# How Shall I Drive? Interaction Modeling and Motion Planning towards Empathetic and Socially-Graceful Driving

## TL;DR

This paper introduces a social gracefulness measure for autonomous vehicle interaction, enabling more empathetic and socially-aware motion planning that results in safer and more natural driving behaviors.

## Contribution

It proposes a novel social gracefulness metric and an empathetic intent inference framework for AVs, improving interaction quality with human drivers.

## Key findings

- AVs can learn passive-aggressive behaviors to influence human drivers.
- Social awareness and empathy are crucial for intent inference in AVs.
- The proposed method enhances the naturalness and safety of autonomous driving.

## Abstract

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.