Journal of Intelligent & Robotic Systems

, Volume 72, Issue 3–4, pp 559–590 | Cite as

Multi-Level Planning for Semi-autonomous Vehicles in Traffic Scenarios Based on Separation Maximization

  • Rahul Kala
  • Kevin Warwick


The planning of semi-autonomous vehicles in traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and outbound traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.


Unmanned ground vehicles Dijkstra’s algorithm Optimization Robotic motion planning Nonholonomic constraints Autonomous vehicles 

Mathematics Subject Classification (2010)



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material (66 kb)
Online Resouce 1: Path traced by single vehicle for various scenarios (ZIP 66.0 KB) (172 kb)
Online Resouce 2: Path traced by two vehicles for various scenarios (ZIP 171 KB)


  1. 1.
    Arai, T., Pagello, E., Parker, L.E.: Editorial: advances in multi-robot systems. IEEE Trans. Robot. Autom. 18(5), 655–661 (2002)CrossRefGoogle Scholar
  2. 2.
    Svestka, P., Overmars, M.H.: Coordinated motion planning for multiple car-like robots using probabilistic roadmaps. In: Proceedings of the 1995 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1631–1636. Nagoya, Japan (1995)Google Scholar
  3. 3.
    Sánchez-Ante, G., Latombe, J.C.: Using a PRM planner to compare centralized and decoupled planning for multi-robot systems. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2112–211. Washington, DC (2002)Google Scholar
  4. 4.
    Clark, C.M., Rock, S.M., Latombe, J.C.: Dynamic networks for motion planning in multi-robot space systems. In: Proceedings of the 7th International Symposium on Artificial Intelligence, Robotics and Automation in Space i-SAIRA. Nara, Japan (2003)Google Scholar
  5. 5.
    Kala, R., Shukla, A., Tiwari, R.: Robotic path planning using multi neuron heuristic search. In: Proceedings of the ACM 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, ICIS 2009, pp. 1318–1323. Seoul, Korea (2009)Google Scholar
  6. 6.
    Xiao, J., Michalewicz, Z., Zhang, L., Trojanowski, K.: Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans. Evol. Comput. 1(1), 18–28 (1997)CrossRefGoogle Scholar
  7. 7.
    Kala, R., Shukla, A., Tiwari, R.: Robotic path planning using evolutionary momentum based exploration. J. Exp. Theor. Artif. Intell. 23(4), 469–495 (2011)CrossRefGoogle Scholar
  8. 8.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings of the 1985 IEEE International Conference on Robotics and Automation, pp. 500–505. St. Louis, Missouri (1985)Google Scholar
  9. 9.
    Kala, R., Shukla, A., Tiwari, R.: Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning. Artif. Intell. Rev. 33(4), 275–306 (2010)CrossRefGoogle Scholar
  10. 10.
    Kala, R., Shukla, A., Tiwari, R., Roongta, S., Janghel, R.R.: Mobile robot navigation control in moving obstacle environment using genetic algorithm, artificial neural networks and A* algorithm. In: Proceedings of the IEEE World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 705–713. Los Angeles/Anaheim (2009)Google Scholar
  11. 11.
    Tu, K.Y., Baltes, J.: Fuzzy potential energy for a map approach to robot navigation. Robot. Auton. Syst. 54(7), 574–589 (2006)CrossRefGoogle Scholar
  12. 12.
    Baxter, J.L., Burke, E.K., Garibald, J.M., Normanb, M.: Shared potential fields and their place in a multi-robot co-ordination taxonomy. Robot. Auton. Syst. 57(10), 1048–1055 (2009)CrossRefGoogle Scholar
  13. 13.
    Lian, F.L., Murray, R.: Cooperative task planning of multi-robot systems with temporal constraints. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, ICRA ’03, vol. 2, pp. 2504–2509. Taipei, Taiwan (2003)Google Scholar
  14. 14.
    Marchese, F.M.: Multiple mobile robots path-planning with MCA. In: Proceedings of the 2006 IEEE International Conference on Autonomic and Autonomous Systems, ICAS’06, pp. 56–56. Silicon Valley, California (2006)Google Scholar
  15. 15.
    Klancar, G., Skrjanc, I.: A case study of the collision-avoidance problem based on Bernstein–Bézier path tracking for multiple robots with known constraints. J. Intell. Robot. Syst. 60(2), 317–337 (2010)CrossRefzbMATHGoogle Scholar
  16. 16.
    Asama, H., Matsumoto, A., Ishida, Y.: Design of an autonomous and distributed robot system: ACTRESS. In: Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems, pp. 283–290. Tsukuba, Japan (1989)Google Scholar
  17. 17.
    Asama, H., Ozaki, K., Itakura, H., Matsumoto, A., Ishida, Y., Endo, I.: Collision avoidance among multiple mobile robots based on rules and communication. In: Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems, pp. 1215–1220. Osaka, Japan (1991)Google Scholar
  18. 18.
    Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B., Johnston, D., Klumpp, S., Langer, D., Levandowski, A., Levinson, J., Marcil, J., Orenstein, D., Paefgen, J., Penny, I., Petrovskaya, A., Pflueger, M., Stanek, G., Stavens, D., Vogt, A., Thrun, S.: Junior: the Stanford entry in the urban challenge. J. Field Rob. 25(9), 569–597 (2008)CrossRefGoogle Scholar
  19. 19.
    Crane, C., Armstrong, D., Arroyo, A., Baker, A., Dankel, D., Garcia, G., Johnson, N., Lee, J., Ridgeway, S., Schwartz, E., Thorn, E., Velat, S., Yoon, J., Washburn, J.: Team gator nation’s autonomous vehicle development for the 2007 DARPA urban challenge. J. Aerosp. Comput. Inform. Commun. 4(12), 1059–1085 (2007)CrossRefGoogle Scholar
  20. 20.
    Vendrell, E., Mellado, M., Crespo, A.: Robot planning and re-planning using decomposition, abstraction, deduction, and prediction. Eng. Appl. Artif. Intell. 14(4), 505–518 (2001)CrossRefGoogle Scholar
  21. 21.
    Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Norwell (1991)CrossRefGoogle Scholar
  22. 22.
    Snape, J., van den Berg, J., Guy, S.J., Manocha, D.: Independent navigation of multiple mobile robots with hybrid reciprocal velocity obstacles. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robotic Systems, pp. 5917–5922. St. Louis, MO (2009)Google Scholar
  23. 23.
    Ortigosa, N., Morillas, S., Peris-Fajarnes, G.: Obstacle-free pathway detection by means of depth maps. J. Intell. Robot. Syst. 63(1), 115–129 (2011)CrossRefGoogle Scholar
  24. 24.
    Shinzato, P.Y., Wolf, D.F.: A road following approach using artificial neural networks combinations. J. Intell. Robot. Syst. 62(3–4), 527–546 (2011)CrossRefGoogle Scholar
  25. 25.
    Reif, J.H., Wang, H.: Social potential fields: a distributed behavioral control for autonomous robots. Robot. Auton. Syst. 27(3), 171–194 (1999)CrossRefGoogle Scholar
  26. 26.
    Gayle, R., Moss, W., Lin, M.C., Manocha, D.: Multi-robot coordination using generalized social potential fields. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation, pp. 106–113. Kobe, Japan (2009)Google Scholar
  27. 27.
    Bennewitz, M., Burgard, W., Thrun, S.: Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots. Robot. Auton. Syst. 41(2–3), 89–99 (2002)CrossRefGoogle Scholar
  28. 28.
    Todt, E., Raush, G., Sukez, R.: Analysis and classification of multiple robot coordination methods. In: Proceedings of the 2000 IEEE International Conference on Robotics & Automation, pp. 3158–3163. San Francisco, CA (2000)Google Scholar
  29. 29.
    Kant, K., Zucker, S.W.: Toward efficient trajectory planning: the path-velocity decomposition. Int. J. Rob. Res. 5(3), 72–89 (1986)CrossRefGoogle Scholar
  30. 30.
    Wilkie, D., van den Berg, J., Manocha, D.: Generalized velocity obstacles. In: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5573–5578. St. Louis, USA (2009)Google Scholar
  31. 31.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Section 33.3: Finding the Convex Hull, Introduction to Algorithms, 2nd edn, pp. 947–957. MIT Press, MA (2001)Google Scholar
  32. 32.
    Leroy, S., Laumond, J.P., Simeon, T.: Multiple path coordination for mobile robots: a geometric algorithm. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, IJCAI’99, vol. 2, pp. 1118–1123. San Francisco, CA (1999)Google Scholar
  33. 33.
    Yahja, A., Stentz, A., Singh, S., Brumitt, B.L.: Framed-quadtree path planning for mobile robots operating in sparse environments. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 650–655. Leuven, Belgium (1998)Google Scholar
  34. 34.
    Kambhampati, S., Davis, L.S.: Multiresolution path planning for mobile robots. IEEE J. Robot. Auton. 2(3), 135–145 (1986)CrossRefGoogle Scholar
  35. 35.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Section 24.3: Dijkstra’s Algorithm, Introduction to Algorithms, 2nd edn, pp. 595–601. MIT Press, MA (2001)Google Scholar
  36. 36.
    Urmson, C., Baker, C., Dolan, J.M., Rybski, P., Salesky, B., Whittaker, W.L., Ferguson, D., Darms, M.: Autonomous driving in traffic: boss and the urban challenge. AI Mag. 30(2), 17–29 (2009)Google Scholar
  37. 37.
    Sewall, J., van den Berg, J., Lin, M.C., Manocha, D.: virtualized traffic: reconstructing traffic flows from discrete spatiotemporal data. IEEE Trans. Vis. Comput. Graph. 17(1), 26–37 (2011)CrossRefGoogle Scholar
  38. 38.
    Bartels, R.H., Beatty, J.C., Barsky, B.A.: An Introduction to Splines for Use in Computer Graphics and Geometric Modelling. Morgan Kaufmann, San Francisco, CA (1987)Google Scholar
  39. 39.
    de Boor, C.: A Practical Guide to Splines. Springer Verlag, Heidelberg (1978)CrossRefzbMATHGoogle Scholar
  40. 40.
    Xidias, E.K., Azariadis, P.N.: Mission design for a group of autonomous guided vehicles. Robot. Auton. Syst. 59(1) 34–43 (2011)CrossRefGoogle Scholar
  41. 41.
    Peng, J., Akella, S.: Coordinating multiple robots with Kinodynamic constraints along specified paths. Int. J. Robot. Res. 24(4), 295–310 (2005)CrossRefGoogle Scholar
  42. 42.
    Schubert, R., Schulze, K., Wanielik, G.: Situation assessment for automatic lane-change maneuvers. IEEE Trans. Intell. Transp. Syst. 11(3), 607–616 (2010)CrossRefGoogle Scholar
  43. 43.
    Furda, A., Vlacic, L.: Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making. IEEE Intel. Trasport. Syst. Magz. 3(1), 4–17 (2011)CrossRefGoogle Scholar
  44. 44.
    Hegeman, G., Tapani, A., Hoogendoorn, S.: Overtaking assistant assessment using traffic simulation. Transp. Res. Part C 17(6), 617–630 (2009)CrossRefGoogle Scholar
  45. 45.
    Wang, F., Yang, M., Yang, R.: Conflict-probability-estimation-based overtaking for intelligent vehicles. IEEE Trans. Intell. Transp. Syst. 10(2), 366–370 (2009)CrossRefGoogle Scholar
  46. 46.
    Lee, J.W., Choy, Y.I., Sugisakaz, M., Lee, J.J.: Study of novel heterogeneous ant colony optimization algorithm for global path planning. In: Proceedings of the 2010 IEEE International Symposium on Industrial Electronics, pp. 1961–1966 (2010)Google Scholar
  47. 47.
    Lee, J.W., Choi, B.S., Park, K.T., Lee, J.J.: Comparison between heterogeneous ant colony optimization algorithm and genetic algorithm for global path planning of mobile robot. In: Proceedings of the 2011 IEEE International Symposium on Industrial Electronics, pp. 881–886 (2011)Google Scholar
  48. 48.
    Alvarez-Sanchez, J.R., de la Paz Lopez, F., Troncoso, J.M.C., de Santos Sierra, D.: Reactive navigation in real environments using partial center of area method. Robot. Auton. Syst. 58(12), 1231–1237 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Systems EngineeringUniversity of ReadingWhiteknightsUK

Personalised recommendations