This paper presents a construction process of the three-dimensional roadway geometry map for an autonomous driving system. The presented process focuses on the post-processing and the three-dimensional roadway geometry modelling algorithms. The post-processing algorithm refines the raw Global Positioning System position data by combining the novel three-dimensional roadway geometry model which originated from the construction engineering of roadways and vehicle motion data from onboard sensors using a Rauch–Tung–Striebel smoother. The three-dimensional geometry modelling algorithm approximates the road geometry information described in three-dimensional point clouds into a mathematical curve model based on the B-spline. An adaptive curve refinement method using dominant points was applied to the road modelling algorithm. This dominant-point-based refinement method can reduce the number of knots and the number of control points of the B-spline road model while maintaining the desired accuracy of the roadway map. Also, since the dominant-point refinement method considers a road shape factor, such as the curvature and the arc length, for the road modelling, it is more efficient than the previous B-spline road modelling algorithms. The proposed map generation algorithm was verified and evaluated through experiments in various test conditions. The experimental results show that the presented construction process of the three-dimensional roadway geometry map can provide sufficient accuracy, reliability and efficiency for applications of autonomous driving systems in comparison with those of other roadway map construction processes.
UrmsonCAnhaltJBagnellDet al. Autonomous driving in urban environments: Boss and the Urban Challenge. J Field Robotics2008; 25: 425–466.
2.
SchreiberMKnoppelCFrankeU.LaneLoc: lane marking based localization using highly accurate maps. In: 2013 IEEE intelligent vehicles symposium, Gold Coast, Queensland, Australia, 23–26 June 2013, pp. 449–454. New York: IEEE.
3.
JoKSunwooM.Generation of a precise roadway map for autonomous cars. IEEE Trans Intell Transpn Systems2014; 15: 925–937.
4.
ParkHLeeJH.B-spline curve fitting based on adaptive curve refinement using dominant points. CAD Comput Aided Des2007; 39: 439–451.
5.
BetailleDToledo-MoreoR.Creating enhanced maps for lane-level vehicle navigation. IEEE IEEE Trans Intell Transpn Systems2010; 11: 786–798.
6.
SchroedlSWagstaffKRogersSet al. Mining GPS traces for map refinement. Data Mining Knowledge Discovery2004; 9: 59–87.
7.
RogersS.Creating and evaluating highly accurate maps with probe vehicles. In: IEEE conference on intelligent transportation systems, Dearborn, Michigan, USA, 1–3 October 2000, pp. 125–130. New York: IEEE.
8.
Ben-AriehDChangSRysMZhangG.Geometric modeling of highways using global positioning system data and B-spline approximation. J Transpn Engng2004; 130: 632–636.
9.
ChenARamanandanAFarrellJA. High-precision lane-level road map building for vehicle navigation. In: 2010 IEEE/ION position location and navigation symposium, Indian Wells, California, USA, 4–6 May 2010, pp. 1035–1042. New York: IEEE.
10.
CastroMIglesiasLRodríguez-SolanoRSánchezJA.Geometric modelling of highways using global positioning system (GPS) data and spline approximation. Transpn Res Part C: Emerging Technol2006; 14: 233–243.
11.
WedelABadinoHRabeCet al. B-spline modeling of road surfaces with an application to free-space estimation. IEEE Trans Intell Transpn Systems2009; 10: 572–583.
12.
TaoGIwamuraKKogaM.Towards high accuracy road maps generation from massive GPS traces data. In: International 2007 IEEE geoscience and remote sensing symposium, Barcelona, Spain, 23–28 July 2007, pp. 667–670. New York: IEEE.
13.
SchindlerAMaierGJandaF. Generation of high precision digital maps using circular arc splines. In: 2012 IEEE intelligent vehicles symposium, Alcala de Henares, Spain, 3–7 June 2012, pp. 246–251. New York: IEEE.
14.
SchindlerAMaierGPangerlS. Exploiting arc splines for digital maps. In: 2011 14th international IEEE conference on intelligent transportation systems, Washington, DC, USA, 5–7 October 2011, pp. 1–6. New York: IEEE.
15.
SeoY-WUrmsonCWettergreenD. Exploiting publicly available cartographic resources for aerial image analysis. In: 20th international conference on advances in geographic information systems, Redondo Beach, CA, USA, 6–9 November 2012, pp. 109–118. New York: ACM.
16.
PinkO.Visual map matching and localization using a global feature map. In: 2008 IEEE computer society conference on computer vision and pattern recognition workshops, Anchorage, Alaska, USA, 2008, pp. 1–7. New York: IEEE.
17.
KoutakiGUchimuraKZhenchengH. Network active shape model for updating road map from aerial images. In: 2006 IEEE intelligent vehicles symposium, Tokyo, Japan, 13–15 June 2006, pp. 325–330. New York: IEEE.
18.
BetailleDToledo-MoreoRLaneuritJ. Making an enhanced map for lane location based services. In: 2008 11th international IEEE conference on intelligent transportation systems Beijing, People’s Republic of China, 12–15 October 2008, pp. 711–716. New York: IEEE.
19.
BétailleD.GYROLIS: logiciel de localisation de véhicule en post-traitement par couplage GPS–gyromètre–odomètre
(GYROLIS: post-processing of vehicle localization software via GPS–gyrometer–odometer coupling). Bull Lab Ponts Chaussées2008; 272: 75–87.
20.
LiXChenWChanC.A reliable multisensor fusion strategy for land vehicle positioning using low-cost sensors. Proc IMechE Part D: J Automobile Engineering2014: 228(12): 1375–1397.
21.
YangYGuZHuL.Research on the information fusion method of the Global Positioning System–dead reckoning vehicle integrated navigation system. Proc IMechE Part D: J Automobile Engineering2007; 221(5): 543–553.
22.
GuoLZhangQ.A low-cost integrated positioning system for autonomous off-highway vehicles. Proc IMechE Part D: J Automobile Engineering2008; 222(11): 1997–2009.
23.
GuoCKidonoKMeguroJet al. A low-cost solution for automatic lane-level map generation using conventional in-car sensors. IEEE Trans Intell Transpn Systems2016; 17: 2355–2366.
24.
SimonD.Optimal state estimation: Kalman, H∞ and nonlinear approaches. New York: John Wiley, 2006.
25.
AgamennoniGNietoJINebotEM.Robust inference of principal road paths for intelligent transportation systems. IEEE Trans Intell Transpn Systems2011; 12: 298–308.
26.
AgamennoniGNietoJINebotEM.Robust and accurate road map inference. In: 2010 IEEE international conference on robotics and automation, Anchorage, Alaska, USA, 3–7 May 2010, pp. 3946–3953. New York: IEEE.
27.
WenhuanSShuhanSYuncaiL. Automatic generation of road network map from massive GPS, vehicle trajectories. In: 2009 12th international IEEE conference on intelligent transportation systems, St Louis, Missouri, USA, 4–7 October, 2009, pp. 1–6. New York: IEEE.
28.
FathiAKrummJ. Detecting road intersections from GPS traces. In: 6th international conference on geographic information science, Zurich, Switzerland, 14–17 September 2010, Lecture Notes in Computer Science, Vol 6292, pp. 56–69. Berlin: Springer.
29.
WangHKearneyJAtkinsonK. Arc-length parameterized spline curves for real-time simulation. In: Curve and surface design, 5th international conference on curves and surfaces, Saint-Malo, France, 27 June–3 July 2002, pp. 387–396. Brentwood, Tennessee: Nashboro Press, 2003.
30.
WangHKearneyJAtkinsonK. Robust and efficient computation of the closest point on a spline curve. In: Curve and surface design, 5th international conference on curves and surfaces, Saint-Malo, France, 27 June–3 July 2002, pp. 397–406. Brentwood, Tennessee: Nashboro Press, 2003.
31.
LooseHFrankeU. B-spline-based road model for 3D lane recognition. In: 2010 13th international IEEE conference on intelligent transportation systems, Funchal, Madeira, Portugal, 19–22 September 2010, pp. 91–98. New York: IEEE.
32.
GoldenthalRBercovierM.Spline curve approximation and design by optimal control over the knots. Computing2004; 72: 53–64.
RahmigCKlugeA.Digital maps for railway applications based on OpenStreetMap data. In: 16th international IEEE conference on intelligent transportation systems, The Hague, The Netherlands, 6–9 October 2013, pp. 1322–1327. New York: IEEE.
35.
AlinAFritschJButzMV.Improved tracking and behavior anticipation by combining street map information with Bayesian-filtering. In: 16th international IEEE conference on intelligent transportation systems, The Hague, The Netherlands, 6–9 October 2013, pp. 2235–2242. New York: IEEE.
36.
JoKChuKKimJSunwooM. Distributed vehicle state estimation system using information fusion of GPS and in-vehicle sensors for vehicle localization. In: 2011 14th international IEEE conference on intelligent transportation systems, Washington, DC, USA, 5–7 October 2011, pp. 2009–2014. New York: IEEE.
37.
JoKChuKLeeKSunwooM. Integration of multiple vehicle models with an IMM filter for vehicle localization. In: IEEE intelligent vehicles symposium, La Jolla, California, USA, 21–24 June 2010, pp. 746–751. New York: IEEE.
38.
JoKChuKSunwooM.Interacting multiple model filter-based sensor fusion of GPS with in-vehicle sensors for real-time vehicle positioning. IEEE Trans Intell Transpn Systems2012; 13: 329–343.
39.
GwakMJoKSunwooM.Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles. Int J Automot Technol. 2013; 14: 265–274.
40.
JoKKimJSunwooM.Real-time road-slope estimation based on integration of onboard sensors with GPS using an IMMPDA filter. IEEE Trans Intell Transpn Systems2013; 14: 1718–1732.
41.
BaeHSRyuJGerdesJC. Road grade and vehicle parameter estimation for longitudinal control using GPS. In: 2001 IEEE intelligent transportation systems conference, Oakland, California, USA, 25–29 August 2001, pp. 166–171. New York: IEEE.
42.
BevlyDM.Global Positioning System (GPS): a low-cost velocity sensor for correcting inertial sensor errors on ground vehicles. Trans ASME, J Dynamic Systems, Measmt Control2004; 126: 255–264.
43.
DrakeS.Converting GPS coordinates (ϕλh) to navigation coordinates (ENU). Report, DSTO-TN-0432, Surveillance Systems Division, Electronics and Surveillance Research Laboratory, Defence Science Technology, Edinburgh, South Australia, Australia, 2002.
44.
LiAZhaoWLiSet al. Research on the motion trajectory optimization method based on the improved genetic algorithm for an intelligent vehicle. Proc IMechE Part D: J Automobile Engineering2016: 230(13): 1729–1740.
45.
KimJJoKChuKSunwooM.Road-model-based and graph-structure-based hierarchical path-planning approach for autonomous vehicles. Proc IMechE Part D: J Automobile Engineering2014; 228(8): 909–928.
46.
LiWXuSZhaoGGohLP.A heuristic knot placement algorithm for B-spline curve approximation. Comput-Aided Des Applic2004; 1: 727–732.
47.
ParkH.An error-bounded approximate method for representing planar curves in B-splines. Comput Aided Geometric Des2004; 21: 479–497.
