  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 examined the SCBG model on
the Waymo Open Motion Dataset (WOMD) and showed that we were able to control
the SCBG model to generate realistic driving behaviors with desired courtesy
levels. Interestingly, we found that the SCBG model was able to identify
different motion patterns of courteous behaviors according to the scenarios.

Robotics;Artificial Intelligence;Machine Learning