Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive
  Controlled Vehicles by Inverse Reinforcement Learning
Ni Dang Tao Shi Zengjie Zhang Wanxin Jin Marion Leibold Martin Buss
The driving style of an Autonomous Vehicle (AV) refers to how it behaves and
interacts with other AVs. In a multi-vehicle autonomous driving system, an AV
capable of identifying the driving styles of its nearby AVs can reliably
evaluate the risk of collisions and make more reasonable driving decisions.
However, there has not been a consistent definition of driving styles for an AV
in the literature, although it is considered that the driving style is encoded
in the AV's trajectories and can be identified using Maximum Entropy Inverse
Reinforcement Learning (ME-IRL) methods as a cost function. Nevertheless, an
important indicator of the driving style, i.e., how an AV reacts to its nearby
AVs, is not fully incorporated in the feature design of previous ME-IRL
methods. In this paper, we describe the driving style as a cost function of a
series of weighted features. We design additional novel features to capture the
AV's reaction-aware characteristics. Then, we identify the driving styles from
the demonstration trajectories generated by the Stochastic Model Predictive
Control (SMPC) using a modified ME-IRL method with our newly proposed features.
The proposed method is validated using MATLAB simulation and an off-the-shelf
experiment.