지능형 주행 엔드투엔드 대규모 모델 조사 보고서(2026년)

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.

Table of Contents

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