A simulation study of cooperative and autonomous vehicles (CAV) considering courtesy, ethics, and fairness

PLoS One. 2023 May 3;18(5):e0283649. doi: 10.1371/journal.pone.0283649. eCollection 2023.

Abstract

Autonomous vehicles (AV) can be programmed to act cooperatively. Previous research on cooperative and autonomous vehicles (CAV) suggests they can substantially improve traffic system operations in terms of mobility and safety. However, these studies do not explicitly take each vehicle's potential gain/loss into consideration and ignore their individual levels of willingness to cooperate. They do not account for ethics and fairness either. In this study, several cooperation/courtesy strategies are proposed to address the above issues. These strategies are grouped into two categories based on non-instrumental and instrumental principles. Non-instrumental strategies make courtesy/cooperation decisions based on some courtesy proxies and a user-specified courtesy level, while instrumental strategies are based only on courtesy proxies related to local traffic performance. Also, a new CAV behavior modeling framework is proposed based on our previous work on cooperative car-following and merging (CCM) control. With such a framework, the proposed courtesy strategies can be easily implemented. The proposed framework and courtesy strategies are coded in SUMO microscopic traffic simulator. They are evaluated considering different levels of traffic demand on a freeway corridor consisting of a work zone and three weaving areas of different types. Interesting findings are drawn from the simulation results, one of which is that the instrumental Local Utilitarianism strategy performs the best in terms of mobility, safety, and fairness. In the future, auction-based strategies can be considered to model how CAV make decisions.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Accidents, Traffic*
  • Automobile Driving*
  • Autonomous Vehicles
  • Computer Simulation
  • Ethical Theory

Associated data

  • figshare/10.6084/m9.figshare.22310266.v1

Grants and funding

N.E. NSF 1734521 U.S. National Science Foundation https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734521&HistoricalAwards=false No.