Autonomous Intelligent Vehicles - Theory, Algorithms, and Implementation

163 Pages·2016·4.82 MB·English

by  Hong Cheng

Advances in Computer Vision and Pattern
Recognition
Forfurthervolumes:
www.springer.com/series/4205

Hong Cheng
Autonomous
Intelligent Vehicles
Theory, Algorithms, and Implementation
Prof.HongCheng
SchoolofAutomationEngineering
UniversityofElectronicScienceand
Technology
610054Chengdu,Sichuan,
People’sRepublicofChina
[email protected]
SeriesEditors
ProfessorSameerSingh,PhD Dr.SingBingKang
ResearchSchoolofInformatics MicrosoftResearch
LoughboroughUniversity MicrosoftCorporation
Loughborough OneMicrosoftWay
UK Redmond,WA98052
USA
ISSN2191-6586 e-ISSN2191-6594
AdvancesinComputerVisionandPatternRecognition
ISBN978-1-4471-2279-1 e-ISBN978-1-4471-2280-7
DOI10.1007/978-1-4471-2280-7
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Preface
Overtheyears,thefieldofintelligentvehicleshasbecomeamajorresearchtheme
inintelligenttransportationsystemssincetrafficaccidentsareseriousandgrowing
problemsallovertheworld.Thegoalofanintelligentvehicleistoaugmentvehicle
autonomous driving either entirely or partly for the purposes of safety, comforta-
bility,andsavingenergy.Indeed,manytechnologiesofintelligentvehiclesrootin
autonomousmobilerobots.Thetasksofintelligentvehiclesbecomeevenmorechal-
lengingcomparedtoindoormobilerobotsfortworeasons.First,real-timedynamic
complexenvironmentperceptionandmodelingwillchallengecurrentindoorrobot
technologies.Autonomousintelligentvehicleshavetofinishthebasicprocedures:
perceivingandmodelingenvironment,localizingandbuildingmaps,planningpaths
and making decisions, and controlling the vehicles within limit time for real-time
purposes. Meanwhile, we face the challenge of processing large amounts of data
frommulti-sensors,suchascameras,lidars,radars.Thisisextremelyhardinmore
complexoutdoorenvironments.Towardthisend,wehavetoimplementthosetasks
inmoreefficientways.Second,vehiclemotioncontrolfacesthechallengesofstrong
nonlinearcharacteristicsduetohighmass,especiallyintheprocessesofhighspeed
andsuddensteering.Inthiscase,bothlateralandlongitudinalcontrolalgorithmsof
indoorrobotsdonotworkwell.
Thisbookpresentsourrecentresearchworkonintelligentvehiclesandisaimed
attheresearchersandgraduatestudentsinterestedinintelligentvehicles.Ourgoal
in writing this book is threefold. First, it creates an updated reference book of in-
telligentvehicles.Second,thisbooknotonlypresentsobject/obstacledetectionand
recognition,butalsointroducesvehiclelateralandlongitudinalcontrolalgorithms,
whichbenefitsthereaderskeentolearnbroadlyaboutintelligentvehicles.Finally,
weputemphasisonhigh-levelconcepts,andatthesametimeprovidethelow-level
detailsofimplementation.Wetrytolinktheory,algorithms,andimplementationto
promoteintelligentvehicleresearch.
Thisbookisdividedintofourparts.ThefirstpartAutonomousIntelligentVe-
hicles presents the research motivationand purposes, the state-of-art of intelligent
vehiclesresearch.Also,weintroducetheframeworkofintelligentvehicles.Thesec-
ond part Environment Perception and Modeling which includes Road detection
v
vi Preface
andtracking,Vehicledetectionandtracking,Multiple-sensorbasedmultiple-object
tracking introduces environment perception and modeling. The third part Vehicle
LocalizationandNavigationwhichincludesAnintegratedDGPS/IMUpositioning
approach,Vehiclenavigationusingglobalviewspresentsvehiclenavigationbased
on integrated GPS and INS. The fourth part Advanced Vehicle Motion control
introducesvehiclelateralandlongitudinalmotioncontrol.
Most of this book refers to our research work at Xi’an Jiaotong University and
Carnegie Mellon University. During the last ten years of research, a large number
ofpeoplehadbeenworkingintheSpringrobotProjectatXi’anJiaotongUniversity.
I would like to deliver my deep respect to my Ph.D advisor, Professor Nanning
Zheng,wholeadedmeintothisfield.AlsoIwouldliketothank:YuehuLiu,Xiaojun
Lv, Lin Ma, Xuetao Zhang, Junjie Qin, Jingbo Tang, Yingtuan Hou, Jing Yang,
Li Zhao, Chong Sun, Fan Mu, Ran Li, Weijie Wang, and Huub van de Wetering.
Also,IwouldliketothankJieYangatCarnegieMellonUniversitywhosupported
Hong Cheng’s research work during his stay at this university and Zicheng Liu at
MicrosoftResearchwhohelpedHongChengdiscussvehiclenavigationwithglobal
views.IalsowouldliketooursincereanddeepthankstoZhongjunDaiwhohelped
immenselywithfigurepreparationandwiththetypesettingofthebookinLaTeX.
Manypeoplehavehelpedbyproofreadingdraftmaterialsandprovidingcomments
and suggestions, including Nana Chen, Rui Huang, Pingxin Long, Wenjun Jing,
YuzhuoWang.Springerhasprovidedexcellentsupportthroughoutthefinalstages
of preparation of this book, and I would like to thank our commissioning editor
WayneWheelerforhissupportandprofessionalismaswellasSimonReesforhis
help.
Chengdu,People’sRepublicofChina HongCheng
Contents
PartI AutonomousIntelligentVehicles
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1 ResearchMotivationandPurpose . . . . . . . . . . . . . . . . . . 3
1.2 TheKeyTechnologiesofIntelligentVehicles . . . . . . . . . . . . 5
1.2.1 Multi-sensorFusionBasedEnvironmentPerceptionand
Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.2 VehicleLocalizationandMapBuilding . . . . . . . . . . . 7
1.2.3 PathPlanningandDecision-Making . . . . . . . . . . . . 8
1.2.4 Low-LevelMotionControl . . . . . . . . . . . . . . . . . 9
1.3 TheOrganizationofThisBook . . . . . . . . . . . . . . . . . . . 9
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 TheState-of-the-ArtintheUSA . . . . . . . . . . . . . . . . . . . . . 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 CarnegieMellonUniversity—Boss . . . . . . . . . . . . . . . . . 13
2.3 StanfordUniversity—Junior . . . . . . . . . . . . . . . . . . . . . 15
2.4 VirginiaPolytechnicInstituteandStateUniversity—Odin . . . . . 16
2.5 MassachusettsInstituteofTechnology—Talos . . . . . . . . . . . 17
2.6 CornellUniversity—Skynet . . . . . . . . . . . . . . . . . . . . . 18
2.7 UniversityofPennsylvaniaandLehighUniversity—LittleBen . . 19
2.8 OshkoshTruckCorporation—TerraMax . . . . . . . . . . . . . . 20
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3 TheFrameworkofIntelligentVehicles . . . . . . . . . . . . . . . . . 23
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3 InteractiveSafetyAnalysisFramework . . . . . . . . . . . . . . . 25
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
vii
viii Contents
PartII EnvironmentPerceptionandModeling
4 RoadDetectionandTracking . . . . . . . . . . . . . . . . . . . . . . 33
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2.1 Model-BasedApproaches . . . . . . . . . . . . . . . . . . 35
4.2.2 Multi-cueFusionBasedApproach . . . . . . . . . . . . . 35
4.2.3 Hypothesis-ValidationBasedApproaches. . . . . . . . . . 36
4.2.4 NeuralNetworkBasedApproaches . . . . . . . . . . . . . 36
4.2.5 Stereo-BasedApproaches . . . . . . . . . . . . . . . . . . 36
4.2.6 TemporalCorrelationBasedApproaches . . . . . . . . . . 37
4.2.7 ImageFilteringBasedApproaches . . . . . . . . . . . . . 37
4.3 LaneDetectionUsingAdaptiveRandomHoughTransform . . . . 37
4.3.1 TheLaneShapeModel . . . . . . . . . . . . . . . . . . . 37
4.3.2 TheAdaptiveRandomHoughTransform . . . . . . . . . . 38
4.3.3 ExperimentalResults . . . . . . . . . . . . . . . . . . . . 41
4.4 LaneTracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.4.1 ParticleFiltering . . . . . . . . . . . . . . . . . . . . . . . 43
4.4.2 LaneModel . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4.3 DynamicSystemModel . . . . . . . . . . . . . . . . . . . 46
4.4.4 TheImagingModel . . . . . . . . . . . . . . . . . . . . . 46
4.4.5 TheAlgorithmImplementation . . . . . . . . . . . . . . . 48
4.5 RoadRecognitionUsingaMeanShiftalgorithm . . . . . . . . . . 51
4.5.1 TheBasicMeanShiftAlgorithm . . . . . . . . . . . . . . 52
4.5.2 VariousApplicationsoftheMeanShiftAlgorithm . . . . . 54
4.5.3 TheRoadRecognitionAlgorithm . . . . . . . . . . . . . . 55
4.5.4 ExperimentalResultsandAnalysis . . . . . . . . . . . . . 56
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5 VehicleDetectionandTracking . . . . . . . . . . . . . . . . . . . . . 61
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.3 GeneratingCandidateROIs . . . . . . . . . . . . . . . . . . . . . 63
5.4 Multi-resolutionVehicleHypothesis . . . . . . . . . . . . . . . . 65
5.5 VehicleValidationusingGaborFeaturesandSVM . . . . . . . . . 67
5.5.1 VehicleRepresentation . . . . . . . . . . . . . . . . . . . 67
5.5.2 SVMClassifier. . . . . . . . . . . . . . . . . . . . . . . . 68
5.6 BoostedGaborFeatures . . . . . . . . . . . . . . . . . . . . . . . 71
5.6.1 BoostedGaborFeaturesUsingAdaBoost . . . . . . . . . . 72
5.6.2 ExperimentalResultsandAnalysis . . . . . . . . . . . . . 74
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6 Multiple-SensorBasedMultiple-ObjectTracking . . . . . . . . . . . 81
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.3 ObstaclesStationaryorMovingJudgementUsingLidarData . . . 82
6.4 Multi-obstacleTrackingandSituationAssessment . . . . . . . . . 84
Contents ix
6.4.1 Multi-obstacleTrackingBasedonEKFUsingaSingle
Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.4.2 LidarandRadarTrackFusion . . . . . . . . . . . . . . . . 90
6.5 ConclusionandFutureWork . . . . . . . . . . . . . . . . . . . . 92
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
PartIII VehicleLocalizationandNavigation
7 AnIntegratedDGPS/IMUPositioningApproach . . . . . . . . . . . 99
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
7.3 AnIntegratedDGPS/IMUPositioningApproach . . . . . . . . . . 101
7.3.1 TheSystemEquation . . . . . . . . . . . . . . . . . . . . 101
7.3.2 TheMeasurementEquation . . . . . . . . . . . . . . . . . 104
7.3.3 DataFusionUsingEKF . . . . . . . . . . . . . . . . . . . 105
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
8 VehicleNavigationUsingGlobalViews . . . . . . . . . . . . . . . . . 109
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
8.2 TheProblemandProposedApproach . . . . . . . . . . . . . . . . 110
8.3 ThePanoramicImagingModel . . . . . . . . . . . . . . . . . . . 112
8.4 ThePanoramicInversePerspectiveMapping(pIPM) . . . . . . . . 114
8.4.1 TheMappingRelationshipBetweenEachImageanda
PanoramicImage . . . . . . . . . . . . . . . . . . . . . . 114
8.4.2 ThePanoramicInversePerspectiveMapping . . . . . . . . 115
8.5 TheImplementationofthepIPM . . . . . . . . . . . . . . . . . . 116
8.5.1 TheFieldofViewofN CamerasintheVehicleCoordinate
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
8.5.2 CalculationofEachInterestPoint’sViewAngleinthe
VehicleCoordinateSystem . . . . . . . . . . . . . . . . . 116
8.5.3 TheMappingRelationshipBetweena3DOn-roadPoint
andaPanoramicImage . . . . . . . . . . . . . . . . . . . 117
8.5.4 ImageInterpolationintheVehicleCoordinateSystem . . . 117
8.6 TheEliminationofWide-AngleLens’RadialError . . . . . . . . 118
8.7 CombiningPanoramicImageswithElectronicMaps . . . . . . . . 119
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
PartIV AdvancedVehicleMotionControl
9 TheLateralMotionControlforIntelligentVehicles . . . . . . . . . . 125
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
9.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
9.3 TheMixedLateralControlStrategy . . . . . . . . . . . . . . . . . 126
9.3.1 LinearRoads . . . . . . . . . . . . . . . . . . . . . . . . . 127
9.3.2 CurvilinearRoads . . . . . . . . . . . . . . . . . . . . . . 128
9.3.3 CalculatingtheRadiusofanArc . . . . . . . . . . . . . . 131
9.3.4 TheAlgorithmFlow . . . . . . . . . . . . . . . . . . . . . 132