Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning

Success in racing requires a unique combination of vehicle setup, understanding of the racetrack, and human expertise. Since building and testing many different vehicle configurations in the real world is prohibitively expensive, high-fidelity simulation is a critical part of racecar development. However, testing different vehicle configurations still requires expert human input in order to evaluate their performance on different racetracks. In this work, we present the first steps towards an autonomous test driver, trained using deep reinforcement learning, capable of evaluating changes in vehicle setup on racing performance while driving at the level of the best human drivers. In addition, the autonomous driver model can be tuned to exhibit more human-like behavioral patterns by incorporating imitation learning into the RL training process. This extension permits the possibility of driver-specific vehicle setup optimization.

Developing a Series of AI Challenges for the United States Department of the Air Force

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances.