An uncertainty cognition-based game model for lane-changing process in mixed driving environment

Abstract

The non-instantaneous nature of lane-changing demands real-time adaptability for autonomous vehicles (AVs) to respond continuously changing traffic conditions. In the mixed environment where AVs coexist with human-driven vehicles (HVs), the lack of inter-vehicle information exchange necessitates the Nash Equilibrium as best response. In addition, the unpredictable intentions of HV introduce uncertainty, posing a challenge for the solution of equilibrium. This paper introduces an aggressiveness parameter reflecting human drivers' yielding tendencies to autonomous vehicles and enables human-like uncertainty cognition during lane changes. To meet the practical solution requirements of the uncertainty cognition-based game model, we propose Proactive Equilibrium Strategy Algorithm (PESA) based on two-stage Nash equilibrium and anticipation of the opponent's next-stage strategy. Utilising Next Generation Simulation (NGSIM) as environmental data, PESA shows safer and more efficient lane-changing behaviour and leads to more favourable post-lane-changing traffic conditions compared to actual data outcomes.

Keywords:

• Automatic vehicle
• lane changing process
• mixed environment
• uncertainty cognition
• Nash equilibrium

Acknowledgments

The authors would like to express their gratitude to NGSIM for the use of its data set. The datasets analysed during the current study are available in the Next Generation Simulation (NGSIM) Vehicle Trajectories and Supporting Data, https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant numbers 72071158 and 72001172].