Computational Teaching for Driving via Multi-Task Imitation Learning

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