The "Intelligent Driving Simulation and World Model Research Report, 2025" report has been added to ResearchAndMarkets.com's offering.

The world model brings innovation to intelligent driving simulation

In the advancement towards L3 and higher-level autonomous driving, the development of end-to-end technology has raised higher requirements for the scale of high-quality data, coverage of diverse scenarios, assurance of physical realism, synchronized generation of multimodal data, rationality of behavioral logic, and improvement of iteration efficiency.

Among the three core elements of high-quality intelligent driving (data, models, and computing power), the quality and quantity of scenario data are becoming key differentiators in the intelligent driving experience. Meanwhile, training high-level advanced driver-assistance system (ADAS) algorithm models requires tens of millions of video clips and the generation of long-sequence multimodal driving scenarios. However, the long-tail scenarios captured in real-world road data are relatively limited and cannot meet the demand for high-quality data to feed end-to-end algorithm training.

Automated simulation testing is becoming a powerful tool for OEMs and suppliers to shorten development cycles, reduce costs, improve efficiency, address insufficient coverage of long-tail scenarios, and overcome challenges in reproducing high-risk operating conditions. At the same time, world models, which can understand the physical characteristics and spatial attributes of the real-world environment, are being adopted by an increasing number of OEMs and leading Tier1 suppliers.

Currently, for intelligent driving training, scenario data mainly comes from the following sources:

One is simulation technology based on the replay of real road test data, with the advantage of high scenario authenticity, primarily used to reproduce road test problem scenarios and verify the effectiveness of algorithm fixes

The second is artificially defined parametric scenarios (such as OpenScenario format), characterized by standardized testing, exploration of boundary conditions, and strong scenario controllability

The third involves converting real road test data (logsim) into generalizable virtual simulation scenarios (Worldsim), with the core function being data-driven scenario generation and generalization, building a high-confidence simulation scenario library, supporting scenario derivation and automated testing, thereby improving scenario coverage efficiency.

The fourth is the World Model, which uses AI to construct an internal representation of the physical world, enabling intelligent models for environmental state prediction and counterfactual reasoning. Its data sources include multimodal data (images, text, physical rules) and reinforcement learning-generated data. Its advantages include causal reasoning capabilities and support for unknown scenario prediction. However, world models require significant computational resources, their interpretability needs improvement, and they also carry the risk of data bias.

World models also demonstrate advantages in multiple aspects, such as environmental perception and understanding, prediction of future scenario evolution, decision and planning optimization, data generation and training enhancement, simulation and test validation, and improvement of system generalization capabilities.

Full-chain safety validation is driving simulation toward cross-domain integration

Current autonomous driving safety validation has shifted from single-function testing to full-chain closed-loop verification. Simulation technology is breaking through traditional boundaries and moving toward deep cross-domain collaboration, with core drivers including accelerated technological convergence and toolchain integration.

Specifically:

Due to the development of cockpit-driving integration and cross-domain integration applications, simulation is also moving toward cross-domain integration. The industry has introduced simulation testing solutions for various automotive domains, integrating software and hardware tools/platforms to actively promote joint cross-domain testing. Overall, OEMs and suppliers are currently advancing cross-domain simulation, mainly focusing on: Intelligent cockpit + intelligent driving integration, Intelligent chassis + intelligent driving cross-domain integration, Three-electric systems (battery, motor, electronic control) + thermal management integration, IoV + intelligent driving integration, global digital twins.

Industry progress in enhancing simulation credibility

One of the biggest pain points in simulation testing is credibility. The industry needs to consider how to ensure high fidelity in scenario simulation, high accuracy in sensor models, high confidence in dynamics models, as well as challenges in real-time performance, data bandwidth, and stability of data interfaces.

In terms of improving simulation credibility, the following approaches are being adopted.

1. Application of AI Technology

AI technology is gradually being applied to simulation testing in engineering practice, significantly accelerating the automation efficiency of testing and validation, thereby improving automotive development efficiency.

Open-Source Datasets

Additionally, organizations like the China Association of Automobile Manufacturers (CAAM) are actively promoting open-source data initiatives. Nearly 20 datasets have been released, including Coral Data, vehicle-road-cloud integrated simulation scenario open-source data, OEM-open-sourced end-to-end autonomous driving public datasets, and publicly available training datasets related to intelligent driving world models. The goal of open-sourcing is to facilitate efficient reuse of these high-quality scenario datasets and avoid redundant development within the industry.

In April 2025, the ASAM OpenMATERIAL 3D 1.0.0 standard was officially released. This standard specifies a standardized format for physical material properties and 3D object descriptions, precisely defining parameters such as refractive index, surface roughness, and permeability. By providing accurate and standardized 3D assets and material properties, the standard enhances the realism of perception sensor simulations, making the outputs of LiDAR, radar, and cameras more lifelike.

Simulation Tool Upgrades

Simulation testing companies have also updated and upgraded the functions of simulation software tools/platforms, such as PreScan software version 2503, HEXAGON VTD/MSC/ADAMS/KISSoft simulation software, CarMaker14.0, AURELION 24.3, MATLAB/Simulink R2025a, Ansys 2025R1, Oasis SIM 3.0, aiSim intelligent driving simulation software UE5.5 upgrade, Qianxing system V3.0 with 20+ new features, PanoCarV1.7 PanoSim V33 version, etc. (see the report for details).

Key Topics Covered:

Overview of Intelligent Driving Simulation

• Analysis of Intelligent Driving Simulation Technology Advancements
• Traffic Scenario Simulation Analysis
• Typical Collaborations in Traffic Scenario Simulation
• Sensor Simulation
• Comparison of Different Virtual Camera Modeling Techniques
• High-Fidelity Radar Simulation: Performance Comparison of Radar Modeling Technologies
• Comparison of Sensor Simulation Solutions
• Vehicle Dynamics Simulation Solution Comparison
• Summary and Comparison of Model-in-the-Loop (MiL) Solutions
• Summary and Comparison of Software-in-the-Loop (SiL) Solutions
• Summary and Comparison of Hardware-in-the-Loop (HiL) Solutions
• Summary and Comparison of Driver-in-the-Loop (DiL) Solutions
• Summary and Comparison of Vehicle-in-the-Loop (ViL) Solutions
• Summary of Intelligent Driving Simulation Tools/Platforms
• Summary of Intelligent Cockpit Simulation Testing Tools/Platforms
• Summary of Intelligent Chassis Simulation Testing Tools/Platforms
• Summary of Three-Electric (Battery, Motor, Electronic Control) Simulation Testing Tools/Platforms
• Summary of Automotive Ethernet Testing Tools/Platforms

Simulation Test Scenario Libraries

• Summary of Intelligent Driving Simulation Standards and Regulations
• Evaluation Specifications and Reference Standards for Intelligent Cockpit Simulation Testing
• Comparative Analysis of ASAM OpenX Series Standard Updates
• Analysis of ASAM OpenMATERIAL 3D Standard
• Application Cases of OpenX Series Standards in Simulation Platforms
• Research on Scenario Model Layering
• Latest Dynamics in Scenario Model Layering
• Scenario Abstraction Levels
• Scenario Abstraction Classification
• Dynamic Scenario Analysis
• Classification of Simulation Scenario Databases
• 3DGS High-Fidelity Simulation Scene Reconstruction Technology and Case Studies
• Analysis of 4DGS Technology Applications in Intelligent Driving
• Comparative Analysis of 4D Reconstruction and Dynamic Modeling Algorithms for Intelligent Driving Scenarios
• Analysis of Training Data Sources for Intelligent Driving
• Urgent Requirements of End-to-End Algorithm Training for Synthetic Data

Application of World Models in Intelligent Driving

• Open Datasets for End-to-End Autonomous Driving
• Public Datasets Related to Intelligent Driving World Models
• Other Open-Source Simulation Test Scenario Datasets
• Overview of World Models
• Summary of World Models
• Analysis of World Models in the Field of Intelligent Driving
• Cases of Typical World Model Applications

Intelligent Driving Simulation and World Model Applications by OEMs/Tier1 Suppliers

• NIO
• Autonomous Driving Technology System
• NWM World Model
• XPeng Motors
• Simulation Toolchain Application
• World Foundation Model
• Cloud-based Model Factory
• Xiaomi
• Intelligent Driving Simulation Toolchain and Technology System
• ORION Framework
• MiLA Framework
• Li Auto
• VLA and Simulation
• Simulation Toolchain Application
• Closed-loop Simulation System
• Geely
• Simulation Toolchain
• Collaboration of Simulation Toolchain and Technology System
• Zhuoyu Technology
• End-to-End World Model
• Personalized Innovation of GenDrive Generative Autonomous Driving
• Implementation of End-to-End World Model and GenDrive System
• Horizon
• (Simulation) Toolchain/Technology System
• Core Test Scenario
• Scenario Construction Strategy
• UMGen: Unified Framework for Multimodal Driving Scenario Generation
• UniMM: Multi-agent Simulation
• TTOG Framework
• SenseAuto
• Simulation Toolchain
• Technology System
• Cost Reduction through Simulation Toolchain and Technological Innovation
• R-UniAD
• Progress in AI Domain Development
• Mass-Production E2E Solution Framework Diagram
• GigaAI
• Profile
• ReconDreamer
• DriveDreamer4D

Research on AI-Integrated Intelligent Driving Simulation Technologies

• Digital Twin & GNSS Application - Case 1
• Integrated Application Analysis of Digital Twin & GIS Technologies in Intelligent Driving
• AI-Simulation Convergence
• AI-Driven R&D Efficiency
• Automated Testing Analysis for Intelligent Cockpit Systems
• Cockpit Testing

Chinese Simulation Platform and World Model Providers

• 51WORLD
• IAE
• Synkrotron.ai
• Keymotek
• Tsing Standard
• Dotrust Technologies
• Vehinfo
• Beijing Oriental Jicheng
• AUMO (ALINX)
• KOTEI
• Autonomous Driving Test Solution
• Intelligent Cockpit Solution
• Automotive Electronics Software Testing
• AI Simulation Training Platform

Foreign Simulation Platform and World Model Providers

• NVIDIA
• Wayve
• Foretellix
• Siemens
• Hexagon
• AVL
• IPG Automotive
• dSPACE
• MathWorks
• LeddarTech
• NI (EMERSON)
• Ansys (Synopsys)

Companies Featured

• NIO
• XPeng Motors
• Xiaomi
• Li Auto
• Geely
• Zhuoyu Technology
• Horizon
• SenseAuto
• GigaAI
• 51WORLD
• IAE
• Zhejiang PanoSim
• Saimo Technology
• Synkrotron.ai
• PilotD Automotive
• Shengqi Technology
• Keymotek
• Tsing Standard
• Jingwei HiRain
• Dotrust Technologies
• Vehinfo
• Beijing Oriental Jicheng
• AUMO (ALINX)
• Kunyi Electronics
• KOTEI
• NVIDIA
• UE
• Unity
• Wayve
• APPLE
• Foretellix
• Siemens
• Hexagon
• KISSsoft (Gleason)
• AVL
• VECTOR
• IPG Automotive
• dSPACE
• MathWorks
• LeddarTech
• VI-Grade
• NI (EMERSON)
• Ansys (Synopsys) 

For more information about this report visit https://www.researchandmarkets.com/r/116ezs

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