Skip to main content
SpringerLink
Log in

Lane-Level Route Planning for Autonomous Vehicles

  • Conference paper
  • First Online:
Algorithmic Foundations of Robotics XV (WAFR 2022)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 25))

Included in the following conference series:

Abstract

We present an algorithm that, given a representation of a road network in lane-level detail, computes a route that minimizes the expected cost to reach a given destination. In doing so, our algorithm allows us to solve for the complex trade-offs encountered when trying to decide not just which roads to follow, but also when to change between the lanes making up these roads, in order to—for example—reduce the likelihood of missing a left exit while not unnecessarily driving in the leftmost lane. This routing problem can naturally be formulated as a Markov Decision Process (MDP), in which lane change actions have stochastic outcomes. However, MDPs are known to be time-consuming to solve in general. In this paper, we show that—under reasonable assumptions—we can use a Dijkstra-like approach to solve this stochastic problem, and benefit from its efficient \(O(n \log n)\) running time. This enables an autonomous vehicle to exhibit natural lane-selection behavior as it efficiently plans an optimal route to its destination.\(^{1}\)(\(^{1}\)The contents of this paper are covered by US Patent 11,199,841 [5].)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We can use a non-strict inequality here, as long as nodes in the queue with equal \(g(x) + h(x)\) are tie-broken by their g(x) value.

References

  1. Ahmadi, K., Allan, V.H.: Stochastic path finding under congestion. In: CSCI, pp. 135–140. IEEE (2017)

    Google Scholar 

  2. Andre, D., Friedman, N., Parr, R.: Generalized prioritized sweeping. In: NIPS, pp. 1001–1007. The MIT Press (1997)

    Google Scholar 

  3. Badue, C., Guidolini, R., Carneiro, R.V., Azevedo, P., Cardoso, V.B., Forechi, A., Jesus, L.F.R., Berriel, R.F., Paixão, T.M., Mutz, F.W., de Paula Veronese, L., Oliveira-Santos, T., Souza, A.F.D.: Self-driving cars: a survey. Expert Syst. Appl. 165, 113816 (2021)

    Google Scholar 

  4. Bast, H., Delling, D., Goldberg, A.V., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., Werneck, R.F.: Route planning in transportation networks. In: Algorithm Engineering, Lecture Notes in Computer Science, vol. 9220, pp. 19–80. Springer (2016)

    Google Scholar 

  5. van den Berg, J.: Methods and systems for determination of a routing policy for an autonomous vehicle (2021), US Patent 11,199,841

    Google Scholar 

  6. Bertsekas, D.: Dynamic programming and optimal control: Volume II. Athena Scientific (2012)

    Google Scholar 

  7. Bertsekas, D.P., Tsitsiklis, J.N.: An analysis of stochastic shortest path problems. Math. Oper. Res. 16(3), 580–595 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bonet, B., Geffner, H.: Labeled RTDP: Improving the convergence of real-time dynamic programming. In: ICAPS, vol. 3, pp. 12–21 (2003)

    Google Scholar 

  9. Chen, B.: Autonomous vehicle routing at RideOS. Medium.com (2018)

    Google Scholar 

  10. Clawson, Z., Chacon, A., Vladimirsky, A.: Causal domain restriction for Eikonal equations. SIAM J. Sci. Comput. 36(5) (2014)

    Google Scholar 

  11. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press (2009)

    Google Scholar 

  12. Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of A*. J. ACM 32(3), 505–536 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  13. Delling, D., Sanders, P., Schultes, D., Wagner, D.: Engineering route planning algorithms. In: Algorithmics of Large and Complex Networks (2009)

    Google Scholar 

  14. Fan, Y., Kalaba, R., Moore, J.: Shortest paths in stochastic networks with correlated link costs. Comput. Math. with Appl. 49, 1549–1564 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  15. Felner, A., Zahavi, U., Holte, R., Schaeffer, J., Sturtevant, N., Zhang, Z.: Inconsistent heuristics in theory and practice. Artif. Intell. 175(9–10), 1570–1603 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  16. Ferguson, D., Stentz, A.: Field D*: An interpolation-based path planner and replanner. In: Robotics Research. Springer Tracts in Advanced Robotics, vol. 28, pp. 239–253. Springer (2005)

    Google Scholar 

  17. Guillot, M., Stauffer, G.: The stochastic shortest path problem: a polyhedral combinatorics perspective. Eur. J. Oper. Res. 285(1), 148–158 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hansen, E.A., Zilberstein, S.: LAO*: a heuristic search algorithm that finds solutions with loops. Artif. Intell. 129(1–2), 35–62 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  19. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  20. Jiang, K., Yang, D., Liu, C., Zhang, T., Xiao, Z.: A flexible multi-layer map model designed for lane-level route planning in autonomous vehicles. Engineering 5(2), 305–318 (2019)

    Article  Google Scholar 

  21. LaValle, S.M.: Planning Algorithms. Cambridge University Press (2006)

    Google Scholar 

  22. McMahan, H.B., Gordon, G.J.: Fast exact planning in Markov decision processes. In: ICAPS, pp. 151–160. AAAI (2005)

    Google Scholar 

  23. Paden, B., Čáp, M., Yong, S.Z., Yershov, D., Frazzoli, E.: A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1(1), 33–55 (2016)

    Article  Google Scholar 

  24. Rossi, F., Zhang, R., Hindy, Y., Pavone, M.: Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms. In: Autonomous Robots (2018)

    Google Scholar 

  25. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. 93(4), 1591–1595 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  26. Tsitsiklis, J.N.: Efficient algorithms for globally optimal trajectories. IEEE Trans. Autom. Control 40(9), 1528–1538 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  27. Vladimirsky, A.: Label-setting methods for multimode stochastic shortest path problems on graphs. Math. Oper. Res. 33(4), 821–838 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  28. Yershov, D.S., LaValle, S.M.: Simplicial Dijkstra and A* algorithms for optimal feedback planning. In: IROS, pp. 3862–3867. IEEE (2011)

    Google Scholar 

  29. Zheng, W., Thangeda, P., Savas, Y., Ornik, M.: Optimal routing in stochastic networks with reliability guarantees. In: ITSC, pp. 3521–3526 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nuro, 1300 Terra Bella Ave #200, Mountain View, CA, 94043, USA

    Mitchell Jones, Maximilian Haas-Heger & Jur van den Berg

Authors
  1. Mitchell Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maximilian Haas-Heger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jur van den Berg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jur van den Berg .

Editor information

Editors and Affiliations

  1. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Steven M. LaValle

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Jason M. O’Kane

  3. Department of Aerospace Engineering, University of Maryland, College Park, MD, USA

    Michael Otte

  4. Gates Computer Science, Stanford University, Stanford, CA, USA

    Dorsa Sadigh

  5. Department of Computer Science, University of Maryland, College Park, MD, USA

    Pratap Tokekar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jones, M., Haas-Heger, M., van den Berg, J. (2023). Lane-Level Route Planning for Autonomous Vehicles. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_19

Download citation

Publish with us

Policies and ethics

Search

Navigation