Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

  • José Gerardo Carrillo González CONACYT
  • Jesús Arámburo Lizárraga UDG
  • Liliana Ibeth Barbosa Santillán UDG
Keywords: simulation, deceleration, mathematical model, algorithm, intersection,

Abstract

This paper proposes a mathematical model to regulate the acceleration (deceleration) applied by self-driving vehicles in car-following situations. A virtual environment is designed to test the model in different circumstances: (1) the followers decelerate in time if the leader decelerates, considering a time delay of up to 5 s to refresh data (vehicles position coordinates) required by the model, (2) with the intention of optimizing space, the vehicles are grouped in platoons, where 3 s of time delay (to update data) is supported if the vehicles have a centre-to-centre spacing of 20 m and a time delay of 1 s is supported at a spacing of 6 m (considering a maximum speed of 20 m/s in both cases), and (3) an algorithm is presented to manage the vehicles’ priority at a traffic intersection, where the model regulates the vehicles’ acceleration (deceleration) and a balance in the number of vehicles passing from each side is achieved.

Author Biographies

José Gerardo Carrillo González, CONACYT

UAM. Department of information and communications systems.

Researcher, Dr.

Jesús Arámburo Lizárraga, UDG

CUCEA. Department of Information Systems.

Research professor, Dr.

Liliana Ibeth Barbosa Santillán, UDG

CUCEA. Department of Information Systems.

Research professor, Dra.

References

[1] Gattami A, Al Alam A, Johansson KH, et al. Establishing Safety for Heavy Duty Vehicle Platooning: A Game Theoretical Approach. IFAC Proceedings Volumes. 2011;44(1): 3818-3823.

[2] Varaiya P. Smart cars on smart roads: problems of control. IEEE Transactions on automatic control. 1993;38(2): 195-207.

[3] Fernandes P, Nunes U. Platooning of autonomous vehicles with intervehicle communications in SUMO traffic simulator. In: Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on; 2010. p. 1313-1318.

[4] Maurya AK, Bokare PS. Study of deceleration behaviour of different vehicle types. International Journal for Traffic & Transport Engineering. 2012;2(3): 253-270.

[5] Wu Z, Liu Y, Pan G. A smart car control model for brake comfort based on car following. IEEE Transactions on Intelligent Transportation Systems. 2009;10(1): 42-46.

[6] Li G, Wang W, Li SE, et al. Effectiveness of flashing brake and hazard systems in avoiding rear-end crashes. Advances in Mechanical Engineering. Volume 2014.

[7] Ahmed KI. Modeling drivers’ acceleration and lane changing behavior. Doctoral dissertation. Massachusetts Institute of Technology; 1999.

[8] Sun J, Ma Z, Li T, et al. Development and application of an integrated traffic simulation and multi-driving simulators. Simulation Modelling Practice and Theory. 2015;59: 1-17.

[9] Wang H, Kearney JK, Cremer J, et al. Steering behaviors for autonomous vehicles in virtual environments. Virtual Reality, 2005. Proceedings. VR 2005. IEEE; 2005. p. 155-162.

[10] Carvalhosa S, Aguiar AP, Pascoal A. Cooperative motion control of multiple autonomous robotic vehicles. Master’s thesis. Instituto Superior Técnico; 2009.

[11] Isler V, Sun D, Sastry S. Roadmap Based Pursuit-Evasion and Collision Avoidance. Robotics: Science and Systems. 2005;1: 257-264.

[12] Dresner K, Stone P. A multiagent approach to autonomous intersection management. Journal of artificial intelligence research. 2008;31: 591-656.

[13] Makarem L, Gillet D. Fluent coordination of autonomous vehicles at intersections. Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on; 2012. p. 2557-2562.

[14] Yan F, Dridi M, El Moudni A. An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach. International Journal of Applied Mathematics and Computer Science. 2013;23(1): 183-200.

[15] Hausknecht M, Au T-C, Stone P. Autonomous intersection management: Multi-intersection optimization. Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on; 2011. p. 4581-4586.

[16] Doniec A, Mandiau R, Piechowiak S, et al. A behavioral multi-agent model for road traffic simulation. Engineering Applications of Artificial Intelligence. 2008;21(8): 1443-1454.

[17] Mandiau R, Champion A, Auberlet J-M, et al. Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation. Applied Intelligence. 2008;28(2): 121-138.

[18] Bokare PS, Maurya AK. Acceleration-deceleration behaviour of various vehicle types. Transportation Research Procedia. 2017;25: 4737-4753.

[19] Boer ER, Kuge N, Yamamura T. Affording realistic stopping behavior: A cardinal challenge for driving simulators. Proceedings of 1st Human-Centered Transportation Simulation Conference; 2001.

[20] González JG, Arámburo-Lizárraga J. Digitalized roads based on GPS data in a virtual world. Procedia Technology. 2013;7: 20-29.

[21] Mattingly WA, Chang D, Paris R, et al. Robot design using Unity for computer games and robotic simulations. Computer Games (CGAMES), 2012 17th International Conference on; 2012. p. 56-59.

[22] Jie J, Yang K, Haihui S. Research on the 3D game scene optimization of mobile phone based on the Unity 3D engine. Computational and Information Sciences (ICCIS), 2011 International Conference on; 2011. p. 875-877.
Published
2018-04-20
How to Cite
1.
Carrillo González JG, Arámburo Lizárraga J, Barbosa Santillán LI. Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data. Promet - Traffic & Transportation [Internet]. 20Apr.2018 [cited 21Aug.2018];30(2):141-9. Available from: http://www.fpz.unizg.hr/traffic/index.php/PROMTT/article/view/2343
Section
Articles