Impact of Sharing Driving Attitude Information: A Quantitative Study on Lane Changing

Autonomous vehicles (AVs) are expected to be an integral part of the next generation of transportation systems, where they will share the transportation network with human-driven vehicles during the transition period. In this work, we model the interactions between vehicles (two AVs or an AV and a human-driven vehicle) in a lane changing process by leveraging the Stackelberg game. We explicitly model driving attitudes for both vehicles involved in lane changing. We design five cases, in which the two vehicles have different levels of knowledge, and make different assumptions, about the driving attitude of the rival. We conduct theoretical analysis and simulations for different cases in two lane changing scenarios, namely changing lanes from a higher-speed lane to a lower-speed lane, and from a lower-speed lane to a higher-speed lane. We use four metrics (fuel consumption, discomfort, minimum distance gap and lane change success rate) to investigate how the performance of a single vehicle and that of the system will be influenced by the level of information sharing, and whether a vehicle trajectory optimized based on selfish criteria can provide system-level benefits.