Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation

Traffic simulation plays a crucial role in evaluating and improving autonomous driving planning systems. After being deployed on public roads, autonomous vehicles need to interact with human road participants with different
social preferences
(e.g., selfish or courteous human drivers). To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment. We propose a socially-controllable behavior generation (SCBG) model for this purpose, which allows the users to specify the
level of courtesy
of the generated trajectory while ensuring
realistic
and
human-like
trajectory generation through learning from real-world driving data. Specifically, we define a novel and differentiable measure to quantify the level of courtesy of driving behavior, leveraging marginal and conditional behavior prediction models trained from real-world driving data. The proposed courtesy measure allows us to auto-label the courtesy levels of trajectories from real-world driving data and conveniently train an SCBG model generating trajectories based on the input courtesy values. We examine SCBG on the Waymo Open Motion Dataset (WOMD) and show that we are able to control the SCBG model to generate realistic driving behaviors with desired courtesy levels. In particular, we find that SCBG is able to identify different motion patterns of courteous behaviors according to the scenarios.