Active Altruism Learning and Information Sufficiency for Autonomous Driving
This work addresses safe vehicle interaction in autonomous driving, but it is incremental as it builds on existing Active Learning methods.
The paper tackles the problem of enabling autonomous vehicles to safely interact by revealing other vehicles' altruistic preferences through exploratory actions, and demonstrates that a reward function with Information Sufficiency property prevents inadequate exploration and leads to improved behavior.
Safe interaction between vehicles requires the ability to choose actions that reveal the preferences of the other vehicles. Since exploratory actions often do not directly contribute to their objective, an interactive vehicle must also able to identify when it is appropriate to perform them. In this work we demonstrate how Active Learning methods can be used to incentivise an autonomous vehicle (AV) to choose actions that reveal information about the altruistic inclinations of another vehicle. We identify a property, Information Sufficiency, that a reward function should have in order to keep exploration from unnecessarily interfering with the pursuit of an objective. We empirically demonstrate that reward functions that do not have Information Sufficiency are prone to inadequate exploration, which can result in sub-optimal behaviour. We propose a reward definition that has Information Sufficiency, and show that it facilitates an AV choosing exploratory actions to estimate altruistic tendency, whilst also compensating for the possibility of conflicting beliefs between vehicles.