The Defense Advanced Research Projects Agency (DARPA) has awarded Intel Federal the opportunity to develop simulation solutions for off-road autonomous ground vehicles.
The Robotic Autonomy in Complex Environments with Resiliency – Simulation (RACER-Sim) program aims to create the next generation of off-road simulation platforms to reduce the development cost and bridge the gap between simulation and the real world.
“Intel Labs has already made progress in advancing autonomous vehicle simulation through several projects, including the CARLA simulator, and we’re proud to participate in RACER-Sim to continue contributing to the next frontier of off-road robotics and autonomous vehicles,” German Ros, Autonomous Agents Lab director at Intel Labs.
RACER-Sim includes two phases over 48 months with the aim of accelerating the entire research and development process for designing off-road autonomous ground vehicles.
In phase one, Intel’s focus is to create new simulation platforms and map generation tools that mimic complex off-road environments with the highest accuracy (e.g., physics, sensor modeling, terrain complexity, etc.), at scales never seen before. Intel Labs’ simulation platform will enable customization of future maps, including the creation of massive new environments covering more than 100,000 square miles with just a few clicks.
During phase two, Intel Labs will work with RACER collaborators to accelerate the research and development process by implementing new algorithms without the use of a physical robot. Then, teams will validate the performance of the robot in simulation, saving significant time and resources.
Phase two will also include the development of new sim2real techniques – the concept of training the robot in simulation to acquire skills and then transferring these skills to a corresponding real robotic system – enabling the training of off-road autonomous ground vehicles directly in simulation.
Intel expects new simulation tools to significantly improve the development of autonomous systems using virtual testing, which reduces the risks, costs, and delays associated with traditional testing and verification protocols. In the future, the simulation platform will go beyond validation to create AI models ready for implementation in the real world.