Human drivers can have diverse car-following behaviors when interacting with connected and automated vehicles (CAVs) and other human-driven vehicles in mixed traffic where many human-driven vehicles and a limited number of CAVs frequently interact and share the road. In this study, Inverse Reinforcement Learning (IRL) is used to model unique car-following behaviors of different human drivers when interacting with the CAV and another human-driven vehicle by using their driving demonstrations collected from in-field driving tests. The learned driver behavior model is shown that the personalized driving behaviors accurately and consistently can be characterized when following the different types of preceding vehicles in a variety of traffic situations. Furthermore, the energy efficiency of different human-driven vehicles when interacting with the CAV and the human-driven vehicle is investigated with the heterogeneous characteristics of drivers' behaviors, considering driving behaviors have significant influences on vehicle fuel economy. A detailed analysis reveals the significant fuel-saving benefits of the CAV to the following human-driven vehicles during the car-following scenario and the extent of such benefits varies among tested human drivers owing to their intrinsic preferences and perception of CAV. These findings suggest that human-CAV interactions can be effectively leveraged to improve the energy efficiency of mixed traffic.
- Connected and automated vehicles
- driver behavior modeling
- fuel economy
- inverse reinforcement learning