Estimating the Social Gap With a Game Theory Model of Lane Changing

Abstract

Changing lanes is a commonly-used technique for drivers to either overtake slow-moving cars or enter/exit highway ramps. Optional lane changes may save drivers travel time but increase the risk of collision with others. Drivers make such decisions based on experience and emotion rather than analysis, and thus may fail to select the best solution while in a dynamic state of flux. Unlike human drivers, autonomous vehicles can systematically analyze their surroundings and make real-time decisions accordingly. This paper develops a game theory-based lane-changing model by comparing two types of optimization methods. To realize our expectations, we need to first investigate the payoff function of drivers in discretionary lane-changing maneuvers and then quantify it in an equation of costs that trades-off safety and time-saving. After the evaluation for each alternative strategy combination, the results show that there exists a social gap in the discretionary lane-changing game. To deal with that problem, we provide some suggestions for future policy as well as autonomous vehicle controller designs, offering solutions to reduce the impact of disturbances and crashes caused by inappropriate lane changes, and also, inspire further research about more complex cases.