Analysing driving efficiency of mandatory lane change decision for autonomous vehicles

Analysing driving efficiency of mandatory lane change decision for autonomous vehicles

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Mandatory lane change (MLC) is a critical step in formulating global route of an autonomous vehicle on the urban road network. Improper MLC decisions on arterial roads could jeopardise efficiency (travel cost) and reliability (possibility of failed lane change). However, the existing research studies seldom investigate the optimal MLC decision at the planning level to maximise the reliability-based driving efficiency. This research aims at addressing two core strategic decision variables (MLC decision point on the road and maximum waiting time before giving up MLC). A series of simulation experiments for various scenarios are conducted for an arterial road to reveal their relationship. The results indicate that both identified decision variables inherently affect travel time spent on this road and the rate of failed MLCs, and a trade-off exists between arterial travel time and the rate of failed MLCs. Based on the simulation analysis, an analytical lane-level link performance (LLP) function is formulated to assess the impacts of MLC decisions on driving efficiency. The analysis validates that the optimal MLC strategic decision (MLC decision position and maximum waiting time) can be determined by maximising LLP function. It is promising to apply the proposed LLP function in lane-level routing algorithm in the future.


    1. 1)
      • 1. Litman, T.: ‘Autonomous vehicle implementation predictions’, Victoria Transport Policy Institute, 28, 2014. Available at [accessed on 15 April 2017].
    2. 2)
      • 2. National Highway Traffic Safety Administration.: ‘Preliminary Statement of Policy Concerning Automated Vehicles. National Highway Traffic Safety Administration (NHTSA 14-13)’, Washington DC, 2013.
    3. 3)
      • 3. J3016: ‘SAE international. Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. Society for automotive engineers international’, 2014.
    4. 4)
      • 4. Varaiya, P.: ‘Smart cars on smart roads problems of control’, IEEE Trans. Autom. Control, 1993, 38, (2), pp. 195207.
    5. 5)
      • 5. Sawant, N.R.: ‘Longitudinal vehicle speed controller for autonomous driving in urban stop-and-go traffic situations’, PhD thesis, The Ohio State University, 2010.
    6. 6)
      • 6. Nie, L., Guan, J., Lu, C., et al: ‘Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network’, IET Intell. Transp. Syst., 2018, 12, (6), pp. 485494.
    7. 7)
      • 7. Corona, D., De Schutter, B.: ‘Adaptive cruise control for a smart car: a comparison benchmark for MPC-PWA control methods’, IEEE Trans. Control Syst. Technol., 2008, 16, (2), pp. 365372.
    8. 8)
      • 8. Cai, L., Rad, A.B., Chan, W.L.: ‘An intelligent longitudinal controller for application in semiautonomous vehicles’, IEEE Trans. Ind. Electron., 2010, 57, (4), pp. 14871497.
    9. 9)
      • 9. Le Vine, S., Liu, X., Zheng, F., et al: ‘Automated cars: queue discharge at signalized intersections with ‘assured-clear-distance-ahead’ driving strategies’, Transp. Res. C, 2016, 62, pp. 3554.
    10. 10)
      • 10. Nilsson, J., Brännström, M., Coelingh, E., et al: ‘Longitudinal and lateral control for automated lane change maneuvers’. American Control Conf., Chicago, IL, USA, 2015, pp. 13991404.
    11. 11)
      • 11. Dang, R., Wang, J., Li, S., et al: ‘Coordinated adaptive cruise control system with lane-change assistance’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (5), pp. 23732383.
    12. 12)
      • 12. Hatipoglu, C., Ozguner, U., Redmill, K.A.: ‘Automated lane change controller design’, IEEE Trans. Intell. Transp. Syst., 2003, 4, (1), pp. 1322.
    13. 13)
      • 13. Horowitz, R., Tan, C.W., Sun, X.: ‘An efficient lane change maneuver for platoons of vehicles in an automated highway system’. ASME Int. Mechanical Engineering Congress and Exposition: Institute of Transportation Studies, UC Berkeley., 2003, pp. 355362.
    14. 14)
      • 14. Lin, C.F., Juang, J.C., Li, K.R.: ‘Active collision avoidance system for steering control of autonomous vehicles’, IET Intell. Transp. Syst., 2014, 8, (6), pp. 550557.
    15. 15)
      • 15. Naranjo, J.E., Gonzalez, C., Garcia, R., et al: ‘Lane-change fuzzy control in autonomous vehicles for the overtaking maneuver’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (3), pp. 438450.
    16. 16)
      • 16. Ma, L., Yang, J., Zhang, M.: ‘A two-level path planning method for on-road autonomous driving’. The2nd Int. Conf. on Intelligent System Design and Engineering Application, Sanya, China, 2012, pp. 661664.
    17. 17)
      • 17. Long, J., Gao, Z., Zhao, X., et al: ‘Urban traffic jam simulation based on the cell transmission model’, Netw. Spat. Econ., 2011, 11, (11), pp. 4364.
    18. 18)
      • 18. Qi, H., Wang, D., Chen, P., et al: ‘Location-dependent lane-changing behavior for arterial road traffic’, Netw. Spat Econ., 2014, 14, (1), pp. 6789.
    19. 19)
      • 19. Xie, K., Ozbay, K., Yang, H.: ‘The heterogeneity of capacity distributions among different freeway lanes’. Symp. on Celebrating 50 Years of Traffic Flow Theory., Portlan, OR, USA, 2014, pp. 161176.
    20. 20)
      • 20. Pompigna, A., Rupi, F.: ‘Capacity and lane distributional effects on a three-lane carriageway section: a case study on the Italian freeway network’. Sidt Scientific Seminar, 2015.
    21. 21)
      • 21. Chen, P., Qi, H., Sun, J.: ‘Investigation of saturation flow on shared right-turn lane at signalized intersections’, J. Transp. Res. Board, Transp. Res. Record., 2014, 2461, pp. 6675.
    22. 22)
      • 22. Lee, J.W., Yoon, C.R., Kang, J., et al: ‘Development of lane-level guidance service in vehicle augmented reality system’. The 17th Int. Conf. on Advanced Communication Technology (ICACT), Seoul, South Korea, 2015, pp. 263266.
    23. 23)
      • 23. Bétaille, D., Toledo-Moreo, R.: ‘Creating enhanced maps for lane-level vehicle navigation’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (4), pp. 786798.
    24. 24)
      • 24. Rabe, J., Necker, M., Stiller, C.: ‘Ego-lane estimation for lane-level navigation in urban scenarios’. 2016 IEEE Intelligent Vehicles Symp. (IV), Gothenburg, Sweden, 2016, pp. 896901.
    25. 25)
      • 25. Lai, A.H.S., Yung, N.H.C.: ‘Lane detection by orientation and length discrimination’, IEEE Trans. Syst. Man Cybern. B, 2000, 30, (4), pp. 539548.
    26. 26)
      • 26. Cheng, H.Y., Jeng, B.S., Tseng, P.T., et al: ‘Lane detection with moving vehicles in the traffic scenes’, IEEE Trans. Intell. Transp. Syst., 2007, 7, (4), pp. 571582.
    27. 27)
      • 27. Du, J., Barth, M.J.: ‘Next-generation automated vehicle location systems: positioning at the lane level’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (1), pp. 4857.
    28. 28)
      • 28. Alam, N., Balaei, A.T., Dempster, A.G.: ‘An instantaneous lane-level positioning using DSRC carrier frequency offset’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (4), pp. 15661575.
    29. 29)
      • 29. Song, T., Capurso, N., Cheng, X., et al: ‘Enhancing GPS with lane-level navigation to facilitate highway driving’, IEEE Trans. Veh. Technol., 2017, 66, (6), pp. 45794591.
    30. 30)
      • 30. Gong, S., Du, L.: ‘Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp’, Transp. Res. B, Methodol., 2016, 84, pp. 130.
    31. 31)
      • 31. Liu, C., Jiang, K., Xiao, Z., et al: ‘Lane-level route planning based on a multi-layer map model’. 2017 IEEE 20th Int. Conf. on Intelligent Transport Syst., Yokohama, Japan, October 2017, pp. 17.
    32. 32)
      • 32. Slavin, H., Yang, Q., Morgan, D., et al: ‘Lane-level vehicle navigation for vehicle routing and traffic management’, U.S. Patent 9,964,414, May 2018.
    33. 33)
      • 33. Cao, P., Hu, Y., Miwa, T., et al: ‘An optimal mandatory lane change decision model for autonomous vehicles in urban arterials’, J. Intell. Transport. Syst., 2017, 21, (4), pp. 271284.
    34. 34)
      • 34. Sun, D.J., Elefteriadou, L.: ‘Lane-changing behavior on urban streets: an ‘in-vehicle’ field experiment-based study’, Comput.-Aided Civil Infrastruct. Eng., 2012, 27, (7), pp. 525542.
    35. 35)
      • 35. Sun, D., Elefteriadou, L.: ‘A driver behavior-based lane-changing model for urban arterial streets’, Transp. Sci., 2012, 48, (2), pp. 184205.
    36. 36)
      • 36. Lighthill, M.J., Whitham, G.B.: ‘On kinematic waves. ii. a theory of traffic flow on long crowded roads’. Proc. Royal Society of London A: Mathematical, Physical and Engineering Sciences, London, U.K., 1955, Vol. 229, (1178), pp. 317345.
    37. 37)
      • 37. Greenshields, B. D., Channing, W., Miller, H.: ‘A study of traffic capacity’. Highway Research Board Proc., National Research Council (USA), Highway Research Board, Washington, DC, USA, 1935.
    38. 38)
      • 38. HCM2010.: Transportation Research Board, National Research Council, Washington, DC, 2010.
    39. 39)
      • 39. Gurupackiam, S., Jones, S., Turner, D.: ‘Characterization of arterial traffic congestion through analysis of operational parameters (gap acceptance and lane changing)’. UTCA Report Number 07112, University Transportation Center for Alabama, May 2010.
    40. 40)
      • 40. Bojarski, M., Del Testa, D., Dworakowski, D., et al: ‘End to end learning for self-driving cars’, arXiv preprint arXiv:1604.07316, 2016.
    41. 41)
      • 41. Zhu, M., Wang, X., Wang, Y.: ‘Human-like autonomous car-following planning by deep reinforcement learning’. Transportation Research Board 97th Annual Meeting, Washington DC, United States, January 2018.
    42. 42)
      • 42. Gu, Y., Hashimoto, Y., Hsu, L.T., et al: ‘Human-like motion planning model for driving in signalized intersections’. IATSS Res., 2017, 41, (3), pp. 129139.

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