자율주행 시뮬레이션 및 세계 모델 조사 보고서(2026년)

Autonomous driving simulation research: "Simulation test + world model"-driven test system has become R&D infrastructure.

The "Autonomous Driving Simulation and World Model Research Report, 2026" mainly focuses on core technologies, industry trends and mainstream solutions in the field of simulation and world models, covering the complete system of simulation testing (X-in-the-loop testing from MIL to VIL, scenario library construction, etc.), as well as the evolution of world model solutions of OEMs/Tier 1 suppliers. It analyzes 14 Chinese and 13 foreign mainstream simulation platforms and world model solution providers, sorts out the synergistic relationship between simulation testing and world models, and demonstrates the core value of world models in data cost reduction, scenario generalization, and decision reasoning by way of research.

The national standard GB/T 47025-2026 Intelligent and Connected Vehicle - Simulation Test Methods and Requirements for Automated Driving Function was released and officially implemented on January 28, 2026. This standard is applicable to Category M and N vehicles with autonomous driving functions or autonomous driving systems, and stipulates the simulation test methods, test requirements and overall criteria for the autonomous driving function. The standard defines a total of 48 special test items in 7 categories. The GB/T 47025-2026 and the released standards, the GB/T 41798-2022 (Intelligent and Connected Vehicles - Track Testing Methods and Requirements for Automated Driving Function) and the GB/T 44719-2024 (Intelligent and Connected Vehicle - Methods and Requirements of Road Test for Automated Driving Functions), constitute a complete verification system of "simulation-field-road" trinity.

In order to speed up the mass production of L3/L4 autonomous vehicles, mature autonomous driving algorithm verification usually follows the golden ratio of "99.9% simulation tests + 0.09% closed field tests + 0.01% public road tests". The recommended national standard GB/T 47025-2026 requires that the error between the sensor model and the actual vehicle should be <=5%; the consistency between the dynamics model and the actual vehicle should be >=95%; the behavior of traffic participants should be high-fidelity, etc. This means that the autonomous driving industry has entered a new development stage of compliance access and safety priority. Simulation testing is no longer an auxiliary means for research and development, but has become a legally required link for product access, certification, and safety evidence.

Meanwhile, as a generative AI model, the world model can understand the dynamic laws of the real world (covering physical characteristics and spatial attributes) by building internal representations. It also generates video content with input information such as text, images, videos, and motion data. It is quickly showing great application potential in fields such as autonomous driving and robotics, and is becoming the core technical pillar that drives intelligent systems to leap into high-level perception and decision capabilities.

1. The simulation platform evolves into a "training environment" with high fidelity, physical consistency, and dynamic interaction.

The positioning of the simulation system has been upgraded from a traditional test execution tool to a core data infrastructure supporting algorithm training. The simulation environment is also evolving from visual resemblance to behavioral authenticity, emphasizing physical sensor simulation (such as photons, electrical signals, multi-echo), accurate material properties (such as reflectivity, roughness), vehicle dynamics and traffic flow that comply with physical laws, in a bid to bridge the "Sim-to-Real" gap.

In terms of high fidelity, simulation platform companies are continuing to upgrade their simulation verification capabilities, making high-confidence simulations more detailed. For example, Keymotek's aiSim6 can provide physical sensor simulations, such as cameras (nonlinear response, CMOS noise) and LiDAR (Gaussian rays, multi-echo, weather attenuation), following the ASAM OpenMATERIAL 3D standard and defining precise material physical properties. Furthermore, based on its self-developed PBR Splatting technology, it can dynamically adjust scenario lighting for 3DGS models, dynamically switching lighting conditions such as daytime, dusk, and nighttime on the same road segment, transforming it into a "dynamically configurable training environment" and achieving "physical dynamic neural rendering."

Notably, aiSim 6 applies the Navier-Stokes equations describing fluid motion to environmental particle physics simulations, introducing physical environmental disturbances into the synthetic data link. This allows for realistic simulations of leaf movement caused by vehicle airflow, water splashes from pavements during rain, and the dynamic interaction between manhole cover steam and traffic participants, addressing the shortcomings in physical realism of edge scenarios.

In terms of physical consistency, take the high-fidelity physical simulation of PilotD Technology as an example. The company has independently developed a self-evolving dual-turbine driven data training platform. It uses a high-fidelity world model to generate multi-modal data such as vision and point clouds for closed-loop training of the robot brain. Meanwhile, its data credibility verification technology, namely the "Physical Judge" system, checks the physical rationality of generated data, and performs data screening as well as closed-loop retraining of the world model simultaneously. Based on the self-evolving data dual-turbine, the EAI cerebrum completes fully automatic iterative evolution with the injection of increasingly physically relevant synthetic data, enhancing the algorithm's adaptability and generalization capability in complex real-world scenarios.

The company's self-developed fully physical optical core modeling technology highly restores the optical physicality of data, and uses this to train a multimodal world model data generation architecture with high fidelity in both dynamics and optics, providing AI companies with high-fidelity synthetic data solutions.

In terms of dynamic interaction, for example, SYNKROTRON's OASIS Traffic solution, a traffic flow synthesis data platform for advanced autonomous driving, is based on real roadside data. It uses AI to generate adversarial traffic flows covering 60 high-interaction scenarios, quantifies hazard levels using TTC/PET, and covers over 30% of long-tail corner cases. It can generate massive dynamic traffic scenario datasets (typical areas, typical traffic scenarios, dynamic participants, natural and confrontational behaviors).

2. The world model transforms from an auxiliary tool to a core foundation.

The world model is committed to internalizing physical laws, such as gravity, collision, and causality, to solve problems with traditional simulation tools such as long-term consistency and interpretability, and to understand the "common sense" of the world. For example, GigaAI's GigaWorld-1 has excellent physics adherence capabilities and can accurately simulate complex physical interactions such as gravity and collision. Li Auto's MindVLA-o1 uses the native 3D ViT and the predictive latent world model to understand object position relationships and movement patterns in the three-dimensional space structure. It makes use of the world model to generate massive, high-fidelity, and diverse training data to handle the extreme scarcity of real physical interaction data and promote "Sim2Real" migration.

Fusion trend: VLA + world model + reinforcement learning

In the field of autonomous driving, the world model has been upgraded from a single data generator to the core cognition and deduction center of the autonomous driving system, deeply integrated with VLA and reinforcement learning. In algorithm training, VLA is responsible for perception and semantic understanding, the world model for future deduction and prediction, and reinforcement learning for autonomous optimization decision in the virtual world. The three work together. For example,

QCraft's "VLA + world model" unified architecture can not only multiplex end-to-end capabilities that have been verified in millions of mass productions, but can also accurately understand environmental text, complex scenarios and voice commands through language capabilities, achieving triple alignment of model decision, teleoperation and HMI; then with the help of the world prediction model, it can accurately deduce the behavior of traffic participants, road structure changes and dynamic scenario evolution, thereby planning the optimal driving trajectory.

As the "cloud matrix" of VLA 2.0, XPeng X-World is a physical AI simulator that can "think" about driving scenarios. It generates massive scenarios through the world model for training and evaluation, and enables the R&D paradigm to shift from "stacking real vehicle testing" to "stacking computing power training." The model is built based on the leading video generation model WAN 2.2, involving a customized DiT backbone network. Its key innovation lies in the introduction of a perspective-time self-attention mechanism, which forces the model to simultaneously model the temporal dimension and the spatial geometric relationship between the seven surround view camera perspectives during generation, thereby ensuring that the generated virtual world is tightly integrated across perspectives and avoiding objects from "crossing the model" or being misaligned. The underlying layer adopts a 3D causal variational autoencoder (VAE) with high compression ratio, which greatly reduces the computational overhead of multi-channel vide o stream processing and supports long-term modeling.

Core Foundation Cases of World Models:

In the field of autonomous driving, the world model adopts a dual-engine architecture of "cloud training + vehicle reasoning". The cloud is responsible for large-scale training and scenario generation, and the vehicle offers real-time decision and rapid response. For example, on April 24, 2026, Huawei released Qiankun ADS 5, which uses the WEWA 2.0 to improve game-theoretic training and learning efficiency by 10 times, and reduce collision risks by 50%; cloud computing power jumps to the current 60 EFLOPS in 2026, achieving a 21-fold increase from the level in 2023, supporting high-level autonomous driving research and development.

In Huawei's WEWA architecture, the cloud-based WE (World Engine) handles virtual scenario training and model parameter updates. Powered by diffusion generative models, it operates in a mode of simultaneous generation, learning and validation. It can controllably generate various rare scenarios including adjacent vehicle cut-in, dart-out, and sudden braking of leading vehicles, realizing the shift from human training AI to AI self-training. The automotive WA (World Action Model) is in charge of real-time path planning and control.

As a world model, Pony.ai's PonyWorld 2.0 has self-diagnosis and directional evolution capabilities. AI can independently diagnose shortcomings and proactively guide data collection, becoming the core of the paradigm shift in R&D training. Specifically, PonyWorld 2.0 combines the intention semantics layer of Pony.ai's automotive model to realize automated traceback and attribution analysis of every driving decision. The system can automatically identify the root cause of the problem and accurately feed the diagnosis results to the model training process.

Based on self-diagnosis results, PonyWorld 2.0 can automatically identify specific scenarios where the accuracy of the world model is insufficient, and proactively generate directional data collection tasks. For example, the system can automatically push instructions: "Please focus on collecting mixed traffic scenario data of non-motorized vehicles and pedestrians under backlight conditions at designated intersections during specific periods." The R&D and testing teams thus collaborate efficiently around the "accuracy requirements" of the world model to achieve directional data collection and model iteration guided by AI.

In the field of EAI, the world model has evolved from a "data engine" to a "cerebrum" or "simulator" of EAI agents, capable of physical deduction, action planning and mission decision.

For example, unlike the traditional WA architecture that relies on inefficient and lengthy video prediction links, the action-centric paradigm of GigaWorld-Policy, the World-Action Model (WAM) developed by GigaAI, breaks the cross-modal coupling bottleneck and delivers a dramatic improvement in inference efficiency via architectural optimization.

It has pioneered the hybrid paradigm model of "Complex Training & Simplified Inference":

During the training phase, GigaWorld-Policy uses a causal mask mechanism to achieve unified modeling of action tokens and future visual tokens, allowing action prediction to fully benefit from the high-density supervision signals provided by future visual dynamics.

During the inference phase, the model completely abandons the video prediction branch, retaining only a lightweight action generation module. It avoids the need to perform inference processes for long sequences of visual tokens, fundamentally circumventing the structural computational redundancy caused by cross-modal architecture coupling in traditional WA models.

Compared to current mainstream WA models (such as Motus and Cosmos Policy), GigaWorld-Policy achieves a 10x improvement in inference speed while maintaining policy quality, truly meeting the real-time requirements of high-frequency closed-loop control for robots. GigaWorld-Policy's average success rate in real-world tasks approaches 85%. Facing strong competitors like Cosmos-Policy, its absolute success rate is raised by more than 30%.

On April 29, 2026, GensPi Technology officially released MotuBrain, a general-purpose world-action model. Positioned as a general-purpose cerebrum for EAI robots, it possesses multi-robot adaptability, multi-task generalization, and long-term task execution capabilities, achieving multi-functionality and multi-type capabilities with a single brain. MotuBrain's core breakthrough lies in its unified modeling of the "world seen" and the "actions to be performed," allowing the robot to not only understand the environment but also predict changes and generate executable action strategies. MotuBrain won the first place on both RoboTwin 2.0 and WorldArena, two authoritative international benchmarks. In WorldArena, MotuBrain ranked first with an overall EWM score of 63.77, and led across multiple key motion dimensions, including Motion Quality, Flow Score, and Motion Smoothness.

3. "Simulation test + world model"-driven test system has become R&D infrastructure.

In the autonomous driving data closed-loop and test system, simulation testing and world models complement each other, offsetting technical shortcomings and complement capability boundaries of each other.

In the fields of autonomous driving and EAI, simulation testing and world models are moving from "separation" to "deep integration." The industry has begun to establish unified standards and promote the construction of an integrated platform of "reconstruction + generation + simulation + training" to enable simulation capabilities from autonomous driving multiplexing to EAI, realizing a broader physical AI ecosystem.

Currently, world models (especially generative world models) have become the core "power plant" of simulation platforms, driving the AI-powered automatic generation of simulation scenarios and generating massive and diverse scenarios (especially long-tail and rare scenarios) and high-fidelity sensor data at low cost and with high quality.

On April 24, 2026, 51Sim's SimOne 4.0 was comprehensively reconstructed and upgraded for the physical AI era, building a "4DGS reconstruction + generative world model" technology base to automatically build interactive, editable, and scalable virtual simulation assets from real vehicle data to achieve large-scale scenario generation. SimOne 4.0 covers the five-core links of data, training, reasoning, verification and delivery, comprehensively helping AI enter the physical world safely and efficiently. Moreover, SimOne4.0 deeply integrates the neural rendering technology solution - NVIDIA Omniverse NuRec at the product level to build a complete data-driven process from real data collection, neural scenario reconstruction to closed-loop simulation execution. In 51Sim's end-to-end data-driven closed-loop solution, the confidence levels of dynamics, LiDAR, and camera simulations are as high as 95%, 95%, and 90% respectively, and the consistency between simulation testing and field testing reaches 92%.

SimOne 4.0 supports multiple GPU architectures simultaneously. It has achieved systematic adaptation and in-depth optimization with Moore Threads' flagship AI training and inference integrated GPU MTT S5000. The platform enables high-concurrency execution of large-scale 4DGS and world model training tasks, delivering high-quality reconstruction and model training for complex dynamic scenarios within a short time, and driving the continuous evolution of world models and VLA. Up to now, SimOne has empowered more than 100 customers in many EAI fields such as autonomous driving, smart equipment, and robots.

In January 2026, AGIBOT released Genie Sim 3.0, an open-source simulation platform driven by its large language model. Based on NVIDIA Isaac Sim, the platform provides a high-fidelity simulation environment and natural language-driven scenario generation capabilities. It can provide a full-process closed-loop solution from digital asset generation, scenario generalization, data collection to automatic evaluation, significantly speeding up the model training and verification process and reducing dependence on physical hardware.

Highlights of Genie Sim 3.0 include a digital twin-level high-fidelity simulation environment, which pioneeringly deeply integrates three-dimensional reconstruction, visual generation technology and physics engines to achieve the unification of visual realism and physical accuracy. Secondly, it has pioneered natural language-driven scenario generation and generalization. In Genie Sim 3.0, developers can input natural language instructions to drive the platform to automatically generate and generalize thousands of training and test scenarios within minutes, and conduct large-scale parallel training. In addition, the simulation platform also provides a full range of open-source simulation datasets (covering more than 200 tasks with a total duration of tens of thousands of hours) and efficient collection solutions; it has built a three-dimensional evaluation system based on 100,000+ simulation scenarios, etc. It is worth noting that AGIBOT's world model, Genie Envisioner, is based on NVIDIA Cosmos to realize an end-to-end closed loop from perception to action. GE uses a unified video generative world model as the core to integrate policy learning, evaluation and simulation capabilities into the same framework. AGIBOT provides GE-Sim with powerful general visual and physical prior capabilities by deeply integrating Cosmos Predict 2 into its self-developed action-conditioned world model architecture.

The integration of simulation testing and world models essentially builds a flywheel closed loop of data generation - algorithm training - model verification - continuous evolution. In the two fields of autonomous driving and EAI, the integration paths are highly consistent, both pointing to the ultimate goal of "physical AI": allowing the system to complete closed-loop learning from cognition to action in the virtual world, and then seamlessly migrate to the physical world.

Table of Contents

Terms

1 Overview of Simulation Testing

• 1.1 Simulation Test Phase and Collaboration
  • 1.1.1 Simulation Scenario Modeling Technology
  • 3DGS High-Fidelity Simulation Scenario Reconstruction Technology
  • Simulation Scenario Modeling: 3DGS Case 1
  • Simulation Scenario Modeling: 3DGS Case 2
  • Simulation Scenario Modeling: 3DGS Case 3
  • Simulation Scenario Modeling: 3DGS Case 4 (1)
  • Simulation Scenario Modeling: 3DGS Case 4 (2)
  • Simulation Scenario Modeling: 3DGS Case 4 (3)
  • Simulation Scenario Modeling: 3DGS Case 4 (4)
  • Four Major Development Directions of 3DGS Technology
  • Common Algorithms for 4D Reconstruction and Dynamic Modeling of Autonomous Driving Scenarios
  • Simulation Scenario Modeling: 4DGS Case 1
  • 1.1.2 Physical Sensor Simulation
  • Optical Physical Modeling of Lens Models
  • CMOS Sensor Optoelectronic Simulation
  • LiDAR Modeling: Gaussian Rays and Physical Attenuation
  • Case Analysis: LidarPainter (1)
  • Case Analysis: LidarPainter (2)
  • 1.1.3 Comparison of Vehicle Dynamics Simulation Solutions
  • Case Analysis: New Version of PanoCar 2026 (1)
  • Case Analysis: New Version of PanoCar 2026 (2)
  • Case Analysis: New Version of PanoCar 2026 (3)
  • 1.1.4 X-in-the-Loop Testing and Coordination
  • SIL Case 1:
  • SIL Case 2:
• 1.2 Simulation Scenario Libraries
  • 1.2.1 Scenario Abstraction Hierarchy
  • 1.2.2 Classification of Simulation Scenario Libraries
  • 1.2.3 Data Sources of Scenario Libraries (1)
  • 1.2.3 Data Sources of Scenario Libraries (2)
  • 1.2.3 Data Sources of Scenario Libraries (3)
• 1.3 OpenX Series Standards
• Application Cases of OpenX Series Standards in Simulation Platforms
• Traffic Flow Synthesis Data Platform Case:
• Traffic Flow Synthesis Dataset Case:
• Difficulties in Unstructured Road Simulation
• Case of Overcoming Difficulties in Unstructured Road Simulation:
• 1.4 Relationship between Simulation Testing and World Models
• 1.5 A "Simulation Test + World Model"-Driven Test System
• 1.6 Trend 1 in Simulation Testing/World Models:
• 1.7 Trend 2 in Simulation Testing/World Models:
• 1.8 Trend 3 in Simulation Testing/World Models:
• 1.9 Trend 4 in Simulation Testing/World Models:
• 1.10 Interpretation of National Standard GB/T 47025-2026 Intelligent and Connected Vehicle - Simulation Test Methods and Requirements for Automated Driving Function
• 1.10 List of 48 Test Items in the National Simulation Standard
• 1.11 Simulation Testing and World Model Industry Chain

• 2.1 Overview of World Models
• 2.2 Components of World Models
• 2.3 Technical Indicators of World Models
• 2.4 Core Elements of World Models
• 2.5 Key Technologies and Capabilities of World Models
• 2.6 Evolution of World Model Technology Roadmap
• 2.6 Comparison of World Model Technology Roadmaps (1)
• 2.6 Comparison of World Model Technology Roadmaps (2)
• 2.7 Main Architecture Types of World Models
• 2.8 Development History of World Models
• 2.9 Trends in World Models
• 2.10 Mainstream Application Scenarios of World Models
• 2.11 Mainstream World Model Case 1: Genie3
• 2.11 Mainstream World Model Case 2: Cosmos
• 2.11 Mainstream World Model Case 3: Meta V-JEPA2
• 2.11 Mainstream World Model Case 4: Wayve GAIA2
• 2.11 Mainstream World Model Case 5: SenseWorld World Model
• 2.12 Core Differences and Fusion between World Models and VLA
• From VLA to VLA World Model
• VLA and World Model Fuse to Form WorldVLA Model
• Comparison of Action Model, World Model, and Action World Model
• Technology Route Fusion: VLA + World Model + Reinforcement Learning

3 World Model-Based Simulation: Autonomous Driving

• 3.1 Four Major Values of World Models in Autonomous Driving
• 3.2 Cost Reduction and Efficiency Improvement of World Models in Autonomous Driving
• 3.3 Generation Scenarios of World Models in Autonomous Driving
• 3.4 Key Capabilities of World Models in Autonomous Driving
• 3.5 Deployment Challenges of World Models in Autonomous Driving
• 3.6 Future Directions of World Models in Autonomous Driving
• 3.7 Comparison of World Model Technologies from Major Companies (1)
• 3.7 Comparison of World Model Technologies from Major Companies (2)
• 3.7 Comparison of World Model Technologies from Major Companies (3)
• 3.8 Three Mainstream Technology Schools
  • 3.8.1 Case 1: Pony.ai
  • PonyWorld 2.0
  • 3.8.2 Case 2: SenseAuto
  • SenseWorld
  • 3.8.3 Case 3: QCraft
  • "VLA+ World Model" Unified Architecture
  • 3.8.4 Case 4: Momenta
  • R7 Reinforcement Learning World Model (1)
  • R7 Reinforcement Learning World Model (2)
  • R7 Reinforcement Learning World Model (3)
  • 3.8.5 Case 5: Zhuoyu
  • Multimodal End-to-End VLA World Model (1)
  • Multimodal End-to-End VLA World Model (2)
  • Multimodal End-to-End VLA World Model (5)
  • Ecosystem Partners
  • Vehicle Models Delivered by Zhuoyu
  • 3.8.6 Case 6: Huawei WEWA Architecture (1)
  • 3.8.6 Case 6: Huawei WEWA Architecture (2)
  • 3.8.6 Case 6: Huawei WEWA Architecture (3)
  • 3.8.7 Case 7: NIO
  • NWM
  • 3.8.8 Case 8: XPeng
  • VLA 2.0
  • X-World (1)
  • X-World (2)
  • X-World (3)
  • 3.8.9 Case 9: Li Auto
  • MindVLA-o1 (1)
  • MindVLA-o1 (2)
  • MindVLA-o1 (3)
  • MindVLA-o1 (4)
  • Unified Action Generation: Parallel Decoding with VLA-MoE
  • World Simulator for Reinforcement Learning
  • Physical AI Framework
  • 3.8.10 Case 10: Leapmotor World Model
  • 3.8.11 Case 11: Geely
  • Full-Domain AI 2.0 Based on WAM
  • Key Features of WAM
  • Vehicle Models with WAM
  • G-ASD

4 World Models and Simulation Environment: EAI

• 4.1 Overview and Technical Architecture of EAI
• 4.2 Status Quo of EAI
• 4.3 Core Problem of EAI: Scarcity of Physical Interaction Data
• 4.4 Key Capabilities of World Models in EAI
• 4.5 Three Major Data Challenges in the EAI Field
• 4.6 EAI Data Breakthrough Case 1: Synapath AI
• 4.7 EAI Data Simulation Case 2: Lightwheel
• 4.8 Thought on the Next-Generation Embodied Foundation Model Technology Roadmap
• 4.8 Future Directions of World Models in EAI
• 4.9 World Model Evaluation Benchmark - WorldArena
• 4.10 Overview of Simulation Environments in EAI
• Basic Components of Simulation Environments
• Mainstream Simulation Environments (1)
• Mainstream Simulation Environments (2) - Isaac Sim
• Mainstream Simulation Environments (3) - MUJOCO & Pybullet
• 4.11 Embodied Benchmark Testing
• Common Embodied Benchmark Task Types
• Benchmark Test: CALVIN
• Benchmark Test: LIBERO
• Emerging Benchmark Project: ROBOVERSE
• Benchmark Evaluation Development Trends
• 4.12 Humanoid Robots and EAI Standard System (2026)
• 4.12 Interpretation of Humanoid Robots and EAI Standard System (2026)
• 4.13 Application Case 1 of World Models in EAI: ACE Robotics
• Kairos 3.0 (1)
• Kairos 3.0 (2)
• Kairos 3.0 (3)
• 4.14 Application Case 2 of World Models in EAI: HappyOyster
• 4.15 Application Case 3 of World Models in EAI: LingBot-World
• 4.16 Application Case 4 of World Models in EAI: Genie 3
• 4.17 Application Case 5 of World Models in EAI: HY-World 2.0
• 4.18 Application Case 6 of World Models in EAI: Magic-Mix

5 Mainstream Chinese Simulation Platforms and World Model Solution Providers

• 5.1 GigaAI
• Summary of Core Products
• General Purpose Robot: Maker H01
• Product/Solution Application Cases
• The World's First Comprehensive Data System Driven by a "World Model"
• Embodied Foundation Model: GigaBrain-0
• Embodied Foundation Model: GigaBrain-0.5M* (1)
• Embodied Foundation Model: GigaBrain-0.5M* (2)
• Embodied Foundation Model: GigaBrain-0.5M* (3)
• End-to-End Embodied Foundation Model: GigaBrain-0.1
• Embodied World Model: GigaWorld-0 (1)
• Embodied World Model: GigaWorld-0 (2)
• Embodied World Model: GigaWorld-0 (3)
• Embodied World Model: GigaWorld-0 (4)
• Embodied World Model: GigaWorld-1
• World-Action Model (WAM): GigaWorld-Policy (1)
• World-Action Model (WAM): GigaWorld-Policy (2)
• World-Action Model (WAM): GigaWorld-Policy (3)
• 5.2 GensPi Technology
• Funding and Latest Collaboration
• World-Action Model (WAM): MotuBrain (1)
• World-Action Model (WAM): MotuBrain (4)
• Unified Universal World Model: Motus (1)
• Unified Universal World Model: Motus (4)
• 5.3 AGIBOT
• Open Source Simulation Platform: Genie Sim 3.0
• Unified World Model Platform: Genie Envisioner
• 5.4 51Sim
• SimOne
• SimOne 3.8
• SimOne 4.0 (1)
• SimOne 4.0 (2)
• SimOne 4.0 (3)
• Customer Base of SimOne
• Driver-in-the-Loop (DIL) Solution (1)
• Driver-in-the-Loop (DIL) Solution (2)
• Driver-in-the-Loop (DIL) Solution (3)
• OpenX-based End-to-End Closed-Loop Simulation Platform
• Self-developed 3DGS Hybrid Simulation Engine
• "End-to-End Data-Driven Closed-Loop" Solution
• 5.5 IAE
• Profile
• DeepOcean.AI V3.0
• Simulation Scenario Data Products and Services
• Air-Ground Integrated Simulation Platform
• X-In-Loop(R) Full-Stack Simulation and Testing Toolchain
• X-In-Loop Simulation Solution: Autonomous Driving Digital Twin Simulation Platform
• X-In-Loop Simulation Solution: ECU-Level HIL Testing
• X-In-Loop Simulation Solution: Vehicle-Level HIL (VaHIL(R))
• Model and Software Simulation Testing
• Hardware-in-the-Loop Simulation Testing (1)
• Hardware-in-the-Loop Simulation Testing (2)
• Hardware-in-the-Loop Simulation Testing (3)
• Hardware-in-the-Loop Simulation Testing (4)
• Vehicle-in-the-Loop Simulation Testing (1)
• Vehicle-in-the-Loop Simulation Testing (2)
• Driving Simulator (1)
• Driving Simulator (2)
• Cloud-Accelerated Massive Simulation
• Scenario Workshop
• Software Testing
• Scenario-based Automated Software Testing (1)
• Scenario-based Automated Software Testing (2): Automated Code Verification Tools
• Scenario-based Automated Software Testing (3): Automated Model Verification Tools
• Scenario-based Automated Software Testing (4): Automated System Verification Tools
• Scenario-based Automated Software Testing (5): Software Verification Services
• Scenario-based Automated Software Testing (6)
• Intelligent Connected Vehicle Meteorological Innovation Research Center
• 5.6 Zhejiang PanoSim
• Profile
• Key Technologies
• Application solutions
• Ecological Partners
• Integrated Simulation Test Platform for Autonomous Driving: PanoSim
• Integrated Simulation Test Platform for Autonomous Driving: PanoSim
• High-Precision Vehicle Dynamics Simulation Software: PanoCar
• Latest Version of PanoCar (1)
• Latest Version of PanoCar (2)
• Latest Version of PanoCar (6)
• Autonomous Vehicle Driving Simulator: PanoDrive
• Digital Twin Simulation Platform: PanoTwin
• Digital Twin Autonomous Driving Simulation Platform: PanoTwin-E
• Simulation Test Bench: PanoPilot
• Real-Time Hardware-in-the-Loop Simulation Test Platform: PanoHIL
• 5.7 SaimoAI
• Core Products
• Revenue from Core Products
• Safety Pro
• Cooperation Case 1: Saishi Technology Empowes "Three Consistencies"
• Cooperation Case 2: High-Confidence HIL Test Solution (1)
• Cooperation Case 2: High-Confidence HIL Test Solution (2)
• Cooperation Case 2: High-Confidence HIL Test Solution (3)
• Dual-site Collaboration for Full-chain Verification
• Intelligent Connected Vehicle Closed Test Fields
• 5.8 SYNKROTRON
• Core Technical Advantages
• Technology Empowerment Case 1
• Technology Empowerment Case 2
• Dual Engine: Continuous Learning Framework + World Model for Synthetic Data
• Integrated Autonomous Driving Simulation Platform: OASIS SIM (1)
• Integrated Autonomous Driving Simulation Platform: OASIS SIM (2)
• Integrated Autonomous Driving Simulation Platform: OASIS SIM (3)
• Integrated Autonomous Driving Simulation Platform: OASIS SIM (4)
• Data Acquisition System: OASIS ROVER
• Data Platform: OASIS DATA
• Oasis Connect
• OASIS Traffic
• Oasis BotStream Platform
• EAI Operating System: Dora (1)
• EAI Operating System: Dora (5)
• 5.9 PilotD Technology
• Profile
• Self-evolving Dual-Turbine Driven Data Training Platform
• Highly Physical Synthetic Data Empowerment Cases
• Autonomous Driving Simulation Test Software: GaiA7
• PD Robot Virtual Development and Verification Solution
• PDUranus Intelligent Unmanned Low-Altitude Vehicle Virtual Optimization Platform
• On Cloud Precision Simulation Cloud Computing Platform (1)
• On Cloud Precision Simulation Cloud Computing Platform (2)
• On Cloud Precision Simulation Cloud Computing Platform (3)
• PDRHea(R) Recursive Layered Scenario Generator
• PlenRay(R) Physical Ray Technology
• DiL Solutions
• Medusa High-Confidence Simulation Platform
• Hercules Cost-effective Hardware-in-the-Loop Simulation Platform
• Model Library Refined Classification Platform
• 5.10 Keymotek
• End-to-End Autonomous Driving Simulation Software: aiSim
• aiSim: Overcoming Difficulties in Unstructured Road Simulation
• SiL Verification Solution for the Full R&D Cycle of Autonomous Driving
• aiSim 6 (1)
• aiSim 6 (6)
• aiSim 6 (7)
• Dual-modal Simulation and Test Solution (1)
• Dual-modal Simulation and Test Solution (5)
• High-Fidelity End-to-End HIL Simulation Solution (1)
• High-Fidelity End-to-End HIL Simulation Solution (2)
• High-Fidelity End-to-End HIL Simulation Solution (3)
• High-Fidelity End-to-End HIL Simulation Solution (4): Application Cases
• Keymotek & Jingwei Hirain Autonomous Driving HIL Simulation Test System
• HongKe Builds an EAI Full-Stack Test Solution Covering "Perception-Thinking-Action"
• ROSI Platform (Robotec.ai)
• CoppeliaSim Robot Simulation Platform (Coppelia Robotics) (1)
• CoppeliaSim Robot Simulation Platform (Coppelia Robotics) (2)
• CoppeliaSim Robot Simulation Platform (Coppelia Robotics) (3)
• CoppeliaSim Robot Simulation Platform (Coppelia Robotics) (4)
• CoppeliaSim Robot Simulation Platform (Coppelia Robotics) (5)
• 5.11 Tsing Standard
• Customers of Test Services
• Autonomous Driving Testing: ADAS HIL
• CAN/LIN Bus Test Solutions
• Automotive Ethernet Test Solutions
• CAN Sprite and Ethernet Sprite (1)
• CAN Sprite and Ethernet Sprite (2)
• Customers of CAN Sprite and Ethernet Sprite
• OTA Vehicle-Cloud Integrated Test Solutions
• Steering HIL
• Suspension HIL
• Brake HIL
• BMS HIL
• Full-Dimensional Energy Storage BMS HIL Testing System
• PACK HIL
• Battery Production Line Turnkey Project Cases:
• VCU HIL
• MCU HIL
• BCM HIL
• ZCU HIL
• Comprehensive Simulation Test Solutions for "Electric Drive, Battery, Electric Control" Systems
• Safety Detection Capabilities for "Electric Drive, Battery, Electric Control" Systems
• 5.12 Jingwei Hirain
• INTEWORK VBA
• INTEWORK VBA V3.1.0R
• Newly Launched Software-in-the-Loop (SIL) Testing Platform: INTEWORK-TVM (1)
• Newly Launched Software-in-the-Loop (SIL) Testing Platform: INTEWORK-TVM (2)
• Newly Launched Software-in-the-Loop (SIL) Testing Platform: INTEWORK-TVM (3)
• Newly Launched Software-in-the-Loop (SIL) Testing Platform: INTEWORK-TVM (4)
• Autonomous Driving "Vehicle-Cloud" Evaluation Data Platform
• TestBase VCI Product Series
• TestBase VCI Series Product Upgrade
• DeskHIL Simulation Test Platform (1): Product Features
• DeskHIL Simulation Test Platform (2): Platform Composition
• DeskHIL Simulation Test Platform (3): Software Compatibility
• Next-generation HIL Test Platform (1)
• Next-generation HIL Test Platform (2): Platform Composition
• Next-generation HIL Test Platform (3): Real-time Simulator
• Autonomous Driving HIL Simulation Test Solutions
• Autonomous Driving VIL SYNO Solutions
• Intelligent Cockpit HIL Simulation Test Solution
• Chassis Electronic Control System HIL Simulation Test Solutions
• Powertrain System HIL Simulation Test Solutions
• TPA Smart Lab
• 5.13 PoleLink Information
• Profile
• Intelligent Chassis HiL Test Solution (1)
• Intelligent Chassis HiL Test Solution (2)
• Intelligent Chassis HiL Test Solution (6)
• Intelligent Chassis HiL Test Solution (7)
• Intelligent Chassis HiL Test Solution (8): Steering Motor Simulation Testing
• Intelligent Chassis HiL Test Solution (9): Assembly-Level Steering Testing
• Intelligent Chassis HiL Test Solution (10): Assembly-Level EHB Testing
• Intelligent Chassis HIL Test Solution (11): Suspension Testing
• 5.14 TOSUN
• Simulation Test Solution (1)
• Simulation Test Solution (2)
• Simulation Test Solution (3)
• Chassis HIL Simulation Test Solution
• EMB Automated Test Solution

6 Mainstream Foreign Simulation Platforms and World Model Solution Providers

• 6.1 NVIDIA
• NVIDIA Cosmos 3 (1)
• NVIDIA Cosmos 3 (2)
• NVIDIA Cosmos 3 (3)
• NVIDIA Cosmos 3 (4)
• NVIDIA Cosmos Transfer
• NVIDIA Omniverse (1)
• NVIDIA Omniverse (2)
• NVIDIA Omniverse (3)
• NVIDIA AlpaSim
• NVIDIA Alpamayo 1.5 (1)
• NVIDIA Alpamayo 1.5 (2)
• NVIDIA Alpamayo 1.5 (3)
• NVIDIA Alpamayo 1.5 (4)
• NVIDIA Alpamayo 1.5 (5)
• NVIDIA Isaac GR00T N1.7
• Isaac Lab: GPU-Accelerated Simulation Framework for Multimodal Robot Learning
• NVIDIA Physical AI Data Factory Blueprint
• NVIDIA DRIVE Hyperion 10 Sensor Kit L2++ to L4
• NVIDIA DRIVE L2++
• NVIDIA DRIVE L4 Roadmap
• NVIDIA Halos OS for L4
• AV Business History
• Summary of Enterprises Empowered by NVIDIA
• Automotive Business Ecosystem
• 6.2 UE (Unreal Engine)
• Unreal Engine 5.7
• Summary of Vehicle Model HMI Innovation Cases Empowered by Unreal Engine
• Summary of Digital Cockpit Cases Empowered by Unreal Engine 5 (1)
• Summary of Digital Cockpit Cases Empowered by Unreal Engine 5 (2)
• 6.3 Unity
• Technical Advantages
• Unity China's AI OS 3D Spatial Intelligent Cockpit (1)
• Unity China's AI OS 3D Spatial Intelligent Cockpit (2)
• Unity China's Intelligent Cockpit Solution Customer Base
• Customer Case 1:
• Customer Case 2:
• Customer Case 3:
• 6.4 Wayve
• GAIA-2 (1)
• GAIA-2 (2)
• GAIA-2 (3)
• LA-Pose
• Zero-Shot Practice
• Commercialization Progress and Implementation Plan
• 6.5 Waymo
• Waymo Released World Model Based on Google Genie
• 6.6 Foretellix
• Core Technologies of Data-Driven Autonomous Driving Development Toolchain
• Foretify Data-Driven Development Toolchain
• Foretify Evaluate - Data-Driven Evaluation Framework
• Full-Link Evaluation Process - Foretify Evaluate (1)
• Full-Link Evaluation Process - Foretify Evaluate (2)
• Full-Link Evaluation Process - Foretify Evaluate (3)
• Scalable Neural Reconstruction for Autonomous Driving Development (1)
• Scalable Neural Reconstruction for Autonomous Driving Development (4)
• Cooperation Case 1
• Cooperation Case 2
• 6.7 Hexagon
• Autonomous Driving and Intelligent Driving Simulation Test Platform: VTD (1)
• Autonomous Driving and Intelligent Driving Simulation Test Platform: VTD (5)
• Application of VTD
• Automotive Industry Solutions (1)
• Automotive Industry Solutions (2)
• Automotive Industry Solutions (3)
• Automotive Industry Solutions (4)
• Electric Drive System Engineering Simulation Solutions (1)
• Electric Drive System Engineering Simulation Solutions (2)
• Electric Drive System Engineering Simulation Solutions (3)
• Electric Drive System Engineering Simulation Solutions (4)
• Battery Simulation Solutions
• Body Simulation Solutions
• Component Inspection Solutions
• 6.8 IPG Automotive
• Profile
• Shift-left Test Solution
• Partners
• Cooperation Case 1:
• Cooperation Case 2:
• Product Series
• Product Series: Test System
• Product Series: Software
• Product Series: Hardware
• New Xpack4 Hybrid Solution
• Enhanced Functional Safety and Reliability Capabilities of CarMaker Series
• CarMaker 15.0 (1)
• CarMaker 15.0 (2)
• CarMaker 15.0 (3)
• Integrated HIL Test Solution
• Compact ESC HIL Solution
• SBW-in-the-Loop Test System
• 6.9 dSPACE
• High-Precision Simulation Platform for 3D Scenarios and Physical Sensors: AURELION
• Cloud Simulation Solution: SIMPHERA
• SIL Solutions (1)
• SIL Solutions (2)
• SIL Case 1:
• SIL Case 2:
• SIL Case 3:
• Power HIL
• HIL Cluster Management Solutions
• HIL Case 1:
• Intelligent Chassis Test Solutions (SIL/HIL/Bench)
• VIL: APA Ultrasonic OTA Stimulation and Closed-Loop Test Solutions (1)
• VIL: APA Ultrasonic OTA Stimulation and Closed-Loop Test Solutions (2)
• VIL: APA Ultrasonic OTA Stimulation and Closed-Loop Test Solutions (3)
• New Radar Solution: DARTS ARROW
• RCP Family: High-Performance Integrated Rapid Control Prototype - MicroAutoBox
• SCALEXIO AutoBox: Durable and Reliable Modular Automotive Real-Time System
• BMS 12U HIL, SCALEXIO Rack, SCALEXIO Customized
• New SCALEXIO Essential System
• 6.10 Mechanical Simulation (Affiliated to Applied Intuition)
• Profile and Customers
• SuspensionSim Simulation Software
• TruckSim Simulation Software
• BikeSim Simulation Software
• CarSim Simulation Software (1)
• CarSim Simulation Software (2)
• CarSim Simulation Software (3)
• CarSim Simulation Software (4)
• Basic Usage of CarSim (1)
• Basic Usage of CarSim (5)
• Advanced Features of CarSim (1)
• Advanced Features of CarSim (2)
• Advanced Features of CarSim (6)
• 6.11 Spirent (Affiliated to Keysight)
• Automotive Ethernet Conformance Testing (1)
• Automotive Ethernet Conformance Testing (4)
• 6.12 R&S
• Test Solutions (1)
• Test Solutions (2)
• Test Solutions (7)
• 6.13 VI-grade
• Profile
• Summary of Customer Cases (1)
• Summary of Customer Cases (2)
• Summary of Customer Cases (6)
• Summary of Customer Cases (7)
• Updated Versions of Software Products in Jan 2026
• VI-CarRealTime (1)
• VI-CarRealTime (2): Advantages
• VI-CarRealTime (3): Customers
• Core Advantages of VI-NVHSim
• Configuration of VI-NVHSim Simulator
• Applications and Customer Cases of VI-NVHSim
• Full-Spectrum Dynamic Driving Simulators (1)
• Full-Spectrum Dynamic Driving Simulators (2)
• Full-Spectrum Dynamic Driving Simulators (3)
• HEXAREV Driving Simulator
• Structure of Latest Driving Simulator (HexaRev)
• New HexaRev Motion Platform + HyperDock Cockpit Technology
• Cases of DiM500:
• Dynamic Driving Simulators: DiM400
• Cases of DiM400:
• Dynamic Driving Simulators: DiM150 & DiM250
• Dynamic Driving Simulators: DiM50
• Product Series - Static NVH Driving Simulators
• Product Series - Static Driving Simulators
• Product Series - Compact Full-Spectrum Simulators
• Product Series - Compact HMI Driving Simulators
• Product Series - Compact NVH Driving Simulators
• Product Series - Compact Driving Simulators
• Product Series - Desktop NVH Driving Simulators
• Product Series - Desktop Driving Simulators