SCIENCE CHINA Information Sciences, Volume 64 , Issue 11 : 212202(2021) https://doi.org/10.1007/s11432-020-3107-7

Distributed multilane merging for connected autonomous vehicle platooning

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  • ReceivedMay 4, 2020
  • AcceptedOct 1, 2020
  • PublishedOct 20, 2021



This work was supported in part by National Key Research and Development Program of China (Grant No. 2017YFB0102503) and National Natural Science Foundation of China (Grant No. 52072243).


[1] Li D, Liu M, Zhao F. Challenges and countermeasures of interaction in autonomous vehicles. Sci China Inf Sci, 2019, 62: 50201 CrossRef Google Scholar

[2] Hubmann C, Schulz J, Becker M, et al. Automated driving in uncertain environments: Planning with interaction and uncertain maneuver prediction. IEEE Trans Intell Veh, 2018, 3: 5-17. Google Scholar

[3] Ntousakis I A, Nikolos I K, Papageorgiou M. Optimal vehicle trajectory planning in the context of cooperative merging on highways. Transpation Res Part C-Emerging Technologies, 2016, 71: 464-488 CrossRef Google Scholar

[4] Baselt D, Knorr F, Scheuermann B, et al. Merging lanes---fairness through communication. Veh Commun, 2014, 1: 97 - 104. Google Scholar

[5] Liu H, Kan X D, Shladover S E. Modeling impacts of Cooperative Adaptive Cruise Control on mixed traffic flow in multi-lane freeway facilities. Transpation Res Part C-Emerging Technologies, 2018, 95: 261-279 CrossRef Google Scholar

[6] Zheng Z. Recent developments and research needs in modeling lane changing. Transpation Res Part B-Methodological, 2014, 60: 16-32 CrossRef Google Scholar

[7] Kato S, Tsugawa S, Tokuda K. Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications. IEEE Trans Intell Transp Syst, 2002, 3: 155-161 CrossRef Google Scholar

[8] Li S E, Zheng Y, Li K. Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities. IEEE Intell Transp Syst Mag, 2017, 9: 46-58 CrossRef Google Scholar

[9] Englund C, Chen L, Ploeg J. The Grand Cooperative Driving Challenge 2016: boosting the introduction of cooperative automated vehicles. IEEE Wireless Commun, 2016, 23: 146-152 CrossRef Google Scholar

[10] Bengtsson H H, Chen L, Voronov A, et al. Interaction protocol for highway platoon merge. In: Proceedings of IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, 2015. 1971--1976. Google Scholar

[11] Semsar-Kazerooni E, Elferink K, Ploeg J. Multi-objective platoon maneuvering using artificial potential fields. IFAC-PapersOnLine, 2017, 50: 15006-15011 CrossRef Google Scholar

[12] GOLI M, ESKANDARIAN A. A systematic multi-vehicle platooning and platoon merging: Strategy, control, and trajectory generation: number 46193. 2014: V002T25A006. http://dx.doi.org/10.1115/DSCC2014-6336. Google Scholar

[13] You F, Zhang R, Lie G. Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system. Expert Syst Appl, 2015, 42: 5932-5946 CrossRef Google Scholar

[14] Zhou M, Qu X, Jin S. On the impact of cooperative autonomous vehicles in improving freeway merging: A modified intelligent driver model-based approach. IEEE Trans Intell Transp Syst, 2017, 18: 1422-1428. Google Scholar

[15] Graf Plessen M, Bernardini D, Esen H. Spatial-Based Predictive Control and Geometric Corridor Planning for Adaptive Cruise Control Coupled With Obstacle Avoidance. IEEE Trans Contr Syst Technol, 2018, 26: 38-50 CrossRef Google Scholar

[16] Li B, Zhang Y, Shao Z. Simultaneous versus joint computing: A case study of multi-vehicle parking motion planning. J Comput Sci, 2017, 20: 30-40 CrossRef Google Scholar

[17] Karagiannis G, Altintas O, Ekici E, et al. Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. Communications Surveys & Tutorials, IEEE, 2011, 13: 584-616. Google Scholar

[18] Liu J, Huang J. Leader-following consensus of linear discrete-time multi-agent systems subject to jointly connected switching networks. Sci China Inf Sci, 2018, 61: 112208 CrossRef Google Scholar

[19] Li Y, Li K, Zheng T. Evaluating the performance of vehicular platoon control under different network topologies of initial states. Physica A-Statistical Mech its Appl, 2016, 450: 359-368 CrossRef ADS Google Scholar

[20] Calzolari D, Schürmann B, Althoff M. Comparison of trajectory tracking controllers for autonomous vehicles. In: Proceedings of IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, 2017. 1--8. Google Scholar

[21] Wang L, Wang X, Hu X. Connectivity maintenance and distributed tracking for double-integrator agents with bounded potential functions. Int J Robust NOnlinear Control, 2015, 25: 542-558 CrossRef Google Scholar

[22] Olfati-Saber R. Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory. IEEE Trans Automat Contr, 2006, 51: 401-420 CrossRef Google Scholar

[23] Zheng Y, Eben Li S, Wang J. Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies. IEEE Trans Intell Transp Syst, 2016, 17: 14-26 CrossRef Google Scholar

[24] Abe M. Vehicle Handling Dynamics: Theory and Application. Oxford: Butterworth-Heinemann, 2009. Google Scholar

[25] Su H, Chen M Z Q, Lam J. Semi-Global Leader-Following Consensus of Linear Multi-Agent Systems With Input Saturation via Low Gain Feedback. IEEE Trans Circuits Syst I, 2013, 60: 1881-1889 CrossRef Google Scholar

[26] Liao F, Teo R, Wang J L. Distributed Formation and Reconfiguration Control of VTOL UAVs. IEEE Trans Contr Syst Technol, 2017, 25: 270-277 CrossRef Google Scholar

[27] Shladover S E, Nowakowski C, Lu X Y. Cooperative Adaptive Cruise Control. Transpation Res Record, 2015, 2489: 145-152 CrossRef Google Scholar

[28] Rodríguez-Seda E J, Stipanovi? D M, Spong M W. Guaranteed Collision Avoidance for Autonomous Systems with Acceleration Constraints and Sensing Uncertainties. J Optim Theor Appl, 2016, 168: 1014-1038 CrossRef Google Scholar

[29] Goli M, Eskandarian A. Evaluation of lateral trajectories with different controllers for multi-vehicle merging in platoon. In: Proceedings of International Conference on Connected Vehicles and Expo (ICCVE), 2014. 673--678. Google Scholar

  • Figure 1

    Illustration of the scenario. The following CAVs $1,2,\ldots,n$ initially located in multiple lanes are expected to achieve a single-lane platoon formation with a desired distance ${\boldsymbol~r}_i$ relative to the leader and maintain the same velocity ${\boldsymbol~v}_l$ as the leader.

  • Figure 2

    (Color online) (a) The tracking models. The gray vehicle represents the reference. (b) The block diagram of the proposed PaTFS framework for the merging and platoon formation task.

  • Figure 3

    (a) The relationship between acceleration and velocity. The vectors $a_{\rm~ref}$ and $v_{\rm~ref}$ generated by the point model are used to deduce the sideslip angle $\beta$ and turning radius $R$. (b) The geometric relationships of the sideslip angles: $\beta_f,~\beta_r,~\beta$ and the feedforward steering angle $\delta_{\rm~ff}$ that needs to be derived.

  • Figure 4

    Illustration of the forces exerted on a CAV $i$ when a velocity consensus is reached, with the assumption of a repulsive force.

  • Figure 5

    (Color online) (a) illustrates the composition of the HiL experiment platform and the signal flow. Hardware connections and the data transferred within the network are given. (b) shows the actual implemented testbed.

  • Figure 6

    (Color online) (a), (c) and (e) illustrate the trajectories of CAV 1, CAV 2 and CAV 3 during the merging and platooning operations using PT, PaT, and PaTFS respectively. In each subfigure, each of the colored lines denotes the trajectory of a particular CAV with one of the aforementioned three controllers while the gray dotted lines represent the results of other CAVs. The position error state evolutions of CAV 1, CAV 2 and CAV 3 are illustrated in (b), (d) and (f). The differences in CAV positions using PT and PaTFS are given. These values compare the actual (A) and planned (P) positions of the PaTFS with those of the PT. Deviations from PT in the merging phase were ultimately alleviated.

  • Figure 7

    (Color online) The spacing evolution of PT, PaT, and PaTFS applied to each CAV is given in the left column. (a), (c) and (e) show the spacing between CAV 1, CAV 2 and CAV 3 and their predecessors. The reference spacing is given to demonstrate convergence. The velocities of PT, PaT and PaTFS applied to each CAV are given in the right column. (b), (d) and (f) show the velocities of CAV 1, CAV 2 and CAV 3. The reference velocity is given to demonstrate convergence.

  • Figure 8

    (Color online) The lateral deviations of CAV 1 (a), CAV 2 (b), and CAV 3 (c) from the desired lane respectively. The results of the cases where PT, PaT and PaTFS are applied to each CAV are all plotted. The desired lateral position is also plotted for each CAV as the reference.

  • Figure 9

    (Color online) The absolute tracking errors at each step for all CAVs in the (a) close and (b) distant cases, respectively. Absolute values are chosen for comparison. Note that ACLT only provides trajectories for the merging CAVs 3 and 4; the comparison of the tracking errors is thus based on these two CAVs. CAVs 3 and 4 exhibit larger deviations from the trajectories for ACLT. On the other hand, a much smaller magnitude of errors is observed when PaTFS is applied.

  • Table 11  

    Table 1Table 1

    Parameter settings

  • Table 22  

    Table 2Table 2

    Vehicle parameters

  • Table 33  

    Table 3Table 3

    RMS and final values of the tracking errors for 3 states of the following CAVs using the PaTFS framework

  • Table 4  

    Table 4Lateral deviations of the CAVs from the desired positions under the PT, PaT and PaTFS models, respectively

    Scheme CAV 1 CAV 2 CAV 3
    RMS (m)PT $0.1237$0.8667 0.8115
    PaT 0.8898 0.9556 1.1209
    PaTFS 0.039210.79940.8714
    shortstackFinalerror (m)PT $-7.0732\times~10^{-3}$ $-5.5593\times~10^{-3}$ $-2.7976\times~10{-3}$
    PaT $-0.6969$ $-0.5046$ $-0.1012$
    PaTFS $4.9173\times~10^{-4}$ $3.7834\times~10^{-4}$ $1.9921\times~10^{-4}$
  • Table 54  

    Table 5Table 4

    Parameter settings for scenario 2

  • Table 65  

    Table 6Table 5

    Peak longitudinal and lateral acceleration of the CAVs


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