3Q 2023

Automated Driving Simulation: Simulating the Road to Validate ADAS and ADS

With each passing year, automakers and suppliers are continuing to expand the scope of what advanced driver assistance systems (ADAS) and automated driving systems (ADS) can do to supplement or replace human drivers. One of the primary goals is to improve road safety by reducing the number of crashes. However, driving is a very complex task that humans do with a very high frequency. In the US alone, people drive as much as 3.2 trillion miles per year and only crash about once every half million miles on average.

Validating that ADAS and ADS are actually safer than human drivers is a very high bar, particularly given the nearly infinite variability of the driving environment and the difficulty of reproducing test conditions. Simulation has become a crucial tool for validating automotive safety systems over the past several decades, and it is essential for proving the efficacy of ADAS/ADS.

ADAS/ADS developers rely on a range of simulation tools at all stages of the development and testing process to validate new concepts and ensure that changes have not caused errors in systems that are already demonstrated to work. Open- and closed-loop simulations with software, hardware, and drivers in the loop are all being used extensively. Automated tools to generate testing scenarios are also needed to ensure sufficient coverage of the test suite. Most simulation workflows combine a range of tools from multiple vendors to help guarantee that ADAS and ADS contribute to improved safety before the technology is deployed on public roads.

Pages 14
Tables | Charts | Figures 4
  • Why is simulation essential to validating ADAS/ADS?
  • What types of simulation tools are needed?
  • How does ADAS/ADS simulation work?
  • What are some of the companies providing these tools?
  • Is simulation alone sufficient to validate ADAS/ADS?
  • Automakers
  • Suppliers
  • ADAS/ADS developers
  • Policymakers
  • Transportation regulators
  • Investors




The Emergence of ADAS and ADS Drives Simulation Demand

Replicating the Physical Driving Environment in Virtual Space

Compute Platforms

Simulation Types

Unit and Subsystem Simulation

Full-Stack Simulation

Software in the Loop

Hardware in the Loop

Driver in the Loop

Models and Scenarios

Vehicle Physics Modeling

Sensor Modeling

Scene Modeling

Scenario Building

Model Validation

Safe Automation Needs Virtual Validation

Collecting and Sharing Infrastructure Data

Looking toward Regulations

  • Annual Light Duty Vehicle Deployments by Automation Level, World Markets: 2022-2031
  • NVIDIA DRIVE Sim’s Virtual Driving Environments and Simulated Sensor Inputs
  • VI-grade’s DiM150 Dynamic Driving Simulator at Ford Product Development Center
  • Vehicle Physics Simulation Model
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