The Effect of Deceiving Vehicles in an Autonomous Intersection

Intersection management systems that control the trajectory of multiple autonomous vehicles have the potential to solve many of the issues associated with the increasing demand on roads. Models of such systems typically assume that the information transmitted between each vehicle and the central controller is accurate and free from manipulation. We consider vehicles that broadcast false information about their location to gain priority over others and reduce journey times. We model the effects of introducing a number of deceiving vehicles to the traffic flow at an intersection. By simulating this system over a range of traffic volumes on each lane we are able to measure the advantage gained through deception and its impact on the system. We find that using deception is always beneficial to the individual and usually detrimental to the system as a whole. There are, however, some counterintuitive cases where the intersection capacity increases because of the actions of the deceiving vehicles.

[1]  Gaurav Bhatia,et al.  STIP: Spatio-temporal intersection protocols for autonomous vehicles , 2014, 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[2]  Sarbani Roy,et al.  D&RSense: Detection of Driving Patterns and Road Anomalies , 2018, 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU).

[3]  André Luckow,et al.  Optimal scheduling of autonomous vehicle arrivals at intelligent intersections via MILP , 2017, 2017 American Control Conference (ACC).

[4]  Michael Wooldridge,et al.  Computation and the prisoner's dilemma , 2012, IEEE Intelligent Systems.

[5]  Ilja Radusch,et al.  Survey and Classification of Cooperative Automated Driver Assistance Systems , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[6]  Chih-Ping Yeh,et al.  Lane Keeping System by Visual Technology , 2017 .

[7]  Denis Gillet,et al.  Information sharing among autonomous vehicles crossing an intersection , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[8]  Dale Richards,et al.  To delegate or not to delegate: A review of control frameworks for autonomous cars. , 2016, Applied ergonomics.

[9]  Zhixia Li,et al.  Sustainability effects of next-generation intersection control for autonomous vehicles , 2015 .

[10]  Chen Chen,et al.  Driver’s Intention Identification and Risk Evaluation at Intersections in the Internet of Vehicles , 2018, IEEE Internet of Things Journal.

[11]  Peter Stone,et al.  A Multiagent Approach to Autonomous Intersection Management , 2008, J. Artif. Intell. Res..

[12]  Gerardo Iñiguez,et al.  Effects of deception in social networks , 2014, Proceedings of the Royal Society B: Biological Sciences.

[13]  Urbano Nunes,et al.  Intelligent traffic management at intersections supported by V2V and V2I communications , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.