Knowledge-based driver assistance systems: traffic situation description and situation feature relevance
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Wiesbaden
Springer Vieweg
2014
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| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XVI, 176 S. Ill., graph. Darst. |
| ISBN: | 9783658057497 |
Internformat
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Datensatz im Suchindex
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TABLE OF CONTENTS
ACKNOWLEDGMENT V
ABSTRACT VII
KURZFASSUNG IX
LIST OF ABBREVIATIONS XV
LIST OF SYMBOLS XVII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 OBJECTIVE AND CONTRIBUTION OF THIS THESIS 3
1.3 OUTLINE OF THIS THESIS 6
CHAPTER 2 THE RESEARCH DOMAIN OF THIS THESIS AND ITS STATE OF THE ART 7
2.1 ADVANCED DRIVER ASSISTANCE SYSTEMS 7
2.1.1 ADVANCED DRIVER ASSISTANCE FUNCTIONS 7
2.1.2 SENSORS FOR DRIVER ASSISTANCE 8
2.1.3 ACTORS FOR DRIVER ASSISTANCE 11
2.2 TRAFFIC SITUATION DESCRIPTION AND ANALYSIS AS A KEY ENABLER FOR ADAS
11
2.2.1 SITUATION AWARENESS AND COMPREHENSION 11
2.2.2 SITUATION DESCRIPTION IN THE DRIVING CONTROL LOOP 13
2.2.3 ASPECTS OF SITUATION DESCRIPTION RELEVANT TO THIS THESIS 14
2.2.4 SITUATION FEATURE RELEVANCE 18
2.2.5 TRAFFIC SITUATION DESCRIPTION AND INTERPRETATION APPROACHES 19
2.2.6 KNOWLEDGE-BASED TRAFFIC SITUATION DESCRIPTION AND UNDERSTANDING 21
2.3 DELIMITATION OF THIS THESIS 25
CHAPTER 3 THEORETICAL FOUNDATIONS RELEVANT TO THIS THESIS 27
3.1 MUTUAL INFORMATION FEATURE SELECTION 28
3.1.1 FEATURE SELECTION 28
3.1.2 INFORMATION THEORY, ENTROPY AND MUTUAL INFORMATION 29
3.1.3 MUTUAL INFORMATION FEATURE SELECTION METHODS 30
3.1.4 RANDOM FORESTS, A MACHINE LEARNING ALGORITHM 33
3.2 KNOWLEDGE REPRESENTATION 34
3.2.1 METHODS FOR KNOWLEDGE REPRESENTATION 34
3.2.2 ONTOLOGIES 35
3.2.3 DESCRIPTION LOGICS 36
3.2.4 LOGIC REASONING 37
3.2.5 PROBABILISTIC LOGIC REASONING: STATE OF THE ART 39
3.3 SUMMARY 43
HTTP://D-NB.INFO/1049606469
XII
TABLE OF CONTENTS
CHAPTER 4 SITUATION FEATURE RELEVANCE ON MEASUREMENT DATA 45
4.1 RELEVANCE OF SITUATION FEATURES FOR DRIVER ASSISTANCE FUNCTIONS 46
4.1.1 SELECTION OF RELEVANT SITUATION FEATURES 46
4.1.2 EVALUATING GENERATED FEATURES 46
4.1.3 FINDING CAUSES FOR SALIENT EFFECTS 47
4.1.4 FEATURE SELECTION FOR AUTOMATED MACHINE LEARNING 47
4.2 ADAPTIONS TO IMPROVE ACCURACY 47
4.2.1 RELATED METHODICAL ERRORS 48
4.2.2 IMPROVED CRITERION: MINIMUM CLASS REDUNDANCY MAXIMUM RELEVANCE
RANKING (MCRMR) 48
4.2.3 ERROR CALCULATIONS 49
4.2.4 VARIATION BY WEIGHTING TO REDUCE NUMERICAL EFFECTS 51
4.2.5 EXPERIMENTAL RESULTS 52
4.3 APPLICATION ON MEASUREMENT DATA FROM ENDURANCE RUNS 56
4.3.1 THE PROCESS OF DATA PREPARATION AND RANKING CREATION 57
4.3.2 DATA DESCRIPTION 58
4.3.3 DEFINITION OF THE DRIVER INTENTION TO BE ESTIMATED 58
4.3.4 FEATURE RANKING RESULTS 59
4.3.5 RESULTS OF DRIVER INTENTION RECOGNITION WITH THE
RANDOM-FOREST-CLASSIFIER. 62
4.3.6 COMPARISON OF TIME CONSUMPTION TO BUILD RANKINGS 64
4.4 MULTI FEATURE RANKINGS 65
4.4.1 MAXIMUM RELEVANCE FOR MULTIPLE FEATURES 65
4.4.2 REDUCING REDUNDANCY AMONG PAIRS OF FEATURES 68
4.5 RELEVANCE OF HISTORICAL MEASUREMENT VALUES 69
4.5.1 PREPARATION OF FEATURE TIME SERIES 71
4.5.2 DETERMINATION OF HISTORICAL RELEVANCE WITH MUTUAL INFORMATION 73
4.5.3 SIMULATION RESULTS ON HISTORICAL RELEVANCE OF FEATURES 81
4.6 SUMMARY 90
CHAPTER 5 KNOWLEDGE-BASED TRAFFIC SITUATION DESCRIPTION 93
5.1 AN ONTOLOGY FOR THE DESCRIPTION OF COMPLEX INTERSECTION SITUATIONS
93
5.1.1 SIMPLE INTERSECTION DESCRIPTION 93
5.1.2 COMPLEX INTERSECTION AND ROAD NETWORK ONTOLOGY 94
5.1.3 REASONING RESULTS OF AN INTERSECTION EXAMPLE 101
5.1.4 EXPANSIONS FOR PARTIAL KNOWLEDGE MODELING 104
5.1.5 TIME CONSUMPTION FOR REASONING AND IMPLEMENTATION ISSUES FOR DAS
106
5.2 ASYNCHRONOUS REAL-TIME FRAMEWORK FOR KNOWLEDGE-BASED DAS 108
5.2.1 SIMULATION OF KNOWLEDGE-BASED DRIVER ASSISTANCE SYSTEMS 108
5.2.2 ASYNCHRONOUS REAL-TIME FRAMEWORK 108
5.2.3 MULTILEVEL-STRUCTURE OF ONTOLOGY FUNCTIONALITY FOR ONLINE TRAFFIC
SITUATION DESCRIPTION 110
5.2.4 INTERSECTION SIMULATION 113
TABLE OF CONTENTS XIII
5.3 KNOWLEDGE-BASED DRIVER ASSISTANCE FUNCTIONS 115
5.3.1 APPLICATION SCENARIOS AND FUNCTIONS 115
5.3.2 EXEMPLARY IDAF-REALIZATIONS OF TISDO-BASED DRIVER ASSISTANCE
SYSTEMS. 118
5.3.3 BENEFIT OF KNOWLEDGE-BASED SITUATION DESCRIPTION 122
5.4 UNCERTAINTY HANDLING 124
5.4.1 SENSOR UNCERTAINTY 124
5.4.2 CERTAINTY AND UNCERTAINTY OF ONTOLOGY AXIOMS AND RULES 129
5.4.3 HANDLING PRIOR PROBABILITIES WITH ONTOLOGIES 129
5.4.4 GHOST-OBJECTS FOR UNKNOWN INFORMATION 136
5.5 SUMMARY 137
CHAPTER 6 RELEVANCE BY MUTUAL INFORMATION ON ONTOLOGY FEATURES 139
6.1 SELECTING IMPORTANT ONTOLOGY FEATURES 139
6.1.1 FEATURES FOR ONTOLOGY ELEMENTS 140
6.1.2 COMBINING ONTOLOGY ELEMENTS AS ELEMENT CHAIN FEATURES 143
6.2 APPLICATION SCENARIOS 146
6.3 SAMPLE APPLICATION OF AN ONTOLOGY FEATURE RANKING 147
6.3.1 DATA AND LABELING DESCRIPTION 147
6.3.2 ONTOLOGY FEATURE RANKING RESULTS ON LABELED DATA 148
6.4 SUMMARY 150
CHAPTER 7 CONCLUSION 153
7.1 CONTRIBUTIONS 153
7.2 OUTLOOK 156
APPENDIX A FULL RANKING TABLES 161
A. 1 MAXIMUM RELEVANCE RANKING OF TWO JOINT FEATURES 161
A.2 MAXIMUM RELEVANCE RANKING OF SEMANTIC FEATURES 164
REFERENCES 167 |
| any_adam_object | 1 |
| author | Hülsen, Michael |
| author_facet | Hülsen, Michael |
| author_role | aut |
| author_sort | Hülsen, Michael |
| author_variant | m h mh |
| building | Verbundindex |
| bvnumber | BV041860736 |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.276 |
| dewey-search | 629.276 |
| dewey-sort | 3629.276 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Maschinenbau / Maschinenwesen Verkehr / Transport |
| format | Book |
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| isbn | 9783658057497 |
| language | English |
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| physical | XVI, 176 S. Ill., graph. Darst. |
| publishDate | 2014 |
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| publisher | Springer Vieweg |
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| spelling | Hülsen, Michael Verfasser aut Knowledge-based driver assistance systems traffic situation description and situation feature relevance Michael Huelsen 201405 Wiesbaden Springer Vieweg 2014 XVI, 176 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Fahrerassistenzsystem (DE-588)4622983-8 gnd rswk-swf Verkehrsmodell (DE-588)4593441-1 gnd rswk-swf Fahrerassistenzsystem (DE-588)4622983-8 s Verkehrsmodell (DE-588)4593441-1 s DE-604 Erscheint auch als Online-Ausgabe 978-3-658-05750-3 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4626837&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027304982&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hülsen, Michael Knowledge-based driver assistance systems traffic situation description and situation feature relevance Fahrerassistenzsystem (DE-588)4622983-8 gnd Verkehrsmodell (DE-588)4593441-1 gnd |
| subject_GND | (DE-588)4622983-8 (DE-588)4593441-1 |
| title | Knowledge-based driver assistance systems traffic situation description and situation feature relevance |
| title_auth | Knowledge-based driver assistance systems traffic situation description and situation feature relevance |
| title_exact_search | Knowledge-based driver assistance systems traffic situation description and situation feature relevance |
| title_full | Knowledge-based driver assistance systems traffic situation description and situation feature relevance Michael Huelsen |
| title_fullStr | Knowledge-based driver assistance systems traffic situation description and situation feature relevance Michael Huelsen |
| title_full_unstemmed | Knowledge-based driver assistance systems traffic situation description and situation feature relevance Michael Huelsen |
| title_short | Knowledge-based driver assistance systems |
| title_sort | knowledge based driver assistance systems traffic situation description and situation feature relevance |
| title_sub | traffic situation description and situation feature relevance |
| topic | Fahrerassistenzsystem (DE-588)4622983-8 gnd Verkehrsmodell (DE-588)4593441-1 gnd |
| topic_facet | Fahrerassistenzsystem Verkehrsmodell |
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