Measuring Sociality in Driving Interaction

TL;DR

This paper introduces a Virtual-Game-based Interaction Model (VGIM) with an Interaction Preference Value (IPV) to quantify sociality in driving, enhancing understanding and prediction of human-like driving behaviors in interactive scenarios.

Contribution

The paper proposes a novel sociality measurement framework using IPV within VGIM, including a method to identify IPV from driving data and demonstrate its effectiveness in modeling human driving interactions.

Findings

Drivers show specific social preferences during tasks like turning or going straight.

Strategic competitive actions are used by drivers to coordinate with others.

IPV identification improves motion prediction in interactive driving scenarios.

Abstract

Interacting with other human road users is one of the most challenging tasks for autonomous vehicles. For congruent driving behaviors, it is essential to recognize and comprehend sociality, encompassing both implicit social norms and individualized social preferences of human drivers. To understand and quantify the complex sociality in driving interactions, we propose a Virtual-Game-based Interaction Model (VGIM) that is parameterized by a social preference measurement, Interaction Preference Value (IPV). The IPV is designed to capture the driver's relative inclination towards individual rewards over group rewards. A method for identifying IPV from observed driving trajectory is also developed, with which we assessed human drivers' IPV using driving data recorded in a typical interactive driving scenario, the unprotected left turn. Our findings reveal that (1) human drivers exhibit particular social preference patterns while undertaking specific tasks, such as turning left or proceeding straight; (2) competitive actions could be strategically conducted by human drivers in order to coordinate with others. Finally, we discuss the potential of learning sociality-aware navigation from human demonstrations by incorporating a rule-based humanlike IPV expressing strategy into VGIM and optimization-based motion planners. Simulation experiments demonstrate that (1) IPV identification improves the motion prediction performance in interactive driving scenarios and (2) the dynamic IPV expressing strategy extracted from human driving data makes it possible to reproduce humanlike coordination patterns in the driving interaction.