A control law for vehicle merging inspired by dragonfly behavior

Autonomous control of vehicles has recently attracted considerable attention. In this sense, vehicle merging has become an important topic in this field of research. However, in conventional studies, the controlled vehicle must calculate the movement of other surrounding vehicles to complete the merge, requiring high computational costs. In this paper, we focus on dragonfly behavior to solve this issue. Indeed, insects can behave adaptively in the complex real world in spite of the limited size of their brains. They reduce the computational requirements of their brain by relying on different properties of their surroundings, basing their intelligent behaviors on simple strategies. The behavior of a dragonfly when chasing a prey is an example of these strategies. In this study, we address the vehicle merging maneuver by applying dragonfly’s strategies to control the movement of the merging vehicle. We propose a simple control method inspired by the aforementioned strategies and, finally, we present simulation results that were conducted to demonstrate the effectiveness of this method.

[1]  Taketoshi Kawabe,et al.  Real-time generation of cooperative merging trajectory on the motor way using model predictive control scheme , 2015, 2015 European Control Conference (ECC).

[2]  Hikaru Nishira,et al.  Automotive longitudinal speed pattern generation with acceleration constraints aiming at mild merging using model predictive control method , 2013, 2013 9th Asian Control Conference (ASCC).

[3]  A. Georgopoulos,et al.  Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction , 2012, Proceedings of the National Academy of Sciences.

[4]  Hikaru Nishira,et al.  Cooperative vehicle path generation during merging using model predictive control with real-time optimization , 2015 .

[5]  Vicente Milanés Montero,et al.  Automated On-Ramp Merging System for Congested Traffic Situations , 2011, IEEE Transactions on Intelligent Transportation Systems.

[6]  R. Olberg,et al.  Prey pursuit and interception in dragonflies , 2000, Journal of Comparative Physiology A.

[7]  Hikaru Nishira,et al.  Merging trajectory generation for vehicle on a motor way using receding horizon control framework consideration of its applications , 2014, 2014 IEEE Conference on Control Applications (CCA).

[8]  Guizhen Yu,et al.  Automated on-ramp merging control algorithm based on internet-connected vehicles , 2013 .

[9]  Kazuyuki Ito,et al.  Importance of real-world properties in chasing task: simulation and analysis of dragonfly’s behavior , 2014, Artificial Life and Robotics.

[10]  Anthony Leonardo,et al.  Internal models direct dragonfly interception steering , 2014, Nature.

[11]  R. Olberg,et al.  Visual control of prey-capture flight in dragonflies , 2012, Current Opinion in Neurobiology.

[12]  R. Olberg,et al.  Eye movements and target fixation during dragonfly prey-interception flights , 2007, Journal of Comparative Physiology A.