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.

Driving is a sensorimotor task that is done often, and requires a degree of competency that has to be taught. While daily driving is complex and safety critical, performance driving requires a higher degree of competency in handling the vehicle at high speeds and limits of stability and requires years of one-on-one instruction and practice to master. Although driving instructors can help drivers perform better and safer [1], their availability is limited and costly. Hence, there is a clear need for automated teaching which can help drivers improve at the population scale. Driving instructors, e.g. in performance track driving [2], rely on their expertise in the driving task and their inference of student's skill levels to effectively teach students of various skill levels and learning styles. Instructors can gauge their students' skill levels and estimate what a student might do in a given scenario to provide contextually-relevant verbal instructions to the student. For example, consider how an instructor in the passenger seat might instruct a student driver on the appropriate timing for braking or the lateral positioning of the car with respect to the racing line (the optimal minimum time path around a race course). The teacher's ability to judge whether the student can maintain the racing line or oversteer in a turn influences what instructions are provided. An automated teaching system for driving should be able to take in relevant vehicle context (pose and dynamics, map information, etc.) and other factors (eg., driver monitoring) as inputs and output appropriate teaching actions for the

Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions, they may miss these important interaction outcomes. In this paper, we propose NashFormer, a framework for trajectory prediction that leverages game-theoretic inverse reinforcement learning to improve coverage of multi-modal predictions. We use a training-time game-theoretic analysis as an auxiliary loss resulting in improved coverage and accuracy without presuming a taxonomy of actions for the agents. We demonstrate our approach on the interactive split of the Waymo Open Motion Dataset, including four subsets involving scenarios with high interaction complexity. Experiment results show that our predictor produces accurate predictions while covering $33\%$ more potential interactions versus a baseline model.

Near the limits of adhesion, the forces generated by a tire are nonlinear and intricately coupled. Efficient and accurate modelling in this region could improve safety, especially in emergency situations where high forces are required. To this end, we propose a novel family of tire force models based on neural ordinary differential equations and a neural-ExpTanh parameterization. These models are designed to satisfy physically insightful assumptions while also having sufficient fidelity to capture higher-order effects directly from vehicle state measurements. They are used as drop-in replacements for an analytical brush tire model in an existing nonlinear model predictive control framework. Experiments with a customized Toyota Supra show that scarce amounts of driving data -- less than three minutes -- is sufficient to achieve high-performance autonomous drifting on various trajectories with speeds up to 45mph. Comparisons with the benchmark model show a $4 \times$ improvement in tracking performance, smoother control inputs, and faster and more consistent computation time.