Autonomous driving systems rely upon sensors and embedded software for localization, perception, motion planning and execution.
These systems can only be released to the public after developers have demonstrated their ability to achieve extremely high levels of safety.
Today’s hands-off autonomous driving systems are largely built with deep-learning algorithms that can be trained to make the right decision for nearly every driving situation. These systems, however, lack the detailed requirements and architecture that have been used up to now to validate safety-critical software, such as that which controls commercial airliners.
Road testing is clearly an essential part of the development process, but billions of miles of road testing would be required to validate the safety of autonomous driving systems and software.
In this whitepaper, you will learn about how ANSYS has leveraged its vast experience in multiphysics simulation and safety-critical embedded software to deliver a complete closed loop simulation platform for virtual testing of autonomous driving systems.
The platform includes physically accurate sensor models, a virtual environment and scenarios, and virtual SIL and HIL testing. These unique capabilities enable you to drive your future autonomous vehicle on virtual test tracks with realistic traffic conditions, including weather, oncoming vehicles and pedestrian scenarios, to train your machine learning software and validate its response to any driving scenario.
Key benefits:
- Accurate simulation Test complete autonomous driving systems in a fraction of the time and cost required for road testing
- Full Integration Sensor simulation is incorporated in a closed-loop simulation environment that interacts with traffic simulators, enabling thousands of driving scenarios to be executed virtually
- Safety validation process Integrated solutions can achieve end-to-end safety in deep-learning-based and other autonomous driving systems
By providing realistic physics-based simulation in real time, ANSYS enables automotive systems engineers to create the safest autonomous driving systems.
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