NEO 2016 pp 3-44 | Cite as

Defensive Driving Strategy and Control for Autonomous Ground Vehicle in Mixed Traffic

  • Xiang Li
  • Jian-Qiao Sun
Part of the Studies in Computational Intelligence book series (SCI, volume 731)


One of the challenges of autonomous ground vehicles (AGVs) is to interact with human driven vehicles in the traffic. This paper develops defensive driving strategies and controls for AGVs to avoid problematic vehicles in the mixed traffic. The multi-objective optimization algorithms for local trajectory planning and adaptive cruise control are proposed. The dynamic predictive control is used to derive optimal trajectories in a rolling horizon. The intelligent driver model and lane-changing rules are employed to predict the movement of the vehicles. Multiple performance objectives are optimized simultaneously, including traffic safety, transportation efficiency, driving comfort, tracking error and path consistency. The multi-objective optimization problems are solved with the cell mapping method. Different scenarios are created to test the effectiveness of the defensive driving strategies and adaptive cruise control. Extensive experimental simulations show that the proposed defensive driving strategy and PID-form control are promising and may provide a new tool for designing the intelligent navigation system that helps autonomous vehicles to drive safely in the mixed traffic.


Defensive driving Motion planning Trajectory planning Multi-objective optimization Adaptive cruise control 



The material in this chapter is based on work supported by grants (11172197, 11332008 and 11572215) from the National Natural Science Foundation of China, and a grant from the University of California Institute for Mexico and the United States (UC MEXUS) and the Consejo Nacional de Ciencia y Tecnología de México (CONACYT) through the project “Hybridizing Set Oriented Methods and Evolutionary Strategies to Obtain Fast and Reliable Multi-objective Optimization Algorithms”.


  1. 1.
    Bageshwar, V.L., Garrard, W.L., Rajamani, R.: Model predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Trans. Vehicul. Technol. 53(5), 1573–85 (2004)CrossRefGoogle Scholar
  2. 2.
    Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y.: Dynamical model of traffic congestion and numerical simulation. Phys. Rev. E 51(2), 1035–1042 (1995)CrossRefGoogle Scholar
  3. 3.
    Bose, A., Ioannou, P.: Mixed manual/semi-automated traffic: a macroscopic analysis. Transp. Res. Part C Emerg. Technol. 11C(6), 439–62 (2003)CrossRefGoogle Scholar
  4. 4.
    Brackstone, M., McDonald, M.: Car-following: A historical review. Transp. Res. Part F Traffic Psychol. Behav. 2(4), 181–196 (1999)Google Scholar
  5. 5.
    Burns, L.D.: Sustainable mobility: a vision of our transport future. Nature 497(7448), 181–182 (2013)CrossRefGoogle Scholar
  6. 6.
    Cao, Y.Y., Lin, Z.L., Shamash, Y.: Set invariance analysis and gain-scheduling control for LPV systems subject to actuator saturation. Syst. Control Lett. 46(2), 137–51 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Chandler, R.E., Herman, R., Montroll, E.W.: Traffic dynamics: studies in car following. Oper. Res. 6(2), 165–184 (1958)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chiha, I., Liouane, N., Borne, P.: Tuning PID controller using multiobjective ant colony optimization. Appl. Comput. Intell. Soft Comput. 536326, 7 (2012)Google Scholar
  9. 9.
    Choi, H.C., Jang, S., Chwa, D., Hong, S.K.: Guaranteed cost control of uncertain systems subject to actuator saturation. In: Proceedings of SICE-ICASE International Joint Conference. p. 6. Piscataway, NJ, USA (2007)Google Scholar
  10. 10.
    Cook, P.A.: Conditions for string stability. In: Proceedings of IEEE Transactions on Automatic Control, vol. 54, pp. 991–998. Netherlands (2005)Google Scholar
  11. 11.
    Das, I.: On characterizing the knee of the Pareto curve based on normal-boundary intersection. Struct. Optim. 18(2), 107–115 (1999)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ge, J.I., Avedisov, S.S., Orosz, G.: Stability of connected vehicle platoons with delayed acceleration feedback. In: Proceedings of the ASME Dynamic Systems and Control Conference. Palo Alto, California, USA (2013)Google Scholar
  13. 13.
    Gipps, P.G.: A model for the structure of lane-changing decisions. Transp. Res. Part B Methodol. 20B(5), 403–414 (1986)CrossRefGoogle Scholar
  14. 14.
    González, D., Pérez, J., Lattarulo, R., Milanés, V., Nashashibi, F.: Continuous curvature planning with obstacle avoidance capabilities in urban scenarios. In: Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, pp. 1430–1435 (2014)Google Scholar
  15. 15.
    Hardy, J., Campbell, M.: Contingency planning over probabilistic obstacle predictions for autonomous road vehicles. IEEE Trans. Robot. 29(4), 913–929 (2013)CrossRefGoogle Scholar
  16. 16.
    Hidas, P.: Modelling lane changing and merging in microscopic traffic simulation. Transp. Res. Part C Emerg. Technol. 10C(5–6), 351–371 (2002)CrossRefGoogle Scholar
  17. 17.
    Hidas, P.: Modelling vehicle interactions in microscopic simulation of merging and weaving. Transp. Res. Part C Emerg. Technol. 13(1), 37–62 (2005)CrossRefGoogle Scholar
  18. 18.
    Howard, T.M., Green, C.J., Kelly, A., Ferguson, D.: State space sampling of feasible motions for high-performance mobile robot navigation in complex environments. J. Field Robot. 25(6–7), 325–345 (2008)CrossRefGoogle Scholar
  19. 19.
    Hsu, C.S.: Cell-to-cell Mapping: A Method of Global Analysis for Nonlinear Systems. Springer, New York (1987)CrossRefzbMATHGoogle Scholar
  20. 20.
    Hu, T.S., Lin, Z.L., Chen, B.M.: An analysis and design method for linear systems subject to actuator saturation and disturbance. Automatica 38(2), 351–9 (2002)CrossRefzbMATHGoogle Scholar
  21. 21.
    Huang, S., Ren, W.: Autonomous intelligent cruise control with actuator delays. J. Intell. Robot. Syst. Theory Appl. 23(1), 27–43 (1998)CrossRefzbMATHGoogle Scholar
  22. 22.
    Ioannou, P., Xu, Z.: Throttle and brake control systems for automatic vehicle following. J. Intell. Transp. Syst. 1(4), 345–77 (1994)Google Scholar
  23. 23.
    Jonsson, J.: Fuel optimized predictive following in low speed conditions. In: Proceedings of Modeling and Control of Economic Systems, p. 119. Klagenfurt, Austria (2003)Google Scholar
  24. 24.
    Katrakazas, C., Quddus, M., Chen, W.H., Deka, L.: Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transp. Res Part C Emerg. Technol. 60, 416–442 (2015)CrossRefGoogle Scholar
  25. 25.
    Kelly, A., Nagy, B.: Reactive nonholonomic trajectory generation via parametric optimal control. Int. J. Robot. Res. 22(7–8), 583–601 (2003)CrossRefGoogle Scholar
  26. 26.
    Kesting, A., Treiber, M., Helbing, D.: General lane-changing model mobil for car-following models. Transp. Res. Record 1999, 86–94 (2007)CrossRefGoogle Scholar
  27. 27.
    Khoie, M., Salahshoor, K., Nouri, E., Sedigh, A.K.: PID controller tuning using multi-objective optimization based on fused genetic-immune algorithm and immune feedback mechanism. In: Proceedings of Advanced Intelligent Computing Theories and Applications, pp. 267–76. Berlin, Germany (2011)Google Scholar
  28. 28.
    Li, S., Li, K., Rajamani, R., Wang, J.: Model predictive multi-objective vehicular adaptive cruise control. IEEE Trans. Control Syst. Technol. 19(3), 556–566 (2011)CrossRefGoogle Scholar
  29. 29.
    Li, X., Sun, J.Q.: Effect of interactions between vehicles and pedestrians on fuel consumption and emissions. Phys. A Stat. Mech. Appl. 416, 661–675 (2014)CrossRefGoogle Scholar
  30. 30.
    Li, X., Sun, J.Q.: Studies of vehicle lane-changing to avoid pedestrians with cellular automata. Phys. A Stat. Mech. Appl. 438, 251–271 (2015)CrossRefGoogle Scholar
  31. 31.
    Li, X., Sun, J.Q.: Effects of turning and through lane sharing on traffic performance at intersections. Phys. A Stat. Mech. Appl. 444, 622–640 (2016)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Li, X., Sun, J.Q.: Effects of vehicle-pedestrian interaction and speed limit on traffic performance of intersections. Phys. A Stat. Mech. Appl. 460, 335–347 (2016)CrossRefGoogle Scholar
  33. 33.
    Li, X., Sun, J.Q.: Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact. Phys. A Stat. Mech. Appl. 467, 41–58 (2017)CrossRefGoogle Scholar
  34. 34.
    Li, X., Sun, Z., Cao, D., Liu, D., He, H.: Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mechanical Systems and Signal Processing (2015). doi: 10.1016/j.ymssp.2015.10.021 Google Scholar
  35. 35.
    Liebner, M., Baumann, M., Klanner, F., Stiller, C.: Driver intent inference at urban intersections using the intelligent driver model. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 1162–1167 (2012)Google Scholar
  36. 36.
    Martinez, J.J., de Wit, C.C.: A safe longitudinal control for adaptive cruise control and stop-and-go scenarios. IEEE Trans. Control Syst. Technol. 15(2), 246–58 (2007)CrossRefGoogle Scholar
  37. 37.
    McNaughton, M., Urmson, C., Dolan, J.M., Lee, J.W.: Motion planning for autonomous driving with a conformal spatiotemporal lattice. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 4889–4895 (2011)Google Scholar
  38. 38.
    Miller, I., Campbell, M., Huttenlocher, D.: Efficient unbiased tracking of multiple dynamic obstacles under large viewpoint changes. IEEE Trans. Robot. 27(1), 29–46 (2011)CrossRefGoogle Scholar
  39. 39.
    Naus, G., van den Bleek, R., Ploeg, J., Scheepers, B., van de Molengraft, R., Steinbuch, M.: Explicit mpc design and performance evaluation of an acc stop-&-go. In: Proceedings of American Control Conference, pp. 224–9. Piscataway, NJ, USA (2008)Google Scholar
  40. 40.
    Naus, G.J.L., Ploeg, J., de Molengraft, M.J.G.V., Heemels, W.P.M.H., Steinbuch, M.: Design and implementation of parameterized adaptive cruise control: an explicit model predictive control approach. Control Eng. Pract. 18(8), 882–892 (2010)CrossRefzbMATHGoogle Scholar
  41. 41.
    Orosz, G., Moehlis, J., Bullo, F.: Delayed car-following dynamics for human and robotic drivers. In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Washington, DC, USA (2011)Google Scholar
  42. 42.
    Orosz, G., Shah, S.P.: A nonlinear modeling framework for autonomous cruise control. In: Proceedings of the ASME 5th Annual Dynamic Systems and Control Conference Joint with the JSME 11th Motion and Vibration Conference, vol. 2, pp. 467–471. Fort Lauderdale, FL, United States (2012)Google Scholar
  43. 43.
    Orosz, G., Wilson, R.E., Stépán, G.: Traffic jams: dynamics and control. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 368(1928), 4455–4479 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    Pareto, V.: Manual of Political Economy. The MacMillan Press, London (1971)Google Scholar
  45. 45.
    Qin, W.B., Orosz, G.: Digital effects and delays in connected vehicles: Linear stability and simulations. In: Proceedings of the ASME Dynamic Systems and Control Conference. Palo Alto, California, USA (2013)Google Scholar
  46. 46.
    Schütze, O., Laumanns, M., Coello, C.A.C.: Approximating the knee of an MOP with stochastic search algorithms. In: Parallel Problem Solving from Nature, pp. 795–804. Springer, Berlin (2008)Google Scholar
  47. 47.
    Schwesinger, U., Rufli, M., Furgale, P., Siegwart, R.: A sampling-based partial motion planning framework for system-compliant navigation along a reference path. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), pp. 391–396 (2013)Google Scholar
  48. 48.
    Shim, T., Adireddy, G., Yuan, H.: Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control. Inst. Mech. Eng. Part D J. Autom. Eng. 226(6), 767–778 (2012)Google Scholar
  49. 49.
    Shinar, D., Compton, R.: Aggressive driving: an observational study of driver, vehicle, and situational variables. Accident Anal. Prevention 36(3), 429–437 (2004)CrossRefGoogle Scholar
  50. 50.
    Swaroop, D., Hedrick, J.K.: String stability of interconnected systems. IEEE Trans. Autom. Control 41(3), 349–57 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  51. 51.
    Toledo, T., Zohar, D.: Modeling duration of lane changes. Transp. Res. Record 1999, 71–78 (2007)CrossRefGoogle Scholar
  52. 52.
    Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62(2), 1805–1824 (2000)CrossRefzbMATHGoogle Scholar
  53. 53.
    Treiber, M., Kanagaraj, V.: Comparing numerical integration schemes for time-continuous car-following models. Phys. A Stat. Mech. Appl. 419, 183–195 (2015)CrossRefGoogle Scholar
  54. 54.
    Tsugawa, S.: An overview on energy conservation in automobile traffic and transportation with its. In: Proceedings of the IEEE International Vehicle Electronics Conference, pp. 137–42. Piscataway, NJ, USA (2001)Google Scholar
  55. 55.
    Varaiya, P.: Smart cars on smart roads: problems of control. IEEE Trans. Autom. Control 38(2), 195–207 (1993)MathSciNetCrossRefGoogle Scholar
  56. 56.
    Wei, J., Snider, J.M., Gu, T., Dolan, J.M., Litkouhi, B.: A behavioral planning framework for autonomous driving. In: Proceedings of IEEE Intelligent Vehicles Symposium Proceedings, pp. 458–464 (2014)Google Scholar
  57. 57.
    Xiao, L., Gao, F.: Practical string stability of platoon of adaptive cruise control vehicles. In: Proceedings of IEEE Transactions on Intelligent Transportation Systems, vol. 12, pp. 1184–94. USA (2011)Google Scholar
  58. 58.
    Xiong, F., Qin, Z., Xue, Y., Schütze, O., Ding, Q., Sun, J.: Multi-objective optimal design of feedback controls for dynamical systems with hybrid simple cell mapping algorithm. Commun. Nonlinear Sci. Numer. Simul. 19(5), 1465–1473 (2014)MathSciNetCrossRefGoogle Scholar
  59. 59.
    Xiong, F.R., Qin, Z.C., Ding, Q., Hernández, C., Fernández, J., Schütze, O., Sun, J.Q.: Parallel cell mapping method for global analysis of high-dimensional nonlinear dynamical systems. J. Appl. Mech. 82(11), (2015). doi: 10.1115/1.4031149
  60. 60.
    Xu, W., Wei, J., Dolan, J.M., Zhao, H., Zha, H.: A real-time motion planner with trajectory optimization for autonomous vehicles. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2061–2067 (2012)Google Scholar
  61. 61.
    Yadlapalli, S.K., Darbha, S., Rajagopal, K.R.: Information flow and its relation to stability of the motion of vehicles in a rigid formation. In: Proceedings of IEEE Transactions on Automatic Control, vol. 51, pp. 1315–19. USA (2006)Google Scholar
  62. 62.
    Yoon, Y., Shin, J., Kim, H.J., Park, Y., Sastry, S.: Model-predictive active steering and obstacle avoidance for autonomous ground vehicles. Control Eng. Pract. 17(7), 741–750 (2009)CrossRefGoogle Scholar
  63. 63.
    You, F., Zhang, R., Lie, G., Wang, H., Wen, H., Xu, J.: Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Syst. Appl. 42(14), 5932–5946 (2015)CrossRefGoogle Scholar
  64. 64.
    Zhang, J., Ioannou, P.A.: Longitudinal control of heavy trucks in mixed traffic: Environmental and fuel economy considerations. IEEE Trans. Intell. Transp. Syst. 7(1), 92–104 (2006)CrossRefGoogle Scholar
  65. 65.
    Zhang, L., Orosz, G.: Designing network motifs in connected vehicle systems: delay effects and stability. In: Proceedings of the ASME Dynamic Systems and Control Conference. Palo Alto, California, USA (2013)Google Scholar
  66. 66.
    Zhang, Y., Kosmatopoulos, E.B., Ioannou, P.A., Chien, C.C.: Autonomous intelligent cruise control using front and back information for tight vehicle following maneuvers. IEEE Trans. Vehicul. Technol. 48(1), 319–328 (1999)CrossRefGoogle Scholar
  67. 67.
    Ziegler, J., Bender, P., Dang, T., Stiller, C.: Trajectory planning for Bertha - A local, continuous method. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 450–457 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of MechanicsTianjin UniversityTianjinChina
  2. 2.School of EngineeringUniversity of CaliforniaMercedUSA

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