Summary of Evolution Route of Intelligent Driving End-to-End Large Models
Trend 1: The Core Focus of Autonomous Driving Large Model Evolution in 2026 Will Be Competition and Deep Integration of Multiple Technical Routes
Integration Case 1: Overall Architecture of Afari Technology's Autonomous Driving System Adopts VLA+E2E Collaborative Closed Loop
Integration Case 2: L3-Capable World Action Model (WAM) Builds Trinity Architecture of "VLA + World Model + Safety Adversarial Model"
Trend 2: VLA and World Model Fusion Paradigm Is Expected to Become One of the Mainstream Approaches for Physical AI Implementation
VLA+World Model Integration Case 1: Xiaomi OneVL Unifies VLA and World Model into One Framework
Disassembly of Xiaomi OneVL Architecture
VLA+World Model Integration Case 2: XPeng Launches X-World
VLA+World Model Integration Case 3: Huawei DriveVLA-W0 Predicts Future Images via World Modeling Tasks
Disassembly of DriveVLA-W0 Architecture
DriveVLA-W0 Leverages World Models to Amplify Autonomous Driving Data Scaling Law
VLA+World Model Integration Case 4: Bosch ExploreVLA Introduces World Model Based on VLA+RL to Achieve Three Major Breakthroughs
Disassembly of Bosch ExploreVLA Model Architecture
Trend 3: Autonomous Driving Is Entering the Physical AI Stage
Ultimate Form of Physical AI Connects Digital and Physical Worlds, and Autonomous Driving Serves as Its Optimal Implementation Carrier
Trend 4: Evolution of Intelligent Driving AI Towards Foundation Models Accelerates, and the Industry Will Enter A Competition Period of General Cognitive and Reasoning Capabilities of Foundation Models
Case 1: Hardcore Technological Innovations in DeepRoute 40B VLA Foundation Model
Case 2: Core of 2026 Strategy of Zhuoyu Technology: Building Mobile Intelligent Foundation Model (1)
Case 2: Core of 2026 Strategy of Zhuoyu Technology: Building Mobile Intelligent Foundation Model (2)
Case 3: XPeng World Foundation Model
Trend 5: End-to-End Autonomous Driving Has Entered the Stage of Data Closed-Loop Competition and Refined Operation
Case: NVIDIA MOSAIC
Trend 6: Robots and Intelligent Driving Become Two Mainstream E2E Application Scenarios on the Road to AGI (1)
Trend 6: Robots and Intelligent Driving Become Two Mainstream E2E Application Scenarios on the Road to AGI (2)
2.2 End-to-End Autonomous Driving Market Trends
Comparison of End-to-End Autonomous Driving Large Model Layout between ADAS Tier 1 Suppliers (1)
Comparison of End-to-End Autonomous Driving Large Model Layout between ADAS Tier 1 Suppliers (2)
Comparison of End-to-End Autonomous Driving Large Model Layout between ADAS Tier 1 Suppliers (3)
Comparison of End-to-End Autonomous Driving Large Model Layout between ADAS Tier 1 Suppliers (4)
Comparison of End-to-End Autonomous Driving Large Model Layout between ADAS Tier 1 Suppliers (5)
Solution Layout Comparison between Other End-to-End Autonomous Driving System Suppliers
Comparison of End-to-End Autonomous Driving Large Model Layout between OEMs (1): Xiaomi, XPeng, Li Auto, NIO
Comparison of End-to-End Autonomous Driving Large Model Layout between OEMs (2): Changan, BYD, Leapmotor
Comparison of End-to-End Autonomous Driving Large Model Layout between OEMs (3): Chery, Dongfeng, IM Motors
Comparison of End-to-End Autonomous Driving Large Model Layout between OEMs (4): GAC, FAW Hongqi, Geely

3 End-to-End Autonomous Driving Suppliers

3.1 Afari Technology - End-to-End Autonomous Driving Model
Profile
Fully Entering into AI-Driven Intelligent Vehicle Era
AI + Vehicle Strategy
Top-Level Strategy and Commercial Closed Loop
Ecosystem Alliance
Judgment on Next-Generation End-to-End Architecture Trend (1)
Judgment on Next-Generation End-to-End Architecture Trend (2)
Judgment on Next-Generation End-to-End Architecture Trend (3)
End-to-End Large Model Architecture: E2E2.0+VLA
E2E Architecture
World Model Closed-Loop Simulation Architecture
Native Intelligent Driving Foundation Model
Three Major Businesses (1)
Three Major Businesses (2): Robotaxi Deployment Plan, 2026-2030
Evolution Route of Intelligent Driving Solutions (ASD1.0 to ASD4.0) and End-to-End Large Model
Mass Production of Chongqing Qianli Intelligent Driving Technology Co., Ltd.
3.2 Horizon Robotics - End-to-End Autonomous Driving Large Model
Ultimate Strategic Roadmap: 2025-2030+
Three Strategic Evolutions
Latest Product Launches in 2026 (1)
Latest Product Launches in 2026 (2)
Adopts One-Model End-to-End + VLM Solution
Introduction of Reinforcement Learning and World Model
Thoughts on One-Model End-to-End Large Models
Urban Driving Assistance System: HSD
Journey 6 Series Chips
SparseDriveV2 (1)
SparseDriveV2 (2)
UMGen: Unified Framework for Multimodal Driving Scene Generation
GoalFlow: Goal-Driven Approach Unlocking New Future of Generative End-to-End Strategies
MomAD: Momentum-Aware Planning in End-to-End Autonomous Driving
DiffusionDrive: Towards Generative Multimodal End-to-End Autonomous Driving
RAD: Post-Training Paradigm of End-to-End Reinforcement Learning Based on 3DGS Digital Twin World
Mass Production
Super Drive High-Level Intelligent Driving and Its Advantages
Architecture and Technical Principles of Super Drive
Senna Intelligent Driving System (Large Model + End-to-End)
Core Technologies and Training Methods of Senna
Core Modules of Senna
3.3 Zhuoyu Technology - Intelligent Driving Large Model
Comparison of Three Intelligent Driving Model Paradigms: One-Model End-to-End, World Model and VLA (1)
Comparison of Three Intelligent Driving Model Paradigms: One-Model End-to-End, World Model and VLA (2)
Launched Mobile Physical AI Foundation Model in 2026: Native Multimodal Foundation Model
Comparison between Three VLA Technical Paradigms and Zhuoyu's 2026 Native Multimodal Foundation Model
Evolution Route of ClixPilot End-to-End Large Model (1)
Evolution Route of ClixPilot End-to-End Large Model (2)
End-to-End World Model Architecture
Two-Stege Training Model for End-to-End World Model
Core Functions of Generative Intelligent Driving GenDrive
Core Technologies of Generative Intelligent Driving
Two-Model End-to-End
Interpretable One-Model End-to-End
Mass Production and Clients of End-to-End
3.4 NVIDIA - Intelligent Driving Large Model
Ten-Year Layout of Autonomous Driving Business
L2++/L4 Intelligent Driving Plan (2026-2030)
L3 and L4 Implementation Roadmap of NVIDIA
DRIVE Full-Stack Driving Assistance Platform: 5-Layer Architecture
Drive Hyperion 10 (1): Hardware Configuration
Drive Hyperion 10 (2): Software Architecture
Building Autonomous Driving Safety and AI Ecosystem Based on Halos OS
DRIVE AV Intelligent Driving Large Model Solution: VLA + Classic Rule-Based Algorithms
E2E+VLM->Drive VLA (1)
E2E+VLM->Drive VLA (2)
VLA On-Vehicle Deployment Solution (1)
VLA On-Vehicle Deployment Solution (2)
Launched Alpamayo 1.5
Drive VLA Technical Route: 10B Large Model Alpamayo 1.5
New-Generation In-Vehicle Computing Platform - Drive Thor
World Foundation Model Development Platform - Cosmos
Cosmos Training Paradigm
NVIDIA DriveOS: Foundation Platform Built for Autonomous Driving
Core Design Concept of NVIDIA Multicast
End-to-End Intelligent Driving Framework - Hydra-MDP
Self-Developed Model Architecture - Model Room
3.5 Momenta - Intelligent Driving Large Model
Profile
R7 Reinforcement Learning World Model
Mass-Produced Vehicles Equipped with R7
R6 Flywheel Large Model
Disassembly of One-Model End-to-End
Algorithm Development Path
Evolution Roadmap of Intelligent Driving Large Models
Intelligent Driving Technology Evolution and Industrial Paradigm Changes
End-to-End Planning Architecture
End-to-End Large Model Mass Production Solutions
3.6 DeepRoute.ai - Intelligent Driving Large Model
Product Layout and Strategic Deployment
Launched Unified Foundation Model in 2026
Principle, Architecture and Technical Highlights of 40B VLA Foundation Model (1)
Principle, Architecture and Technical Highlights of 40B VLA Foundation Model (2)
Principle, Architecture and Technical Highlights of 40B VLA Foundation Model (3)
Value Brought by Foundation Models
End-to-End Intelligent Driving Large Model Evolution, 2023-2026
DeepRoute IO 2.0: VLA 2.0 (1)
DeepRoute IO 2.0: VLA 2.0 (2)
VLA2.0 Designated Mass Production Projects
Adopted End-to-End Intelligent Driving Solutions in 2023
In-Depth Cooperation with Volcano Engine in 2025
Implementation Platform of RoadAGI - AI Spark
End-to-End VLA Model: VLA1.0
End-to-End VLA Model: Architecture of VLA1.0
End-to-End 1.0 Designated Mass Production Projects
Introduction of Hierarchical Hint Tokens
End-to-End Training Solution - DINOv2
Application Value of DINOv2 in Computer Vision
VQA Evaluation Dataset for Intelligent Driving
BLEU Evaluation Metrics and CIDEr Automatic Evaluation Metric for Image Caption Generation Tasks
Score Comparison between DeepRoute HoP and Huawei Solution
3.7 Huawei - End-to-End Intelligent Driving Large Model
Evolution Roadmap of Qiankun Intelligent Driving Large Model (ADS2.0 to ADS5)
ADS 5 (1): WEWA 2.0 Architecture
Comparation between WEWA2.0 and WEWA1.0
ADS 5 (2): Computing Power
ADS 5 (3): Benchmarking of Four Versions and Production Vehicle Models
Hierarchical Architecture of Pangu Large Model
Pangu Model Product System (1)
Pangu Model Product System (2)
ADS 4: WEWA 1.0
In-Depth Integration of ADS 4 and XMC, and Cloud Simulation Verification
ADS 4: Commercial L3 Highway Solution
Mass Production of ADS 4 End-to-End
ADS 2.0 (1): End-to-End Concept and Perception Algorithm
ADS 2.0 (2): End-to-End Concept and Perception Algorithm
Summary of ADS 2.0
ADS 3.0 (1): End-to-End
ADS 3.0 (2): End-to-End
ADS 3.0 (3): ASD3.0 VS. ASD2.0
ADS 3.0 End-to-End Application Case (1): STELATO S9
ADS 3.0 End-to-End Application Case (2): LUXEED R7
ADS 3.0 End-to-End Application Case (3): AITO Series
Architecture and Principles of Perception-Enhanced World-Awareness-Action Model (Percept-WAM) (1)
Architecture and Principles of Perception-Enhanced World-Awareness-Action Model (Percept-WAM) (2)
Architecture and Principles of Perception-Enhanced World-Awareness-Action Model (Percept-WAM) (3)
Multimodal LLM End-to-End Autonomous Driving Solution
End-to-End Test - VQA Tasks
Architecture of DriveGPT4
End-to-End Training Solution Case
Two Training Stages of DriveGPT4
Comparison between DriveGPT4 and GPT4V
3.8 QCraft - Intelligent Driving Large Model
Product Matrix in Intelligent Driving: Three-Tier Product Matrix of Intelligent Driving System QPilot 2.0
Mass-Produced Urban NOA End-to-End Solution Based on Single Journey 6M Chip
Core Technologies Implementing Urban NOA with Single J6M Chip: Interpretable One-Model End-to-End
Core Technologies Enabling Ultimate Urban NOA Experience: VLA and World Model Architecture
Evolution of Intelligent Driving Large Models
Intelligent Driving Solution Evolution Roadmap
Data and Model Training Closed Loop
Ecosystem Partners Panorama
3.9 Bosch - Intelligent Driving Large Model
Zongheng Driving Assistance Solution
Urban Driving Assistance Solution Based on End-to-End Model
China Strategic Layout of Bosch Mobility
Bosch Mobility Launched New Organizational Restructuring and Strategic Cooperation Based on End-to-End Development Trends
Adopt One-Model End-to-End for Mass Production Solutions
End-to-End Technical Route of Premium Zongheng Driving Assistance Solution
Disassembly of One-Model End-to-End Technical Paradigm
Comparison between End-to-End Mass Production Solutions
Overall Design Idea of CriticVLA
Architecture of CriticVLA (1)
Architecture of CriticVLA (2)
Classification System of Foundation Models for Autonomous Driving Trajectory Planning
Customized Foundation Models for Trajectory Planning: Fine-Tuning
Foundation Model for Autonomous Driving Trajectory Planning: Customized Foundation Models for Trajectory Planning
Foundation Model for Autonomous Driving Trajectory Planning: Models Focused Solely on Trajectory Planning
Models and Core Features of Trajectory Planning Methods with Language Interaction Capability
Core Features of Models with Action Interaction Capability: Training Datasets, Training Methods and Evaluation Metrics
3.10 WeRide - End-to-End Large Model
Profile
Business Model
Financial Overview, 2023-2025
Five Major Product Matrices
Exploration of Business Model for L4 Autonomous Driving Multi-Scenario Application
Traditional Autonomous Driving Architecture: Two Major Problems of Perception-Prediction-Planning-Control Modular Pipeline
Unsolved Problems of One-Model End-to-End
E2E + Traditional Pipeline Dual Architecture
E2E Model Architecture
Evolution Route of End-to-End Autonomous Driving Large Models
Hardware Architecture of Gen8 L4 Autonomous Driving System
HPC 3.0
Self-Developed General Simulation Model: WeRide GENESIS
3.11 Pony.ai - End-to-End Intelligent Driving Large Model
Profile
Three Major Business Lines and Business Model
Robotaxi Business Layout
Business Model of Robotaxi
Revenue Overview, 2024-2025
Comparative Analysis between Pony.ai and WeRide: Market Value, Revenue, Business, Robotaxi Business and Intelligent Driving Models
PonyWorld World Model 2.0 (1)
PonyWorld World Model 2.0 (2)
PonyWorld World Model 2.0 (3)
PonyWorld World Model 2.0 (4)
E2E End-to-End Intelligent Driving Model
Evolution Route of 1st to 7th Generation Robotaxi Products
Released New-Generation Autonomous Driving Domain Controller
Ecosystem Partners
3.12 Baidu - End-to-End
DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment
Overview of Baidu Apollo
Robotaxi Business Layout
Commercial Implementation Progress of Robotaxi (1): Overseas Markets
Commercial Implementation Progress of Robotaxi (2): Domestic Market
Key Nodes of Robotaxi Deployment in 8 Cities in China, 2021-2026
Two-Model End-to-End: Adopt the Strategy of Segmenting First and Then Joint Training
Production Vehicle Equipped with Two-Model End-to-End Architecture: Jiyue 07
Baidu Automotive Cloud 3.0 Enables End-to-End Systems in Three Aspects (1)
Baidu Automotive Cloud 3.0 Enables End-to-End Systems in Three Aspects (2)
3.13 SenseAuto - End-to-End
Profile
Technical Route Analysis 1: End-to-End Autonomous Driving Evolution Roadmap
Technical Route Analysis 2: Analysis of Generative Intelligent Driving R-UniAD (1)
Technical Route Analysis 3: Analysis of Generative Intelligent Driving R-UniAD (2)
Architecture of R-UniAD
Practical Demonstration of R-UniAD: Complex Scene Mining, 4D Simulation Reproduction, Reinforcement Learning and Generalization Verification
Kaiwu World Model 2.0
Mass Production
Released UniAD End-to-End Solution
DriveAGI: New-Generation Intelligent Driving Large Model and Its Advantages
DiFSD: End-to-end Intelligent Driving System That Simulates Human Driving Behaviors
DiFSD: Technical Interpretation
3.14 Wayve - Intelligent Driving Large Model
Profile
Advantages of AV 2.0
Latest Progress: Architecture of GAIA-1 World Model
GAIA-1 World Model - Token
GAIA-1 World Model - Generation Effects
LINGO-2 Model
3.15 Waymo - Intelligent Driving Large Model
Foundation Model
Building the Driver Algorithm
Validating the Driver Algorithm
Released Multimodal End-to-End Model EMMA
EMMA: Multimodal Input
EMMA: Defining Driving Tasks as Visual Q&A
EMMA: Introducing Chain-of-Thought Reasoning to Enhance Interpretability
Limitations of EMMA Model
Implementation and Operation
3.16 GigaAI - End-to-End
Profile
Evolution Route of World Models
Hierarchical Construction Method for 4D Generative World Models
Application of World Models (1)
Application of World Models (2)
ReconDreamer
World Model: DriveDreamer
World Model: DriveDreamer 2
Overall Framework of DriveDreamer4D
3.17 Nullmax - Intelligent Driving Large Model
Profile
MaxDrive Driving Assistance Solution
New-Generation Intelligent Driving Technology - Nullmax Intelligence
End-to-End Technical Architecture
End-to-End Data Platform
HiP-AD: End-to-End Intelligent Driving Framework Based on Multi-Granularity Planning and Deformable Attention
Mass Production

4 End-to-End Autonomous Driving Layout of OEMs

4.1 Xiaomi
Profile
2026 Strategic Planning/li>
Comprehensive Analysis of New Vehicle Planning in 2026
Product Positioning and Parameter Benchmarking of 2026 New Vehicles (1)
Product Positioning and Parameter Benchmarking of 2026 New Vehicles (2)
Organizational Structure Changes of Intelligent Driving Division
Intelligent Driving Technical Route: Full-Route Pre-Research without Betting on Single Technology
Comparison between VLA and End-to-End Routes
Intelligent Driving Algorithm Evolution Trend: from Modular End-to-End to End-to-End Architecture Introducing World Model + Reinforcement Learning
Launched XLA Cognitive Large Model in 2026
Evolution Roadmap of Intelligent Driving System and Large Models
Enhanced Version of HAD (1)
Enhanced Version of HAD (2)
End-to-End VLA Intelligent Driving Solution Orion
ORION Framework
Physical World Modeling Architecture
Multi-Model End-to-End with Three-Layer Separated Modeling
Long Video Generation Framework - MiLA
4.2 XPeng
Evolution Roadmap of End-to-End Intelligent Driving Large Models
Autonomous Driving Product Planning, 2025~2026
L4 Autonomous Driving Layout in 2026: Robotaxi
Second-Generation VLA: Native Multimodal Physical World Large Model
L4 Capability = Model X Computing Power X Data X Vehicle Hardware
Second-Generation VLA (1)
Second-Generation VLA (2)
World Foundation Model (1)
World Foundation Model (2)
Core Technical Path of World Foundation Model
Three Phased Achievements in R&D of World Foundation Model
Cloud Model Factory (1)
Cloud Model Factory (2)
End-to-End System: Architecture
4.3 Li Auto
Evolution Roadmap of End-to-End Intelligent Driving Large Models (1)
Evolution Roadmap of End-to-End Intelligent Driving Large Models (2)
Launched New-Generation Unified Architecture MindVLA-o1 in 2026 (1)
Launched New-Generation Unified Architecture MindVLA-o1 in 2026 (2)
Next-Generation Unified Architecture MindVLA-o1 (1)
Next-Generation Unified Architecture MindVLA-o1 (2)
Next-Generation Unified Architecture MindVLA-o1 (3)
Evolution from E2E+VLM Dual System to MindVLA
Architecture of MindVLA Model
Core Technology 1 of MindVLA: Great 3D Physical Spatial Perception Capability
Core Technology 2 of MindVLA: Integration with Large Language Model (LLM)
Core Technology 3 of MindVLA: Combination of Diffusion and RLHF
Core Technology 4 of MindVLA: World Model and NVAIE Accelerated Reinforcement Learning
End-to-End Solution (1): Iterative Evolution of System 1
End-to-End Solution (2): System 1 (End-to-End Model) + System 2 (VLM)
End-to-End Solution (3): Intelligent Driving Technical Architecture
End-to-End Solution (4): DriveVLM Large Model - Architecture
End-to-End Solution (5): DriveVLM Large Model - Rendering Effects
End-to-End Solution (6): DriveVLM Large Model - BEV and Text Feature Processing
4.4 Tesla
Interpretation of 2024 AI Conference
Development History of AD Algorithms
Summary of End-to-End Progress, 2023-2024
FSD v13 (1)
FSD v13 (2)
FSD v13 (3): Subsequent Updates
Development History of AD Algorithms: Entering the Perception-heavy Map-light Era
Development History of AD Algorithms: Shadow Mode
Development History of AD Algorithms: Background of Occupancy Network Adoption
Development History of AD Algorithms: Occupancy Network (1)
Development History of AD Algorithms: Occupancy Network (2)
Development History of AD Algorithms: Occupancy Network (3)
Development History of AD Algorithms: Multi-Camera Fusion Algorithm HydraNet
Development History of AD Algorithms: FSD V12
Core Elements of Perception-Decision Full-Stack Integrated Model
End-to-End Algorithms
World Model (1)
World Model (2)
Data Engine
Dojo Supercomputer Center: Overview
Dojo Supercomputer Center: Training Tile Based on D1 Chip Integration
Dojo Supercomputer Center: Computing Power Development Plan
4.5 NIO
Organizational Structure Adjustment of Intelligent Driving Division, 2024-2025
From Model-Based to End-to-End, World Model Becomes Dominant Technical Paradigm
Evolution Route of End-to-End Large Models
Detailed Explanation of Intelligent Driving System
NIO World Model (NWM) (1)
NIO World Model (NWM) (2)
Imagination Reconstruction Capability and Swarm Intelligence of World Model
NSim Simulator (NIO Simulation)
World Model 2.0
Comparation between End-to-End Model and World Model
Comparation between VLA and World Model
4.6 Changan
Dubhe Plan 2.0 - Tianshu Intelligent Driving
Software Architecture of TOPS AD
Brand Layout
ADAS Strategy: "Dubhe Plan" Strategy
End-to-End System: BEV+LLM+GoT (1)
End-to-End System: BEV+LLM+GoT (2)
Production Vehicle Equipped with End-to-End System: NEVO E07
4.7 Chery
Product Matrix and Vehicle Models
Evolution History of Intelligent Driving System
Launched Four Versions of Falcon Pilot in 2025
Progress of End-to-End Intelligent Driving Large Models (1)
Progress of End-to-End Intelligent Driving Large Models (2)
4.8 GAC Group
Intelligent Driving Large Model Strategy
Evolution Roadmap of ADiGO Intelligent Driving System (ADiGO1.0 to ADiGO6.0)
Launched Five Major Intelligent Driving Platforms in 2025
L2.9 Vehicles and Urban NOA Algorithm/Intelligent Driving System Suppliers
Achieves "High-End Orientation + Mass Popularization" of Urban NOA through "Dual-Gradient Intelligent Driving Suppliers + Scenario-Price Precision Matching" Strategy
Established Huawang Adopting the "GAC Smart Manufacturing + Huawei Intelligence" Model to Expand High-End Market and Improve Brand Matrix
First Model Huawang Aistaland F03 Expected to Be Launched in Q2 2026
Momenta 5.0 One-Model End-to-End Algorithm Is Deployed on RMB150,000-Level Vehicles, and Urban NOA Function Is Also Available
Trumpchi Xiangwang S7 to Be Equipped with Momenta R6 Reinforcement Large Model
Architecture of ADiGO End-to-End Embodied Reasoning Model
Core Technologies of ADiGO
4.9 Leapmotor
Released World Model in 2026
D19 Adopts VLA Large Model to Realize Full-Scenario Door-to-Door NOA
Adopts Intelligent Driving System Self-Development Model
Evolution Roadmap of Leapmotor Pilot (1)
Evolution Roadmap of Leapmotor Pilot (2)
End-to-End High-Level Intelligent Driving
Application Scenarios of End-to-End High-Level Intelligent Driving
4.10 IM Motors
Iteration History of Intelligent Driving System
Cooperation with Momenta on Intelligent Driving
IM AD End-to-End 2.0 Intelligent Driving Large Models
Core Technologies of IM AD End-to-End 2.0 Intelligent Driving Large Models
Application Scenario Comparison between IM AD End-to-End 2.0 Intelligent Driving Large Models
4.11 FAW Hongqi
Technical Architecture of Sinan Intelligent Driving
Core Technologies of End-to-End Large Models
Sinan Intelligent Driving Solution
Vehicle Deployment Schedule and Future Planning of Sinan Intelligent Driving Solution
Sinan Intelligent Driving System: Co-Developed with DJI Zhuoyu Technology (1)
Sinan Intelligent Driving System: Co-Developed with DJI Zhuoyu Technology (2)
Deployed Vehicles and Key Configurations of Sinan Intelligent Driving System
Zhuoyu End-to-End 4.0 System Debuted with Sinan Intelligent Driving in 2026
FAW Hongqi 9 Series Models to Adopt Huawei Hi Mode in 2026
4.12 Dongfeng
Intelligent Driving Strategic Plan 2026-2030
Launched Four-Tier Tianyuan Intelligent Driving Product Matrix in 2025: Full Coverage from L2 to L4/L5
Comparison of Intelligent Driving Configurations between Production Vehicles First Equipped with Tianyuan T100/T200/T500
Tianyuan Intelligent Driving Technical Architecture R-AiD
Intelligent Driving Strategy: Self-development + External Procurement in Parallel in Short Term, and Gradual Self-development for Replacement in Long Term
4.13 BYD
Overview of 2026 Intelligent Driving Planning
Layout in Intelligent Driving Field: Pre-Research on World Models
Organizational Structure Adjustment of Intelligent Driving Team (1): Integration of Dual Intelligent Driving Departments to Pool Resources to Accelerate Universal Intelligent Driving
Organizational Structure Adjustment of Intelligent Driving Team (2): Establishment of Advanced Technology R&D Center to Increase Investment in