Junxiang Li, Bin Dai, Xiaohui Li, Ruili Wang, Xin Xu, Bohan Jiang, and Yi Di


  1. [1] A. Geiger, P. Lenz, and R. Urtasun, Are we ready for autonomous driving? The Kitti vision benchmark suite, in 2012IEEE Conf. on Computer Vision and Pattern Recognition(CVPR), IEEE, Washington, D.C, USA, 2012, 3354–3361.
  2. [2] A. Broggi, M. Buzzoni, S. Debattisti, P. Grisleri, M.C. Laghi,P. Medici, and P. Versari, Extensive tests of autonomous drivingtechnologies, IEEE Transactions on Intelligent TransportationSystems, 14(3), 2013, 1403–1415.
  3. [3] F. Codevilla, M. Miiller, A. L´opez, V. Koltun, and A. Dosovitskiy, End-to-end driving via conditional imitation learning, in2018 IEEE Int. Conf. on Robotics and Automation (ICRA),IEEE, Brisbane, Queensland, Australia, 2018, 1–9.
  4. [4] T.M. Howard, C.J. Green, A. Kelly, and D. Ferguson, Statespace sampling of feasible motions for high performance mobilerobot navigation in complex environments, Journal of FieldRobotics, 25(6–7), 2008, 325–345.
  5. [5] R. Kala and K. Warwick, Motion planning of autonomousvehicles on a dual carriageway without speed lanes, Electronics,4(1), 2015, 59–81.
  6. [6] X. You, S. Liu, and C. Zhang, An improved ant colony systemalgorithm for robot path planning and performance analysis,International Journal of Robotics and Automation, 33(5), 2018.
  7. [7] C. Lin, H. Wang, J. Yuan, and M. Fu, An online path planningmethod based on hybrid quantum ant colony optimizationfor AUV, International Journal of Robotics and Automation,33(4), 2018.
  8. [8] L. Yi, M. Cong, H. Dong, and D. Liu, Reinforcement learningand ega-based trajectory planning for dual robots, International Journal of Robotics and Automation, 33(4), 2018.
  9. [9] S. Lef`evre, D. Vasquez, and C. Laugier, A survey on motion prediction and risk assessment for intelligent vehicles,ROBOMECH Journal, 1(1), 1, 2014.
  10. [10] X. Li, Z. Sun, D. Cao, D. Liu, and H. He, Developmentof a new integrated local trajectory planning and trackingcontrol framework for autonomous ground vehicles, MechanicalSystems and Signal Processing, 87(Part B), 2015, 118–137.
  11. [11] D. Gonzalez, J. Perez, V. Milanes, and F. Nashashibi, A reviewof motion planning techniques for automated vehicles, IEEETransactions on Intelligent Transportation Systems, 17(4),2016, 1135–1145.
  12. [12] M. Pivtoraiko, R.A. Knepper, and A. Kelly, Differentiallyconstrained mobile robot motion planning in state lattices,Journal of Field Robotics, 26(3), 2009, 308–333.
  13. [13] M. Likhachev and D. Ferguson, Planning long dynamically feasible maneuvers for autonomous vehicles, International Journalof Robotics Research, 28(8), 2009, 933–945.
  14. [14] D. Fassbender, A. Mueller, and H.-J. Wuensche, Trajectoryplanning for car-like robots in unknown, unstructured environments, in 2014 IEEE/RSJ Int. Conf. on Intelligent Robotsand Systems, IEEE, Chicago, Illinois, USA, 2014, 3630–3635.
  15. [15] A.J. Rimmer and D. Cebon, Planning collision-free trajectoriesfor reversing multiply-articulated vehicles, IEEE Transactionson Intelligent Transportation Systems, 17, 2016, 1998–2007.
  16. [16] R. Cui, Y. Li, and W. Yan, Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT, IEEE Transactions on Systems, Man, andCybernetics: Systems, 46(7), 2016, 993–1004.
  17. [17] M. Du, T. Mei, H. Liang, J. Chen, R. Huang, and P. Zhao,Drivers’ visual behavior-guided RRT motion planner for autonomous on-road driving, Sensors (Switzerland), 16(1), 2016,1–19.
  18. [18] B. Hao and Z. Yan, Recovery path planning for an agriculturalmobile robot by Dubins-RRT algorithm, International Journalof Robotics and Automation, 33(2), 2018.
  19. [19] F. von Hundelshausen, M. Himmelsbach, F. Hecker, A. Mueller,and H.-J. Wuensche, Driving with tentacles: integral structuresfor sensing and motion, Journal of Field Robotics, 25(9), 2008,640–673.
  20. [20] S.M. Erlien, S. Fujita, and J.C. Gerdes, Shared steering controlusing safe envelopes for obstacle avoidance and vehicle stability,IEEE Transactions on Intelligent Transportation Systems,17(2), 2016, 441–451.
  21. [21] R. Zhao and D. Sidobre, On-line trajectory generation considering kinematic motion constraints for robot manipulators,International Journal of Robotics and Automation, 33(6), 2018.
  22. [22] A. Kelly and B. Nagy, Reactive nonholonomic trajectorygeneration via parametric optimal control, The InternationalJournal of Robotics Research, 22(7–8), 2003, 583–601.
  23. [23] T.M. Howard and A. Kelly, Optimal rough terrain trajectory generation for wheeled mobile robots, The InternationalJournal of Robotics Research, 26(2), 2007, 141–166.
  24. [24] B. Nagy and A. Kelly, Trajectory generation for car-likerobots using cubic curvature polynomials, 6th InternationalConference on Field and Service Robots, Helsinki, Finland, 11,2001.
  25. [25] M. Matthew, Parallel algorithms for real-time motion planning.Ph.D. Thesis, Carnegie Mellon University, Pittsburgh, PA,USA, 2011.
  26. [26] M. Werling, J. Ziegler, S. Kammel, and S. Thrun, Optimaltrajectory generation for dynamic street scenarios in a Frenetframe, in 2010 IEEE Int. Conf. on Robotics and Automation(ICRA), IEEE, Anchorage, Alaska, 2010, 987–993.
  27. [27] T. Gu and J.M. Dolan, On-road motion planning for autonomous vehicles, in Int. Conf. on Intelligent Robotics andApplications, Springer, 2012, 588–597.
  28. [28] T. Gu, J.M. Dolan, and J. Lee, On-road trajectory planningfor general autonomous driving with enhanced tunability, inIntelligent Autonomous Systems, 13, Springer, 2016, 247–261.
  29. [29] T. Gu, J.M. Dolan, and J. Lee, On-road trajectory planningfor general autonomous driving with enhanced tunability, inIntelligent Autonomous Systems 13, Springer, 2014, 247–261.
  30. [30] K. Kawabata, A trajectory generation method for mobile robotbased on iterative extension-like process, Artificial Life andRobotics, 21(4), 2016, 500–509.
  31. [31] L. Zhang, L. Sun, S. Zhang, and J. Liu, Trajectory planningfor an indoor mobile robot using quintic B´ezier curves, in2015 IEEE Int. Conf. on Robotics and Biomimetics (ROBIO),IEEE, Zhuhai, China, 2015, 757–762.
  32. [32] K.R. Simba, N. Uchiyama, and S. Sano, Real-time smooth trajectory generation for nonholonomic mobile robots using Beziercurves, Robotics and Computer-Integrated Manufacturing, 41,2016, 31–42.
  33. [33] X. Li, Z. Sun, D. Cao, Z. He, and Q. Zhu, Real-time trajectory planning for autonomous urban driving: framework,algorithms, and verifications, IEEE/ASME Transactions onMechatronics, 21(2), 2016, 740–753.
  34. [34] J. Li, B. Dai, X. Li, C. Li, and Y. Di, A real-time and predictivetrajectory-generation motion planner for autonomous groundvehicles, in 2017 9th Int. Conf. onIntelligent Human-MachineSystems and Cybernetics (IHMSC), 2, IEEE, Hangzhou, China,2017, 108–113.
  35. [35] C. Fulgenzi, A. Spalanzani, and C. Laugier, Dynamic obstacleavoidance in uncertain environment combining PVOs andoccupancy grid, in 2007 IEEE International Conference onRobotics and Automation, Roma, Italy, IEEE, 2007, 1610–1616.
  36. [36] M. Br¨annstr¨om, E. Coelingh, and J. Sj¨oberg, Model-basedthreat assessment for avoiding arbitrary vehicle collisions,11(3), 2010, 658–669.
  37. [37] G. Xie, H. Gao, L. Qian, B. Huang, K. Li, and J. Wang, Vehicletrajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models, IEEETransactions on Industrial Electronics, 65(7), (2018), 5999–6008.
  38. [38] W. Xu, J. Pan, J. Wei, and J.M. Dolan, Motion planning underuncertainty for on-road autonomous driving, in 2014 IEEE Int.Conf. on Robotics & Automation(ICRA), Hong Kong, China,2014, 2507–2512.
  39. [39] J. Schlechtriemen, A. Wedel, J. Hillenbrand, G. Breuel, andK.-d. Kuhnert, A lane change detection approach using featureranking with maximized predictive power, in 2014 IEEE Intelligent Vehicles Symposium (IV), Ypsilanti, Michigan, USA,June 8–11, 2014, 108–114.
  40. [40] H. Woo, Y. Ji, H. Kono, Y. Tamura, Y. Kuroda,T. Sugano, Y. Yamamoto, A. Yamashita, and H. Asama,Dynamic potential-model-based feature for lane change prediction, 2016 IEEE Int. Conf. on Systems, Man, and Cybernetics,SMC 2016, Budapest, Hungary, 2017, 838–843.
  41. [41] D. Kasper, G. Weidl, T. Dang, G. Breuel, A. Tamke, A. Wedel,and W. Rosenstiel, Object-oriented Bayesian networks fordetection of lane change maneuvers, Intelligent TransportationSystem Magazine, 4(1), 2012, 673–678.
  42. [42] M. Liebner, M. Baumann, F. Klanner, and C. Stiller, Driverintent inference at urban intersections using the intelligentdriver model, IEEE Intelligent Vehicles Symposium, Madrid,Spain, 2012, 1162–1167.
  43. [43] S.B. Amsalu, A. Homaifar, A. Karimoddini, and A. Kurt,Driver intention estimation via discrete hidden Markov model,in 2017 IEEE Int. Conf. on Systems, Man, and Cybernetics(SMC), Banff, Canada, October 2017, 2712–2717.
  44. [44] D.J. Phillips, T.A. Wheeler, and M.J. Kochenderfer, Generalizable intention prediction of human drivers at intersections,IEEE Intelligent Vehicles Symposium (IV), California, USA,2017, 1665–1670.
  45. [45] D. Lee, Y.P. Kwon, S. McMains, and J.K. Hedrick, Convolution neural network-based lane change intention prediction ofsurrounding vehicles for ACC, 2017 IEEE 20th InternationalConference on Intelligent Transportation Systems (ITSC),Yokohama, Japan, 2017, 1–6.
  46. [46] T. Gindele, S. Brechtel, and R. Dillmann, A probabilistic modelfor estimating driver behaviors and vehicle trajectories in trafficenvironments, in IEEE Conf. on Intelligent TransportationSystems (ITSC), Madeira Island, Portugal, 2010, 1625–1631.
  47. [47] M. Schreier, V. Willert, and J. Adamy, An integrated approachto maneuver-based trajectory prediction and criticality assessment in arbitrary road environments, IEEE Transactions onIntelligent Transportation Systems, 17(10), 2016, 2751–2766.
  48. [48] M. Bahram, C. Hubmann, A. Lawitzky, M. Aeberhard, andD. Wollherr, A combined model- and learning-based frameworkfor interaction-aware maneuver prediction, IEEE Transactionson Intelligent Transportation Systems, 17(6), 2016, 1538–1550.
  49. [49] M. Bahram, A. Wolf, M. Aeberhard, and D. Wollherr, Aprediction-based reactive driving strategy for highly automateddriving function on freeways, in IEEE Intelligent VehiclesSymposium (IV), Michigan, USA, 2014, 400–406.
  50. [50] S. Brechtel, T. Gindele, and R. Dillmann, Probabilisticdecision-making under uncertainty for autonomous driving using continuous pomdps, in 17th Int. IEEE Conf. on Intelli-gent Transportation Systems (ITSC), Qingdao, China, 2014,392–399.
  51. [51] E. Galceran, A.G. Cunningham, R.M. Eustice, and E. Olson,Multipolicy decision-making for autonomous driving viachangepoint-based behavior prediction: Theory and experiment, Autonomous Robots, 41(6), 2007, 1367–1382.
  52. [52] A.G. Cunningham, E. Galceran, R.M. Eustice, andE. Olson, MPDM: Multipolicy decision-making in dynamic,uncertain environments for autonomous driving, IEEE Conf.on Robotics and Automation (ICRA), Washington, USA, 2015,1670–1677.
  53. [53] T. Lee and Y.J. Kim, Massively parallel motion planningalgorithms under uncertainty using POMDP, InternationalJournal of Robotics Research, 35(8), 2016, 928–942.
  54. [54] M. Bahram, A. Lawitzky, J. Friedrichs, M. Aeberhard, andD. Wollherr, A game-theoretic approach to replanning-awareinteractive scene prediction and planning, IEEE Transactionson Vehicular Technology, 65(6), 2016, 3981–3992.
  55. [55] D. Lenz, T. Kessler, and A. Knoll, Tactical cooperative planningfor autonomous vehicles using MCTS, IEEE Intelligent VehiclesSymposium (IV), Gothenburg, Sweden, 2016, 1–7.
  56. [56] F. Damerow and J. Eggert, Risk-aversive behavior planningunder multiple situations with uncertainty, 18th IEEE Conf. onIntelligent Transportation Systems, (ITSC), Canary Islands,Spain, September 2015, 656–663.
  57. [57] J. Wei, J.M. Dolan, and B. Litkouhi, A prediction-and costfunction-based algorithm for robust autonomous freeway driving, in 2010 IEEE Intelligent Vehicles Symposium (IV), IEEE,San Diego, CA, USA, 2010, 512–517.
  58. [58] J. Wei, J.M. Dolan, and B. Litkouhi, Autonomous vehiclesocial behavior for highway entrance ramp management, IEEEIntelligent Vehicles Symposium (IV), Gold Coast, Australia,201–207, 2013.
  59. [59] J. Li, B. Dai, X. Li, X. Xu, and D. Liu, A dynamic Bayesiannetwork for vehicle maneuver prediction in highway drivingscenarios: Framework and verification, Electronics, 8(1), 2019,40.
  60. [60] S. Sarkka, On unscented Kalman filtering for state estimationof continuous-time nonlinear systems, IEEE Transactions onAutomatic Control,, 52(9), 2007, 1631–1641.
  61. [61] T.M. Howard and A. Kelly, Optimal rough terrain trajectorygeneration for wheeled mobile robots, International Journal ofRobotics Research, 26(2), 2007, 141–166.
  62. [62] D. Ferguson, T.M. Howard, and M. Likhachev, Motion planningin urban environments, Journal of Field Robotics, 25(11–12),2008, 939–960.
  63. [63] R.A. Knepper and M.T. Mason, Real-time informed path sampling for motion planning search real-time informed path sampling for motion, International Journal of Robotics Research,31(11), 2012, 1231–1250.
  64. [64] R.A. Knepper, S.S. Srinivasa, and M.T. Mason, Toward adeeper understanding of motion alternatives via an equivalencerelation on local paths, International Journal of RoboticsResearch, 31(2), 2012, 167–186.
  65. [65] J. Ziegler, P. Bender, T. Dang, and C. Stiller, Trajectoryplanning for Bertha—A local, continuous method, IEEE Intelligent Vehicles Symposium, Michigan, USA, Vol. 35, April,2014, 450–457.

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