Applied Intelligence

, Volume 41, Issue 1, pp 260–281 | Cite as

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

  • Rahul Kala
  • Kevin Warwick


Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.


Autonomous vehicles Robotics Graph search Planning Multi-robot path planning Multi-robotic systems 



The authors wish to thank the Commonwealth Scholarship Commission in the United Kingdom and the British Council for their support of the first named author through the Commonwealth Scholarship and Fellowship Program (2010)—UK through award number INCS-2010-161.


  1. 1.
    Alvarez-Sanchez J, de la Paz Lopez F, Troncoso J, de Santos Sierra D (2010) Reactive navigation in real environments using partial center of area method. Robot Auton Syst 58(12):1231–1237 CrossRefGoogle Scholar
  2. 2.
    Arai T, Ota J (1992) Motion planning of multiple mobile robots. In: Proceedings of the 1992 IEEE/RSJ international conference on intelligent robots and systems, Raleigh, North Carolina, USA, pp 1761–1768 CrossRefGoogle Scholar
  3. 3.
    Bennewitz M, Burgard W, Thrun S (2002) Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots. Robot Auton Syst 41(2–3):89–99 CrossRefGoogle Scholar
  4. 4.
    Bishop R (2000) A survey of intelligent vehicle applications worldwide. In: Proceedings of the IEEE intelligent vehicles symposium, 2000. IV 2000, Oct 2000, pp 25–30 Google Scholar
  5. 5.
    Burchan Bayazit O, Lien J-M, Amato N (2002) Probabilistic roadmap motion planning for deformable objects. In: Proceedings of the IEEE international conference on robotics and automation. ICRA’02, vol 2, pp 2126–2133 Google Scholar
  6. 6.
    Chand P, Carnegie D (2012) A two-tiered global path planning strategy for limited memory mobile robots. Robot Auton Syst 60(3):309–321 CrossRefGoogle Scholar
  7. 7.
    Chang H-J, Kwak H-J, Park G-T (2009) Efficient dissemination method for traffic jams information sharing based on inter-vehicle communication. In: 2009 IEEE student conference on research and development (SCOReD), 16–18 Nov 2009, pp 61–64 CrossRefGoogle Scholar
  8. 8.
    Chu K, Lee M, Sunwoo M (2012) Local path planning for off-road autonomous driving with avoidance of static obstacles. IEEE Trans Intell Transp Syst 13(4):1599–1616 CrossRefGoogle Scholar
  9. 9.
    Cowlagi R, Tsiotras P (2012) Hierarchical motion planning with dynamical feasibility guarantees for mobile robotic vehicles. IEEE Trans Robot 28(2):379–395 CrossRefGoogle Scholar
  10. 10.
    Furda A, Vlacic L (2011) Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making. IEEE Intell Transp Syst Mag 3(1):4–17 CrossRefGoogle Scholar
  11. 11.
    Gehrig S, Stein F (2007) Collision avoidance for vehicle-following systems. IEEE Trans Intell Transp Syst 8(2):233–244 CrossRefGoogle Scholar
  12. 12.
    Kala R, Warwick K (2012) Multi-vehicle planning using RRT-connect. Paladyn 2(3):134–144 CrossRefGoogle Scholar
  13. 13.
    Kala R, Warwick K (2013) Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes. Eng Appl Artif Intell 26(5–6):1588–1601 CrossRefGoogle Scholar
  14. 14.
    Kala R, Warwick K (2013) Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization. J Intell Robot Syst. doi: 10.1007/s10846-013-9817-7 zbMATHGoogle Scholar
  15. 15.
    Kala R, Warwick K (2013) Planning autonomous vehicles in the absence of speed lanes using an elastic strip. IEEE Trans Intell Transp Syst. doi: 10.1109/TITS.2013.2266355 zbMATHGoogle Scholar
  16. 16.
    Kala R, Shukla A, Tiwari R (2011) Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness. Neurocomputing 74(14–15):2314–2335 CrossRefGoogle Scholar
  17. 17.
    Kuwata Y, Karaman S, Teo J, Frazzoli E, How J, Fiore G (2009) Real-time motion planning with applications to autonomous urban driving. IEEE Trans Control Syst Technol 17(5):1105–1118 CrossRefGoogle Scholar
  18. 18.
    Leroy S, Laumond J, Siméon T (1999) Multiple path coordination for mobile robots: a geometric algorithm. In: Proceedings of the 16th international joint conf artificial intelligence, Stockholm, Sweden, pp 1118–1123 Google Scholar
  19. 19.
    Lu Y, Huo X, Arslan O, Tsiotras P (2011) Incremental multi-scale search algorithm for dynamic path planning with low worst-case complexity. IEEE Trans Syst Man Cybern, Part B, Cybern 41(6):1556–1570 CrossRefGoogle Scholar
  20. 20.
    Lumelsky V, Harinarayan K (1997) Decentralized motion planning for multiple mobile robots: the cocktail party model. Auton Robots 4(1):121–135 CrossRefGoogle Scholar
  21. 21.
    Ma Y, Chowdhury M, Sadek A, Jeihani M (2009) Real-time highway traffic condition assessment framework using vehicle–infrastructure integration (VII) with artificial intelligence (AI). IEEE Trans Intell Transp Syst 10(4):615–627 CrossRefGoogle Scholar
  22. 22.
    McCrae J, Singh K (2009) Sketching piecewise clothoid curves. Comput Graph 33(4):452–461 CrossRefGoogle Scholar
  23. 23.
    Mohan D, Bawa P (1985) An analysis of road traffic fatalities in Delhi, India. Accid Anal Prev 17(1):33–45 CrossRefGoogle Scholar
  24. 24.
    Nilsson N (1971) Problem solving methods in artificial intelligence. McGraw-Hill, New York Google Scholar
  25. 25.
    Nutbourne A, McLellan P, Kensit R (1972) Curvature profiles for plane curves. Comput Aided Des 4(4):176–184 CrossRefGoogle Scholar
  26. 26.
    Paruchuri P, Pullalarevu A, Karlapalem K (2002) Multi agent simulation of unorganized traffic. In: Proc ACM 1st intl jt conf autonomous agents and multiagent systems: part 1, New York, pp 176–183 Google Scholar
  27. 27.
    Peng J, Akella S (2003) Coordinating the motions of multiple robots with kinodynamic constraints. In: Proc IEEE international conference on robotics & automation, Taipei, Taiwan, pp 4066–4073 Google Scholar
  28. 28.
    Peng J, Akella S (2005) Coordinating multiple robots with kinodynamic constraints along specified paths. Int J Robot Res 24(4):295–310 CrossRefGoogle Scholar
  29. 29.
    Rajamani R, Tan H-S, Law BK, Zhang W-B (2000) Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons. IEEE Trans Control Syst Technol 8(4):659–708 CrossRefGoogle Scholar
  30. 30.
    Reichardt D, Miglietta M, Moretti L, Morsink P, Schulz W (2002) CarTALK 2000: safe and comfortable driving based upon inter-vehicle-communication. In: IEEE intelligent vehicle symposium 2002, vol 2, pp 545–550. pp 17–21 CrossRefGoogle Scholar
  31. 31.
    Reveliotis SA, Roszkowska E (2011) Conflict resolution in free-ranging multivehicle systems: a resource allocation paradigm. IEEE Trans Robot 27(2):283–296 CrossRefGoogle Scholar
  32. 32.
    Russell S, Norvig P (2010) Artificial intelligence: a modern approach. Prentice Hall, New York Google Scholar
  33. 33.
    Sánchez-Ante G, Latombe J (2002) 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, Washington, DC, pp 2112–2119 Google Scholar
  34. 34.
    Schubert R (2012) Evaluating the utility of driving: toward automated decision making under uncertainty. IEEE Trans Intell Transp Syst 13(1):354–364 CrossRefGoogle Scholar
  35. 35.
    Schubert R, Schulze K, Wanielik G (2010) Situation assessment for automatic lane-change manoeuvers. IEEE Trans Intell Transp Syst 11(3):607–616 CrossRefGoogle Scholar
  36. 36.
    Svestka P, Overmars M (1995) Coordinated motion planning for multiple car-like robots using probabilistic roadmaps. In: Proceedings of the 1995 IEEE international conference on robotics and automation, Nagoya, Japan, vol 2, pp 1631–1636 CrossRefGoogle Scholar
  37. 37.
    Tsugawa S (2002) Inter-vehicle communications and their applications to intelligent vehicles: an overview. In: IEEE intelligent vehicles symposium, 17–21 June 2002, vol 2, pp 564–569 Google Scholar
  38. 38.
    Valdes F, Iglesias R, Espinosa F, Rodriguez M (2012) An efficient algorithm for optimal routing applied to convoy merging manoeuvres in urban environments. Appl Intell 37(2):267–279 CrossRefGoogle Scholar
  39. 39.
    Vanajakshi L, Subramanian S, Sivanandan R (2009) Travel time prediction under heterogeneous traffic conditions using global positioning system data from buses. IET Intell Transp Syst 3(1):1–9 CrossRefGoogle Scholar
  40. 40.
    Varaiya P (1993) Smart cars on smart roads: problems of control. IEEE Trans Autom Control 38(2):195–207 CrossRefMathSciNetGoogle Scholar
  41. 41.
    Viet H, Dang V-H, Laskar M, Chung T-C (2013) BA: an online complete coverage algorithm for cleaning robots. Appl Intell 39(2):217–235 CrossRefGoogle Scholar
  42. 42.
    Weber J, Wotawa F (2012) Diagnosis and repair of dependent failures in the control system of a mobile autonomous robot. Appl Intell 36(3):511–528 CrossRefGoogle Scholar
  43. 43.
    Zheng Y-J, Chen S-Y (2013) Cooperative particle swarm optimization for multiobjective transportation planning. Appl Intell 39(1):202–216 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Systems EngineeringUniversity of ReadingReadingUK

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