PUBLISHER: Guidehouse Insights | PRODUCT CODE: 1328595
PUBLISHER: Guidehouse Insights | PRODUCT CODE: 1328595
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.
Spark
Context
Recommendations
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
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