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Heuristic and metaheuristic solutions of pickup and delivery problem for self-driving taxi routing

Abstract

Self-driving car automated control problem includes automated routing problem. This paper addresses the self-driving taxi routing problem formalized as the Pickup and Delivery problem (PDP). We use small moves technique as the basis for our evolutionary computation framework that considers small moves as mutation operations. We introduce strategies, which determine vector of probabilities to apply small moves on each step. Also we introduce policies, which determine how strategies evolve during the algorithm runtime. Finally, we also apply meta-learning to select the best strategy and policy for a given dataset. We test suggested methods with several genetic schemes. The best performance is shown by strategies that apply small moves with probability depending on its success rate and/or complexity. It outperforms Hill Climbing as well as a constraint satisfaction problem solver adopted for PDP.

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Notes

  1. http://veeroute.com/.

  2. http://www.mapquest.com/.

  3. https://github.com/OSUCartography/JMapProjLib.

References

  • Ackerman M, Adolfsson A, Brownstein N (2016) An effective and efficient approach for clusterability evaluation. arXiv:160206687

  • Alur R (2015) Principles of cyber-physical systems. MIT Press, Cambridge

    Google Scholar 

  • Bender P, Tas OS, Ziegler J, Stiller C (2015) The combinatorial aspect of motion planning: maneuver variants in structured environments. In: IEEE intelligent vehicles symposium (IV). IEEE, pp 1386–1392

  • Brazdil P, Carrier CG, Soares C, Vilalta R (2008) Metalearning: applications to data mining. Springer Science & Business Media, New York

    MATH  Google Scholar 

  • Broggi A, Bombini L, Cattani S, Cerri P, Fedriga R (2010) Sensing requirements for a 13,000 km intercontinental autonomous drive. In: IEEE intelligent vehicles symposium (IV). IEEE, pp 500–505

  • Carrabs F, Cordeau JF, Laporte G (2007) Variable neighborhood search for the pickup and delivery traveling salesman problem with lifo loading. INFORMS J Comput 19(4):618–632

    MathSciNet  Article  MATH  Google Scholar 

  • Cheang B, Gao X, Lim A, Qin H, Zhu W (2012) Multiple pickup and delivery traveling salesman problem with last-in-first-out loading and distance constraints. Eur J Oper Res 223:60–75

    MathSciNet  Article  MATH  Google Scholar 

  • Chong Z, Qin B, Bandyopadhyay T, Wongpiromsarn T, Rebsamen B, Dai P, Rankin E, Ang Jr MH (2013) Autonomy for mobility on demand. In: Intelligent autonomous systems, vol 12. Springer, New York, pp 671–682

  • 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

    Article  Google Scholar 

  • Cordeau JF, Laporte G, Ropke S (2008) The vehicle routing problem: latest advances and new challenges, chap, Recent models and algorithms for one-to-one pickup and delivery problems. Springer US, pp 327–357

  • Dallegro JA (2014) How google’s self-driving car will change everything. http://www.investopedia.com/articles/investing/052014/how-googles-selfdriving-car-will-change-everything.asp. Accessed 15 Feb 2016

  • Davies A (2014) Baidu’s self-driving car has hit the road. http://www.wired.com/2015/12/baidus-self-driving-car-has-hit-the-road/. Accessed 15 Feb 2016

  • Dolgov D, Thrun S, Montemerlo M, Diebel J (2010) Path planning for autonomous vehicles in unknown semi-structured environments. Int J Robot Res 29(5):485–501

    Article  Google Scholar 

  • Efimova V, Filchenkov A, Shalyto A (2016) Reinforcement-based simultaneous algorithm and its hyperparameters selection. arXiv:161102053

  • Ercan Z, Sezer V, Heceoglu H, Dikilitas C, Gokasan M, Mugan A, Bogosyan S (2011) Multi-sensor data fusion of dcm based orientation estimation for land vehicles. In: Proceedings of the IEEE international conference on mechatronics (ICM). IEEE, pp 672–677

  • Giraud-Carrier C (2008) Metalearning—a tutorial. In: Tutorial at the 7th international conference on machine learning and applications (ICMLA), San Diego

  • Göhring D, Wang M, Schnürmacher M, Ganjineh T (2011) Radar/lidar sensor fusion for car-following on highways. In: Proceedings of the 5th international conference on automation, robotics and applications (ICARA). IEEE, pp 407–412

  • González D, Perez J, Lattarulo R, Milanés V, Nashashibi F (2014) Continuous curvature planning with obstacle avoidance capabilities in urban scenarios. In: Proceedings of the IEEE 17th international conference on intelligent transportation systems (ITSC). IEEE, pp 1430–1435

  • Hawkins AJ (2015) Google vs. uber and the race to self-driving taxis. http://www.theverge.com/2015/12/16/10309960/google-vs-uber-competition-self-driving-cars. Accessed 15 Feb 2016

  • Hosny MI (2010) Investigating heuristic and meta-heuristic algorithms for solving pickup and delivery problems. Cardiff University, Cardiff

    Google Scholar 

  • Intel (2017) Interl core i74600u. http://cpuboss.com/cpu/Intel-Core-i7-4600U. Accessed 17 June 2017

  • Iori M, Martello S (2010) Routing problems with loading constraints. Top 18:4–27

    MathSciNet  Article  MATH  Google Scholar 

  • Jafri SH, Kala R (2016) Path planning of a mobile robot in outdoor terrain. In: Intelligent systems technologies and applications. Springer, New York, pp 187–195

  • Jo K, Sunwoo M (2014) Generation of a precise roadway map for autonomous cars. IEEE Trans Intell Transp Syst 15(3):925–937

    Article  Google Scholar 

  • Jo K, Kim J, Kim D, Jang C, Sunwoo M (2014) Development of autonomous car—part I: distributed system architecture and development process. IEEE Trans Ind Electron 61(12):7131–7140

    Article  Google Scholar 

  • Kala R, Warwick K (2013) Planning autonomous vehicles in the absence of speed lanes using an elastic strip. IEEE Trans Intell Transp Syst 14(4):1743–1752

    Article  Google Scholar 

  • Kim J, Kim H, Lakshmanan K, Rajkumar RR (2013) Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car. In: Proceedings of the ACM/IEEE 4th international conference on cyber-physical systems. ACM, pp 31–40

  • Laporte G (1992) The vehicle routing problem: an overview of exact and approximate algorithms. Eur J Oper Res 59(3):345–358

    MathSciNet  Article  MATH  Google Scholar 

  • Li X, Sun Z, Kurt A, Zhu Q (2014) A sampling-based local trajectory planner for autonomous driving along a reference path. In: Intelligent vehicles symposium proceedings. IEEE, pp 376–381

  • Martínez-Barberá H, Herrero-Pérez D (2014) Multilayer distributed intelligent control of an autonomous car. Transp Res Part C Emerg Technol 39:94–112

    Article  Google Scholar 

  • Opturion (2017) Opturion cpx csp solver. http://www.opturion.com/. Accessed 17 June 2017

  • Parc CF (2014) Mobility-as-a-service: yurning transportation into a software industry. http://venturebeat.com/2014/12/13/mobility-as-a-service-turning-transportation-into-a-software-industry/. Accessed 15 Feb 2016

  • Pérez J, Milanés V, Onieva E (2011) Cascade architecture for lateral control in autonomous vehicles. IEEE Trans Intell Transp Syst 12(1):73–82

    Article  Google Scholar 

  • Pollaris H, Braekers K, Caris A, Janssens G, Limbourg S (2015) Vehicle routing problems with loading constraints: state-of-the-art and future directions. OR Spectrum 37:297–330

    MathSciNet  Article  MATH  Google Scholar 

  • Savelsbergh MW, Sol M (1995) The general pickup and delivery problem. Transp Sci 29(1):17–29

    Article  MATH  Google Scholar 

  • Schreiber M, Hellmund AM, Stiller C (2015) Multi-drive feature association for automated map generation using low-cost sensor data. In: IEEE intelligent vehicles symposium (IV). IEEE, pp 1140–1147

  • Shalamov V, Filchenkov A, Chivilikhin D (2016a) Small-moves based mutation for pick-up and delivery problem. In: Proceedings of the companion publication of the 2016 on genetic and evolutionary computation conference. ACM (in press)

  • Shalamov V, Filchenkov A, Shalyto A (2016b) Genetic search of pickup and delivery problem solutions for self-driving taxi routing. In: Proceedings of the IFIP international conference on artificial intelligence applications and innovations. Springer, New York, pp 348–355

  • Sun Q (2013) Fantail mlkit. http://fantail.quansun.com/. Accessed 14 Jan 2017

  • Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge

    MATH  Google Scholar 

  • Tanzmeister G, Friedl M, Wollherr D, Buss M (2014) Efficient evaluation of collisions and costs on grid maps for autonomous vehicle motion planning. IEEE Trans Intell Transp Syst 15(5):2249–2260

    Article  Google Scholar 

  • Taylor M (2016) Apple confirms it is working on self-driving cars. https://www.theguardian.com/technology/2016/dec/04/apple-confirms-it-is-working-on-self-driving-cars. Accessed 20 Jan 2017

  • Valencia R, Morta M, Andrade-Cetto J, Porta JM (2013) Planning reliable paths with pose slam. IEEE Trans Robot 29(4):1050–1059

    Article  Google Scholar 

  • Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341–1390

    Article  Google Scholar 

  • Wolpert DH (2000) Any two learning algorithms are (almost) exactly identical, Technical Report. NASA Ames Research Center

  • Wolpert DH, Macready WG, et al (1995) No free lunch theorems for search, Technical Report SFI-TR-95-02-010. Santa Fe Institute

  • Yuen SY, Chow CK, Zhang X (2013) Which algorithm should i choose at any point of the search: an evolutionary portfolio approach. In: Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, pp 567–574

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Acknowledgements

Authors would like to thank Ivan Smetannikov, Sergey Muravyov, Valeria Efimova and Daniil Chivilikhin for useful comments. The meta-learning based method suggested in the paper was developed under the research project financially supported by the Russian Foundation for Basic Research, Grant 16-37-60115 mol_a_dk. The reinforcement learning based method suggested in the paper was developed under the research project financially supported by the Government of the Russian Federation, Grant~074-U01. All other suggested methods and the developed framework were obtained under the research project supported by the Ministry of Education and Science of the Russian Federation, Project No 2.8866.2017/8.9.

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Correspondence to Andrey Filchenkov.

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Shalamov, V., Filchenkov, A. & Shalyto, A. Heuristic and metaheuristic solutions of pickup and delivery problem for self-driving taxi routing. Evolving Systems 10, 3–11 (2019). https://doi.org/10.1007/s12530-017-9209-5

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  • DOI: https://doi.org/10.1007/s12530-017-9209-5

Keywords

  • Self-driving car
  • Autonomous car
  • Routing
  • Pickup and delivery
  • Genetic algorithms
  • City taxi
  • Meta-learning