Interacting with surrounding road users is a key feature of vehicles and
is critical for intelligence testing of autonomous vehicles. The
Existing interaction modalities in autonomous vehicle simulation and
testing are not sufficiently smart and can hardly reflect human-like
behaviors in real world driving scenarios. To further improve the
technology, in this work we present a novel hierarchical
game-theoretical framework to represent naturalistic multi-modal
interactions among road users in simulation and testing, which is then
validated by the Turing test. Given that human drivers have no access to
the complete information of the surrounding road users, the Bayesian
game theory is utilized to model the decision-making process. Then, a
probing behavior is generated by the proposed game theoretic model, and
is further applied to control the vehicle via Markov chain. To validate
the feasibility and effectiveness, the proposed method is tested through
a series of experiments and compared with existing approaches. In
addition, Turing tests are conducted to quantify the human-likeness of
the proposed algorithm. The experiment results show that the proposed
Bayesian game theoretic framework can effectively generate
representative scenes of human-like decision-making during autonomous
vehicle interactions, demonstrating its feasibility and effectiveness.
Corresponding author(s) Email: [email protected]
Heuristics-oriented overtaking decision making for autonomous vehicles using reinforcement learning