  In this article, the authors present a novel method to learn the personalized
tactic of discretionary lane-change initiation for fully autonomous vehicles
through human-computer interactions. Instead of learning from human-driving
demonstrations, a reinforcement learning technique is employed to learn how to
initiate lane changes from traffic context, the action of a self-driving
vehicle, and in-vehicle user feedback. The proposed offline algorithm rewards
the action-selection strategy when the user gives positive feedback and
penalizes it when negative feedback. Also, a multi-dimensional driving scenario
is considered to represent a more realistic lane-change trade-off. The results
show that the lane-change initiation model obtained by this method can
reproduce the personal lane-change tactic, and the performance of the
customized models (average accuracy 86.1%) is much better than that of the
non-customized models (average accuracy 75.7%). This method allows continuous
improvement of customization for users during fully autonomous driving even
without human-driving experience, which will significantly enhance the user
acceptance of high-level autonomy of self-driving vehicles.
