How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving
This addresses safety and social acceptance issues for autonomous vehicles, but it is incremental as it builds on existing game-theoretic and control methods.
The paper tackles the problem of autonomous vehicles lacking gracefulness in interactions with human drivers by proposing a model predictive control framework based on social gracefulness, showing that empathy improves intent inference and enabling behaviors like passive-aggressive motions to gently force yielding.
While intelligence of autonomous vehicles (AVs) has significantly advanced in recent years, accidents involving AVs suggest that these autonomous systems lack gracefulness in driving when interacting with human drivers. In the setting of a two-player game, we propose model predictive control based on social gracefulness, which is measured by the discrepancy between the actions taken by the AV and those that could have been taken in favor of the human driver. We define social awareness as the ability of an agent to infer such favorable actions based on knowledge about the other agent's intent, and further show that empathy, i.e., the ability to understand others' intent by simultaneously inferring others' understanding of the agent's self intent, is critical to successful intent inference. Lastly, through an intersection case, we show that the proposed gracefulness objective allows an AV to learn more sophisticated behavior, such as passive-aggressive motions that gently force the other agent to yield.