985348956

More documents

Recommendations

Info

28 R. Streiter et al. measurements which can then be processed in software to alter flexibility. To be more general, sensor data processing is depicted as a post-processing step and the actual application working with the data. The post-processing step can include further, less demanding data refinement procedures. Furthermore, it can be used to assess the suitability of the automatically determined data stream processing parameters and adapt them if necessary. This enables applications to control the process of filtering the sensor baseband data, giving them the opportunity to adapt the mechanisms to varying environmental influences. We expect this approach to yield more precise sensor data by giving applications control over the pre-processing stage as they can integrate situational knowledge, leading to a situation aware tuning of pre-processing parameters rather than a fixed, good on average, model. 4 Conclusion The NAVENTIK receiver is based on a software prototype that has been developed and verified in a wide range of use cases in the European research projects CoVeL (Cooperative Vehicle Localization) and GAIN (Galileo for Interactive Driving) within the 7th Framework Programme. The extension regarding confidence adaptive use cases in a safety related context will be implemented today, within the InDrive project in Horizon 2020. The NAVENTIK project is dedicated to the further development of the Prototype towards a mass-market product. Starting from the software-defined approach for GNSS signal processing we are going to adopt a generalized platform design in order to complement requirements not only arising from the field of satellite navigation but also to extend the system by environmental perception sensors such as Radar and Lidar. Those systems do also suffer from signal preprocessing which is, from a statistical perspective, always subject to strong losses of information. Clustering algorithms and noise reduction at the very early stage are violating the integrity of those sensors as well. Especially advanced tracking algorithms in nowadays ACCs (Adaptive Cruise Control) can benefit from the statistically correct representation within the measurement space especially where the weaknesses of existing systems, like tracking extended targets and obstructed objects are emerging. We can assume that tracking performance and overall reliability of those perception systems can be significantly improved by the proper integration within the NAVENTIK platform. Acknowledgments This work is carried out within the NAVENTIK project. NAVENTIK is a research project co-funded by the Federal Ministry for Economic Affairs and Energy (BMWi) and the European Social Fund (ESF) within the EXIST Transfer of Research programme. The authors would like to thank all partners for supporting this work. Furthermore, for the evaluation and generation of ground truth data, precise real-time corrections provided by the ascos service (http:// www.ascos.de) were used.
Probabilistic Integration of GNSS for Safety-Critical Driving … 29 References 1. Groves PD, Jiang Z, Rudi M, Strode P (2013) A portfolio approach to NLOS and multipath mitigation in dense urban areas. In: Proceedings of the 26th international technical meeting of the satellite division of the institute of navigation 2. Obst M, Bauer S, Reisdorf P, Wanielik G (2012) Multipath detection with 3D digital maps for robust multi-constellation GNSS/INS vehicle localization in urban areas. In: Proceedings of the IEEE intelligent vehicles symposium 3. Peyret F, Betaille D, Carolina P, Toledo-Moreo R, Gomez-Skarmeta A, Ortiz M (2014) GNSS autonomous localization: NLOS satellite detection based on 3-D Maps. IEEE Robot Autom Mag 57–63 4. Bauer S, Streiter R, Obst M, Wanielik G (2015) Non-line-of-sight mitigation for reliable urban GNSS vehicle localization using a particle filter. In: 2015 18th IEEE international conference on information fusion (FUSION), Washington, DC, 6–9 July 2015 5. Viandier N, Marais J, Rabaoui A, Duflos E (2010) GNSS pseudorange error density tracking using Dirichlet process mixture. In: 2010 13th Conference on information fusion (FUSION), pp 1–7 6. Closas P, Fernandez-Prades C, Fernandez-Rubio J (2009) A Bayesian approach to multipath mitigation in GNSS receivers. IEEE J Sel Top Sign Process 3(4):695–706 7. Khider M, Jost T, Sanchez E, Robertson P, Angermann M (2010) Bayesian multisensor navigation incorporating pseudoranges and multipath model. In: Position location and navigation symposium 8. Streiter R, Bauer S, Wanielik G (2015) Probabilistic GNSS signal tracking for safety relevant automotive applications. In: 2015 18th IEEE International conference on information fusion (FUSION), Washington, DC, 6–9 July 2015
Page 1 and 2: Lecture Notes in Mobility Tim Schul
Page 3 and 4: More information about this series
Page 5 and 6: Editors Tim Schulze VDI/VDE Innovat
Page 7 and 8: vi Preface cross-sectorial roadmap
Page 9 and 10: Steering Committee Mike Babala, TRW
Page 11 and 12: xii Contents Robust Facial Landmark
Page 13 and 14: Part I Networked Vehicles & Navigat
Page 15 and 16: 4 J. Rünz et al. Keywords Smartpho
Page 17 and 18: 6 J. Rünz et al. and x b ib from t
Page 19 and 20: 8 J. Rünz et al. In a localization
Page 21 and 22: 10 J. Rünz et al. Table 1 Major di
Page 23 and 24: 12 J. Rünz et al. Fig. 5 Horizonta
Page 25 and 26: 14 J. Rünz et al. which crosses th
Page 27 and 28: 16 J. Rünz et al. sensors like vid
Page 29 and 30: Probabilistic Integration of GNSS f
Page 37: Probabilistic Integration of GNSS f
Page 41 and 42: 32 J. Hiltscher et al. an overview)
Page 43 and 44: 34 J. Hiltscher et al. operates in
Page 45 and 46: 36 J. Hiltscher et al. 3.5 Security
Page 47 and 48: 38 J. Hiltscher et al. 5 Results In
Page 49 and 50: 40 J. Hiltscher et al. 6 Conclusion
Page 51 and 52: Prototyping Framework for Cooperati
Page 63 and 64: 56 S. Müller et al. 1 Trends—Aut
Page 65 and 66: 58 S. Müller et al. Fig. 4 Complem
Page 67 and 68: 60 S. Müller et al. Fig. 7 Tomorro
Page 69 and 70: 62 S. Müller et al. verify Message
Page 71 and 72: 64 S. Müller et al. hardware secur
Page 73 and 74: 66 J. van Hattem more than 30 elect
Page 75 and 76: 68 J. van Hattem operate between 30
Page 77 and 78: 70 J. van Hattem Fig. 2 Map service
Page 79 and 80: 72 J. van Hattem some hurdles to ma
Page 81 and 82: Part II Advanced Sensing, Perceptio
Page 83 and 84: 78 S.A. Bagüés et al. Keywords Da
Page 85 and 86: 80 S.A. Bagüés et al. Finally, Du
Page 87 and 88: 82 S.A. Bagüés et al. Fig. 1 SADA
Page 89 and 90:
84 S.A. Bagüés et al. • Generic
Page 91 and 92:
86 S.A. Bagüés et al. As describe
Page 93 and 94:
88 S.A. Bagüés et al. Up to 1000
Page 95 and 96:
Robust Facial Landmark Localization
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Page 107 and 108:
Using eHorizon to Enhance Camera-Ba
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Performance Enhancements for the De
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
126 M. Ali et al. 1 Introduction Ad
Page 131 and 132:
128 M. Ali et al. 2 Physiological S
Page 133 and 134:
130 M. Ali et al. features from EDA
Page 135 and 136:
132 M. Ali et al. Fig. 2 Our propos
Page 137 and 138:
134 M. Ali et al. strategy; this me
Page 139 and 140:
136 M. Ali et al. Table 2 Performan
Page 141 and 142:
138 M. Ali et al. 11. Soleymani M,
Page 143 and 144:
Highly Automated Driving—Disrupti
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Scenario Identification for Validat
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
166 D. Stumper et al. the task of p
Page 169 and 170:
168 D. Stumper et al. 3.1 Generatio
Page 171 and 172:
170 D. Stumper et al. On the other
Page 173 and 174:
172 D. Stumper et al. Fig. 4 In the
Page 175 and 176:
Functional Safety: On-Board Computi
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Part IV Smart Electrified Vehicles
Page 183 and 184:
184 M. Sansa and H.I.H. Azami 1 Con
Page 185 and 186:
186 M. Sansa and H.I.H. Azami from
Page 187 and 188:
188 M. Sansa and H.I.H. Azami The g
Page 189 and 190:
190 M. Sansa and H.I.H. Azami where
Page 191 and 192:
192 M. Sansa and H.I.H. Azami total
Page 193 and 194:
194 M. Sansa and H.I.H. Azami 3.3 A
Page 195 and 196:
196 M. Sansa and H.I.H. Azami The H
Page 197 and 198:
198 M. Sansa and H.I.H. Azami Fig.
Page 199 and 200:
200 M. Sansa and H.I.H. Azami Refer
Page 201 and 202:
202 R. John et al. demonstrate a ra
Page 203 and 204:
204 R. John et al. project (i.e. re
Page 205 and 206:
206 R. John et al. Fig. 2 Scheme of
Page 207 and 208:
208 R. John et al. 4.1 Front Suspen
Page 209 and 210:
210 R. John et al. adjustments requ
Page 211 and 212:
212 R. John et al. MOT FR MOT BR IN
Page 213 and 214:
214 R. John et al. Fig. 10 Prelimin
Page 215 and 216:
Next Generation Drivetrain Concept
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Embedding Electrochemical Impedance
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
Page 235 and 236:
Page 237 and 238:
240 E. Larrodé Pellicer et al. con
Page 239 and 240:
242 E. Larrodé Pellicer et al. Fig
Page 241 and 242:
Page 243 and 244:
Page 245 and 246:
248 E. Larrodé Pellicer et al. The
Page 247 and 248:
Page 249 and 250:
Time to Market—Enabling the Speci
Page 251 and 252:
Page 253 and 254:
Page 255 and 256:
Page 257 and 258:
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
show all

985348956

Create successful ePaper yourself

Delete template?

Save as template?