Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Optical Memory and Neural Networks
Erschienen in: Optical Memory and Neural Networks 2/2022

01.06.2022

Advanced Techniques for Perception and Localization in Autonomous Driving Systems: A Survey

verfasst von: Qusay Sellat, Kanagachidambaresan Ramasubramanian

Erschienen in: Optical Memory and Neural Networks | Ausgabe 2/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Autonomous driving research has progressed significantly in recent years. In order to travel safely, comfortably, and effectively, an autonomous car must completely comprehend the driving scenario at all times. The key criteria for a comprehensive understanding of the driving situations are proper perception and localization. The research on perception and localization of autonomous cars has increased substantially as a result of recent breakthroughs in AI, such as the wide range of deep learning approaches. However, owing to environmental uncertainties, sensor noise, and the complex interaction between the parts of the driving environment, additional study is required to achieve totally trustworthy perception and localization systems. In this survey, we demonstrate the advanced perception and localization processes in the field of autonomous driving. We show how cutting-edge approaches and practices have brought today’s autonomous cars closer than ever to completely comprehending the driving environment.
Nächster Artikel Survey of Computational Modeling of the Functional Parts of the Brain

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Hoel, C.J., Driggs-Campbell, K., Wolff, K., Laine, L., and Kochenderfer, M.J., Combining planning and deep reinforcement learning in tactical decision making for autonomous driving, IEEE Trans. Intell. Veh., 2019, vol. 5, no. 2, pp. 294-305.CrossRef
2.
Lefevre, S., Carvalho, A., and Borrelli, F., A learning-based framework for velocity control in autonomous driving, IEEE Trans. Autom. Sci. Eng., 2015, vol. 13, no. 1, pp. 32–42.MATHCrossRef
3.
Choi, J., Lee, J., Kim, D., Soprani, G., Cerri, P., Broggi, A., and Yi, K., Environment-detection-and-mapping algorithm for autonomous driving in rural or off-road environment, IEEE Trans. Intell. Transp. Syst., 2012, vol. 13, no. 2, pp. 974–982.CrossRef
4.
Li, Q., Chen, L., Li, M., Shaw, S.L., and Nüchter, A., A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios, IEEE Trans. Veh. Technol., 2013, vol. 63, no. 2, pp. 540–555.CrossRef
5.
Aldibaja, M., Suganuma, N., and Yoneda, K., Robust intensity-based localization method for autonomous driving on snow–wet road surface, IEEE Trans. Ind. Inf., 2017, vol. 13, no. 5, pp. 2369–2378.CrossRef
6.
Kim, T.H. and Park, T.H., Placement optimization of multiple LIDAR sensors for autonomous vehicles, IEEE Trans. Intell. Transp. Syst., 2019, vol. 21, no. 5, pp. 2139–2145.CrossRef
7.
Lim, K.L., Drage, T., Zhang, C., Brogle, C., Lai, W.W., Kelliher, T., Adina-Zada, M., and Bräunl, T., Evolution of a reliable and extensible high-level control system for an autonomous car, IEEE Trans. Intell. Veh., 2019, vol. 4, no. 3, pp. 396–405.CrossRef
8.
Mei, J., Gao, B., Xu, D., Yao, W., Zhao, X., and Zhao, H., Semantic segmentation of 3d lidar data in dynamic scene using semi-supervised learning, IEEE Trans. Intell. Transp. Syst., 2019, vol. 21, no. 6, pp. 2496–2509.CrossRef
9.
Jo, K., Kim, J., Kim, D., Jang, C., and Sunwoo, M., Development of autonomous car – Part II: A case study on the implementation of an autonomous driving system based on distributed architecture, IEEE Trans. Ind. Electron., 2015, vol. 62, no. 8, pp. 5119–5132.CrossRef
10.
Jo, K., Jo, Y., Suhr, J.K., Jung, H.G., and Sunwoo, M., Precise localization of an autonomous car based on probabilistic noise models of road surface marker features using multiple cameras, IEEE Trans. Intell. Transp. Syst., 2015, vol. 16, no. 6, pp. 3377–3392.CrossRef
11.
Vivacqua, R.P.D., Bertozzi, M., Cerri, P., Martins, F.N., and Vassallo, R.F., Self-localization based on visual lane marking maps: An accurate low-cost approach for autonomous driving, IEEE Trans. Intell. Transp. Syst., 2017, vol. 19, no. 2, pp. 582–597.CrossRef
12.
Zong, W., Zhang, C., Wang, Z., Zhu, J., and Chen, Q., Architecture design and implementation of an autonomous vehicle, IEEE Access, 2018, vol. 6, pp. 21956–21970.CrossRef
13.
Wang, C., Sun, Q., Li, Z., Zhang, H., and Ruan, K., Cognitive competence improvement for autonomous vehicles: A lane change identification model for distant preceding vehicles, IEEE Access, 2019, vol. 7, pp. 83229–83242.CrossRef
14.
Artunedo, A., Villagra, J., Godoy, J., and del Castillo, M.D., Motion planning approach considering localization uncertainty, IEEE Trans. Veh. Technol., 2020, vol. 69, no. 6, pp. 5983–5994.CrossRef
15.
Okumura, B., James, M.R., Kanzawa, Y., Derry, M., Sakai, K., Nishi, T., and Prokhorov, D., Challenges in perception and decision making for intelligent automotive vehicles: A case study, IEEE Trans. Intell. Veh., 2016, vol. 1, no, 1, pp. 20–32.CrossRef
16.
Noh, S., Decision-making framework for autonomous driving at road intersections: Safeguarding against collision, overly conservative behavior, and violation vehicles, IEEE Trans. Ind. Electron., 2018, vol. 66, no. 4, pp. 3275–3286.CrossRef
17.
Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, MIT Press, 2016.MATH
18.
Feng, D., Haase-Schütz, C., Rosenbaum, L., Hertlein, H., Glaeser, C., Timm, F., Wiesbeck, W., and Dietmayer, K., Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges, IEEE Trans. Intell. Transp. Syst., 2020, vol. 22, no. 3, pp. 1341–1360.CrossRef
19.
Zou, Q., Jiang, H., Dai, Q., Yue, Y., Chen, L., and Wang, Q., Robust lane detection from continuous driving scenes using deep neural networks, IEEE Trans. Veh. Technol., 2019, vol. 69, no. 1, pp. 41–54.CrossRef
20.
Chen, X., Kundu, K., Zhu, Y., Ma, H., Fidler, S., and Urtasun, R., 3d object proposals using stereo imagery for accurate object class detection, IEEE Trans. Pattern Anal. Mach. Intell., 2017, vol. 40, no. 5, pp. 1259–1272.CrossRef
21.
Li, W., Qu, Z., Song, H., Wang, P., and Xue, B., The traffic scene understanding and prediction based on image captioning, IEEE Access, 2020, vol. 9, pp. 1420–1427.CrossRef
22.
Li, Y., Wang, H., Dang, L.M., Nguyen, T.N., Han, D., Lee, A., Jang, I., and Moon, H., A deep learning-based hybrid framework for object detection and recognition in autonomous driving, IEEE Access, 2020, vol. 8, pp. 194228–194239.CrossRef
23.
Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M., Yolov4: Optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934, 2020.
24.
Cao, Z., Simon, T., Wei, S.E., and Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2017, pp. 7291–7299.
25.
Adadi, A. and Berrada, M., Peeking inside the black-box: a survey on explainable artificial intelligence (XAI), IEEE Access, 2018, vol. 6, pp. 52138–52160.CrossRef
26.
Cai, Y., Luan, T., Gao, H., Wang, H., Chen, L., Li, Y., Sotelo, M.A., and Li, Z., YOLOv4-5D: An effective and efficient object detector for autonomous driving, IEEE Trans. Instrum. Meas., 2021, vol. 70, pp. 1–13.
27.
Ren, S., He, K., Girshick, R., and Sun, J., Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 2016, vol. 39, no. 6, pp. 1137–1149.CrossRef
28.
Cai, Z. and Vasconcelos, N., Cascade r-cnn: Delving into high quality object detection, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018, pp. 6154–6162.
29.
Liu, Y., Yixuan, Y., and Liu, M., Ground-aware monocular 3d object detection for autonomous driving, IEEE Rob. Autom. Lett., 2021, vol. 6, no. 2, pp. 919–926.CrossRef
30.
Wei, J., He, J., Zhou, Y., Chen, K., Tang, Z., and Xiong, Z., Enhanced object detection with deep convolutional neural networks for advanced driving assistance, IEEE Trans. Intell. Transp. Syst., 2019, vol. 21, no. 4, pp. 1572–1583.CrossRef
31.
Chen, L., Zhan, W., Tian, W., He, Y., and Zou, Q., Deep integration: A multi-label architecture for road scene recognition, IEEE Trans. Image Process., 2019, vol. 28, no. 10, pp. 4883–4898.MathSciNetMATHCrossRef
32.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L., Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans. Pattern Anal. Mach. Intell., 2017, vol. 40, no. 4, pp. 834–848.CrossRef
33.
Chen, L.C., Papandreou, G., Schroff, F., and Adam, H., Rethinking atrous convolution for semantic image segmentation, arXiv preprint arXiv:1706.05587, 2017.
34.
Li, G., Xie, H., Yan, W., Chang, Y., and Qu, X., Detection of road objects with small appearance in images for autonomous driving in various traffic situations using a deep learning based approach, IEEE Access, 2020, vol. 8, pp. 211164–211172.CrossRef
35.
Dominguez-Sanchez, A., Cazorla, M., and Orts-Escolano, S., Pedestrian movement direction recognition using convolutional neural networks, IEEE Trans. Intell. Transp. Syst., 2017, vol. 18, no. 12, pp. 3540–3548.CrossRef
36.
Gupta, A. and Choudhary, A., A framework for camera-based real-time lane and road surface marking detection and recognition, IEEE Trans. Intell. Veh., 2018, vol. 3, no. 4, pp. 476–485.CrossRef
37.
Seo, E., Lee, S., Shin, G., Yeo, H., Lim, Y., and Choi, G., Hybrid tracker based optimal path tracking system of autonomous driving for complex road environments, IEEE Access, 2021, vol. 9, pp. 71763–71777.CrossRef
38.
Zhang, Y., Wang, J., Wang, X., and Dolan, J.M., Road-segmentation-based curb detection method for self-driving via a 3D-LiDAR sensor, IEEE Trans. Intell. Transp. Syst., 2018, vol. 19, no. 12, pp. 3981–3991.CrossRef
39.
Thrun, S., Burgard, W., and Fox, D., Probabilistic Robotics, USA: Massachusetts Inst. of Technology, 2005.MATH
40.
Xu, Z., Sun, Y., and Liu, M., iCurb: Imitation learning-based detection of road curbs using aerial images for autonomous driving, IEEE Rob. Autom. Lett., 2021, vol. 6, no. 2, pp. 1097–1104.CrossRef
41.
Cai, P., Wang, S., Sun, Y., and Liu, M., Probabilistic end-to-end vehicle navigation in complex dynamic environments with multimodal sensor fusion, IEEE Rob. Autom. Lett. 2020, vol. 5, no. 3, pp. 4218–4224.
42.
Lin, C., Tian, D., Duan, X., and Zhou, J., 3D Environmental perception modeling in the simulated autonomous-driving systems, Complex Syst. Model. Simul., 2021, vol. 1, no. 1, pp. 45–54.CrossRef
43.
Ku, J., Mozifian, M., Lee, J., Harakeh, A., and Waslander, S.L., Joint 3d proposal generation and object detection from view aggregation, in 2018 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE, 2018, pp. 1–8.
44.
Zhang, L., Li, Y., and Nevatia, R., Global data association for multi-object tracking using network flows, in 2008 IEEE Conf. on Computer Vision and Pattern Recognition, IEEE, 2008, pp. 1–8.
45.
Yoon, K., Kim, D.Y., Yoon, Y.C., and Jeon, M., Data association for multi-object tracking via deep neural networks, Sensors, 2019, vol. 19, no. 3, p. 559.CrossRef
46.
Hu, H.N., Cai, Q.Z., Wang, D., Lin, J., Sun, M., Krahenbuhl, P., Darrell, T., and Yu, F., Joint monocular 3D vehicle detection and tracking, in Proc. of the IEEE/CVF Int. Conf. on Computer Vision, 2019, pp. 5390–5399.
47.
Zhou, X., Koltun, V., and Krähenbühl, P., Tracking objects as points, in European Conf. on Computer Vision, Cham: Springer, 2020, pp. 474–490.
48.
Li, Q., Hu, R., Wang, Z., and Ding, Z., Driving behavior-aware network for 3D object tracking in complex traffic scenes, IEEE Access, 2021, vol. 9, pp. 51550–51560.CrossRef
49.
Leal-Taixé, L., Canton-Ferrer, C., and Schindler, K., Learning by tracking: Siamese CNN for robust target association, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2016, pp. 33–40.
50.
Kong, X., Xin, B., Wang, Y., and Hua, G., Collaborative deep reinforcement learning for joint object search, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2017, pp. 1695–1704.
51.
Yu, F., Wang, D., Shelhamer, E., and Darrell, T., Deep layer aggregation, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018, pp. 2403–2412.
52.
Li, Y., Møgelmose, A., and Trivedi, M.M., Pushing the “Speed Limit”: high-accuracy US traffic sign recognition with convolutional neural networks, IEEE Trans. Intell. Veh., 2016, vol. 1, no. 2, pp. 167–176.CrossRef
53.
Chollet, F., Xception: Deep learning with depthwise separable convolutions, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2017, pp. 1251–1258.
54.
Bangquan, X. and Xiong, W.X., Real-time embedded traffic sign recognition using efficient convolutional neural network, IEEE Access, 2019, vol. 7, pp. 53330–53346.CrossRef
55.
He, K., Gkioxari, G., Dollár, P., and Girshick, R., Mask r-cnn, in Proc. of the IEEE Int. Conf. on Computer Vision, 2017, pp. 2961–2969.
56.
Serna, C.G. and Ruichek, Y., Traffic signs detection and classification for European urban environments, IEEE Trans. Intell. Transp. Syst., 2019, vol. 21, no. 10, pp. 4388–4399.CrossRef
57.
Mannan, A., Javed, K., Rehman, A.U., Babri, H.A., and Noon, S.K., Classification of degraded traffic signs using flexible mixture model and transfer learning, IEEE Access, 2019, vol. 7, pp. 148800–148813.CrossRef
58.
Avramović, A., Sluga, D., Tabernik, D., Skočaj, D., Stojnić, V., and Ilc, N., Neural-network-based traffic sign detection and recognition in high-definition images using region focusing and parallelization, IEEE Access, 2020, vol. 8, pp. 189855–189868.CrossRef
59.
Reza, A.M., Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement, J. VLSI Signal Process. Syst. Signal, Image Video Technol., 2004, vol. 38, no. 1, pp. 35–44.CrossRef
60.
Uijlings, J.R., Van de Sande, K.E., Gevers, T., and Smeulders, A.W., Selective search for object recognition, Int. J. Comput. Vision, 2013, vol. 104, no. 2, pp. 154–171.CrossRef
61.
Girshick, R., Donahue, J., Darrell, T., and Malik, J., Rich feature hierarchies for accurate object detection and semantic segmentation, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 580–587.
62.
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., and Ramanan, D., Object detection with discriminatively trained part-based models, IEEE Trans. Pattern Anal. Mach. Intell., 2009, vol. 32, no. 9, pp. 1627–1645.CrossRef
63.
Møgelmose, A., Liu, D., and Trivedi, M.M., Detection of US traffic signs, IEEE Trans. Intell. Transp. Syst., 2015, vol. 16, no. 6, pp. 3116–3125.CrossRef
64.
Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., and Igel, C., Detection of traffic signs in real-world images: The German traffic sign detection benchmark, in 2013 Int. Joint Conf. on Neural Networks (IJCNN), IEEE, 2013, pp. 1–8.
65.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A., You only look once: Unified, real-time object detection, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2016, pp. 779–788.
66.
Tabernik, D. and Skočaj, D., Deep learning for large-scale traffic-sign detection and recognition, IEEE Trans. Intell. Transp. Syst., 2019, vol. 21, no. 4, pp. 1427–1440.CrossRef
67.
Zhang, X., Chen, Z., Wu, Q.J., Cai, L., Lu, D., and Li, X., Fast semantic segmentation for scene perception, IEEE Trans. Ind. Inform., 2018, vol. 15, no. 2, pp. 1183–1192.CrossRef
68.
Wang, W., Fu, Y., Pan, Z., Li, X., and Zhuang, Y., Real-time driving scene semantic segmentation, IEEE Access, 2020, vol. 8, pp. 36776–36788.CrossRef
69.
Long, J., Shelhamer, E., and Darrell, T., Fully convolutional networks for semantic segmentation, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 3431–3440.
70.
Badrinarayanan, V., Kendall, A., and Cipolla, R., Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 2017, vol. 39, no. 12, pp. 2481–2495.CrossRef
71.
Badrinarayanan, V., Handa, A., and Cipolla, R., Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, arXiv preprint arXiv:1505.07293, 2015.
72.
Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556, 2014.
73.
Ronneberger, O., Fischer, P., and Brox, T., U-net: Convolutional networks for biomedical image segmentation, in Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, Cham: Springer, 2015, pp. 234–241.
74.
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J., Pyramid scene parsing network, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2017, pp. 2881–2890.
75.
Yu, F. and Koltun, V., Multi-scale context aggregation by dilated convolutions, arXiv preprint arXiv:1511.07122, 2015.
76.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L., Semantic image segmentation with deep convolutional nets and fully connected crfs, arXiv preprint arXiv:1412.7062, 2014.
77.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S., Feature pyramid networks for object detection, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2017, pp. 2117–2125.
78.
Chaurasia, A. and Culurciello, E., December. Linknet: Exploiting encoder representations for efficient semantic segmentation, in 2017 IEEE Visual Communications and Image Processing (VCIP), IEEE, 2017, pp. 1–4.
79.
Wu, C., Cheng, H.P., Li, S., Li, H., and Chen, Y., ApesNet: a pixel-wise efficient segmentation network for embedded devices, IET Cyber.-Phys. Syst.: Theory Appl., 2016, vo.1, no. 1, pp. 78-85.
80.
Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E., Enet: A deep neural network architecture for real-time semantic segmentation, arXiv preprint arXiv:1606.02147, 2016.
81.
Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., and Hajishirzi, H., Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation, in Proc. of the European Conf. on Computer Vision (ECCV), 2018, pp. 552–568.
82.
Kim, J. and Heo, Y.S., Efficient semantic segmentation using spatio-channel dilated convolutions, IEEE Access, 2019, vol. 7, pp. 154239–154252.CrossRef
83.
Lo, S.Y., Hang, H.M., Chan, S.W., and Lin, J.J., Efficient dense modules of asymmetric convolution for real-time semantic segmentation, in Proc. of the ACM Multimedia Asia, 2019, pp. 1–6.
84.
Watanabe, S., Pattern Recognition: Human and Mechanical, Wiley, 1985.
85.
Altun, M. and Celenk, M., Road scene content analysis for driver assistance and autonomous driving, IEEE Trans. Intell. Transp. Syst., 2017, vol. 18, no. 12, pp. 3398–3407.CrossRef
86.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y., Generative adversarial nets, in Proc. of the 27th Int. Conf. on Neural Information Processing Systems, Cambridge, MA, USAL MIT Press, 2014, vol. 2, pp. 2672–2680.
87.
Mirza, M. and Osindero, S., Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784, 2014.
88.
Han, X., Lu, J., Zhao, C., You, S., and Li, H., Semisupervised and weakly supervised road detection based on generative adversarial networks, IEEE Signal Process. Lett., 2018, vol. 25, no. 4, pp. 551–555.CrossRef
89.
Yan, F., Wang, K., Zou, B., Tang, L., Li, W., and Lv, C., LiDAR-based multi-task road perception network for autonomous vehicles, IEEE Access, 2020, vol. 8, pp. 86753–86764.CrossRef
90.
Sun, L., Yang, K., Hu, X., Hu, W., and Wang, K., Real-time fusion network for RGB-D semantic segmentation incorporating unexpected obstacle detection for road-driving images, IEEE Rob. Autom. Lett., 2020, vol. 5, no. 4, pp. 5558–5565.CrossRef
91.
Huang, Z., Lv, C., Xing, Y., and Wu, J., Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding, IEEE Sens. J., 2020, vol. 21, no. 10, pp. 11781–11790.CrossRef
92.
Pei, Y., Sun, B., and Li, S., Multifeature selective fusion network for real-time driving scene parsing, IEEE Trans. Instrum. Meas.t, 2021, vol. 70, pp. 1–12.
93.
He, K., Zhang, X., Ren, S., and Sun, J., Deep residual learning for image recognition, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2016, pp. 770–778.
94.
Zhu, Z., Xu, M., Bai, S., Huang, T., and Bai, X., Asymmetric non-local neural networks for semantic segmentation, in Proc. of the IEEE/CVF Int. Conf. on Computer Vision, 2019, pp. 593–602.
95.
Sun, Y., Zuo, W., Huang, H., Cai, P., and Liu, M., PointMoSeg: Sparse tensor-based end-to-end moving-obstacle segmentation in 3D lidar point clouds for autonomous driving, IEEE Rob. Autom. Lett., 2020, vol. 6, no. 2, pp. 510–517.CrossRef
96.
Choy, C., Park, J., and Koltun, V., Fully convolutional geometric features, in Proc. of the IEEE/CVF Int. Conf. on Computer Vision, 2019, pp. 8958–8966.
97.
Graham, B., Engelcke, M., and Van der Maaten, L., 3d semantic segmentation with submanifold sparse convolutional networks, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018, pp. 9224–9232.
98.
Choy, C., Gwak, J., and Savarese, S., 4d spatio-temporal convnets: Minkowski convolutional neural networks, in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2019, pp. 3075–3084.
99.
Kumar, V.R., Yogamani, S., Rashed, H., Sitsu, G., Witt, C., Leang, I., Milz, S., and Mäder, P., Omnidet: Surround view cameras based multi-task visual perception network for autonomous driving, IEEE Rob. Autom. Lett., 2021, vol. 6, no. 2, pp. 2830–2837.CrossRef
100.
Fritsch, J., Kuehnl, T., and Geiger, A., A new performance measure and evaluation benchmark for road detection algorithms, in 16th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013, pp. 1693–1700.
101.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B., The cityscapes dataset for semantic urban scene understanding, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2016, pp. 3213–3223.
102.
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J., Semantickitti: A dataset for semantic scene understanding of lidar sequences, in Proc. of the IEEE/CVF Int. Conf. on Computer Vision, 2019, pp. 9297–9307.
103.
Shin, M.O., Oh, G.M., Kim, S.W., and Seo, S.W., Real-time and accurate segmentation of 3-D point clouds based on Gaussian process regression, IEEE Trans. Intell. Transp. Syst., 2017, vol. 18, no. 12, pp. 3363–3377.CrossRef
104.
Williams, C.K., and Rasmussen, C.E., Gaussian Processes for Machine Learning, Cambridge, MA: MIT press, 2006.MATH
105.
Geiger, A., Lenz, P., Stiller, C., and Urtasun, R., Vision meets robotics: The kitti dataset, Int. J. Rob. Res., 2013, vol. 32, no. 11, pp. 1231–1237.CrossRef
106.
Brostow, G.J., Shotton, J., Fauqueur, J., and Cipolla, R., Segmentation and recognition using structure from motion point clouds, in European Conf. on computer vision, Berlin, Heidelberg: Springer, 2008, pp. 44–57.
107.
Hussain Raza, S., Grundmann, M., and Essa, I., Geometric context from videos, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2013, pp. 3081–3088.
108.
Brostow, G.J., Fauqueur, J., and Cipolla, R., Semantic object classes in video: A high-definition ground truth database, Pattern Recognit. Lett., 2009, vol. 30, no. 2, pp. 88–97.CrossRef
109.
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O., Nuscenes: A multimodal dataset for autonomous driving, in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2020, pp. 11621–11631.
110.
Mudassar, B.A., Saha, P., Long, Y., Amir, M.F., Gebhardt, E., Na, T., Ko, J.H., Wolf, M., and Mukhopadhyay, S., Camel: An adaptive camera with embedded machine learning-based sensor parameter control, IEEE J. Emerging Selected Top. Circuits Syst., 2019, vol. 9, no. 3, pp. 498–508.CrossRef
111.
Kleinfelder, S., Lim, S., Liu, X., and El Gamal, A., A 10000 frames/s CMOS digital pixel sensor, IEEE J. Solid-State Circuits, 2001, vol. 36, no. 12, pp. 2049–2059.CrossRef
112.
Skorka, O. and Joseph, D., CMOS digital pixel sensors: Technology and applications, in Nanosensors, Biosensors, and Info-Tech Sensors and Systems 2014, International Society for Optics and Photonics, 2014, vol. 9060, p. 90600G.
113.
Tu, C., Takeuchi, E., Carballo, A., and Takeda, K., May. Point cloud compression for 3d lidar sensor using recurrent neural network with residual blocks, in 2019 Int. Conf. on Robotics and Automation (ICRA), IEEE, 2019, pp. 3274–3280.
114.
Mehra, A., Mandal, M., Narang, P., and Chamola, V., ReViewNet: A fast and resource optimized network for enabling safe autonomous driving in hazy weather conditions, IEEE Trans. Intell. Transp. Syst., 2020.
115.
Qu, Y., Chen, Y., Huang, J., and Xie, Y., Enhanced pix2pix dehazing network, in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition, 2019, pp. 8160–8168.
116.
Engin, D., Genç, A., and Kemal Ekenel, H., Cycle-dehaze: Enhanced cyclegan for single image dehazing, in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2018, pp. 825–833.
117.
Mehta, A., Sinha, H., Narang, P., and Mandal, M., Hidegan: A hyperspectral-guided image dehazing gan, in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, 2020, pp. 212–213.
118.
Chen, B. and Wornell, G.W., Quantization index modulation: A class of provably good methods for digital watermarking and information embedding, IEEE Trans. Inform. Theory, 2001, vol. 47, no. 4, pp. 1423–1443.MathSciNetMATHCrossRef
119.
Chen, B. and Wornell, G.W., Quantization index modulation methods for digital watermarking and information embedding of multimedia, J. VLSI Signal Process. Syst. Signal, Image Video Technol., 2001, vol. 27, no. 1, pp. 7–33.MATHCrossRef
120.
Malik, H., Subbalakshmi, K.P., and Chandramouli, R., Nonparametric steganalysis of QIM data hiding using approximate entropy, in Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, International Society for Optics and Photonics, 2008, vol. 6819, p. 681914.
121.
Malik, H., Chandramouli, R., and Subbalakshmi, K.P., Steganalysis: Trends and challenges, in Multimedia Forensics and Security, IGI Global, 2009, pp. 245–265.
122.
Changalvala, R. and Malik, H., LiDAR data integrity verification for autonomous vehicle, IEEE Access, 2019, vol. 7, pp. 138018–138031.CrossRef
123.
Bresson, G., Alsayed, Z., Yu, L., and Glaser, S., Simultaneous localization and mapping: A survey of current trends in autonomous driving, IEEE Trans. Intell. Veh., 2017, vol. 2, no. 3, pp. 194–220.CrossRef
124.
Wang, H., Wang, C., and Xie, L., Intensity-SLAM: Intensity Assisted localization and mapping for large scale environment, IEEE Rob. Autom. Lett., 2021, vol. 6, no. 2, pp. 1715–1721.CrossRef
125.
White, C.E., Bernstein, D., and Kornhauser, A.L., Some map matching algorithms for personal navigation assistants, Transp. Res., Part C: Emerging Technol., 2000, vol. 8, no. 1–6, pp. 91–108.CrossRef
126.
El Najjar, M.E. and Bonnifait, P., A road-matching method for precise vehicle localization using belief theory and kalman filtering, Autonom. Rob., 2005, vol. 19, no. 2, pp. 173–191.
127.
Newson, P. and Krumm, J., Hidden Markov map matching through noise and sparseness, in Proc. of the 17th ACM SIGSPATIAL Int. Conf. on advances in geographic information systems, 2009, pp. 336–343.
128.
Ballardini, A.L., Cattaneo, D., Izquierdo, R., Parra, I., Sotelo, M.A., and Sorrenti, D.G., Ego-lane estimation by modeling lanes and sensor failures, in 2017 IEEE 20th Int. Conf. on Intelligent Transportation Systems (ITSC), IEEE, 2017, pp. 1–7.
129.
Popescu, V., Danescu, R., and Nedevschi, S., On-road position estimation by probabilistic integration of visual cues, in 2012 IEEE Intelligent Vehicles Symposium, IEEE, 2012, pp. 583–589.
130.
Jo, K., Chu, K., and Sunwoo, M., Interacting multiple model filter-based sensor fusion of GPS with in-vehicle sensors for real-time vehicle positioning, IEEE Trans. Intell. Transp. Syst., 2011, vol. 13, no. 1, pp. 329–343.CrossRef
131.
Gwak, M., Jo, K., and Sunwoo, M., Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles, Int. J. Autom. Technol., 2013, vol. 14, no. 2, pp. 265–274.CrossRef
132.
Gustafsson, F., Particle filter theory and practice with positioning applications, IEEE Aerosp. Electron. Syst. Mag., 2010, vol. 25, no. 7, pp. 53–82.CrossRef
133.
Klein, G. and Murray, D., Parallel tracking and mapping for small AR workspaces, in 2007 6th IEEE and ACM Int. Symposium on Mixed and Augmented Reality, IEEE, 2007, pp. 225–234.
134.
Mur-Artal, R. and Tardós, J.D., Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras, IEEE Trans. Rob., 2017, vol. 33, no. 5, pp. 1255–1262.CrossRef
135.
Ferrera, M., Eudes, A., Moras, J., Sanfourche, M., and Le Besnerais, G., OV $^{2} $ SLAM: A fully online and versatile visual SLAM for real-time applications, IEEE Rob. Autom. Lett., 2021, vol. 6, no. 2, pp. 1399–1406.CrossRef
136.
Angeli, A., Filliat, D., Doncieux, S., and Meyer, J.A., Fast and incremental method for loop-closure detection using bags of visual words, IEEE Trans. Rob., 2008, vol. 24, no. 5, pp. 1027–1037.CrossRef
137.
Nicosevici, T. and Garcia, R., Automatic visual bag-of-words for online robot navigation and mapping, IEEE Trans. Rob., 2012, vol. 28, no. 4, pp. 886–898.CrossRef
138.
Garcia-Fidalgo, E. and Ortiz, A., ibow-lcd: An appearance-based loop-closure detection approach using incremental bags of binary words, IEEE Rob. Autom. Lett., 2018, vol. 3, no. 4, pp. 3051–3057.CrossRef
139.
Nobis, F., Papanikolaou, O., Betz, J., and Lienkamp, M., Persistent map saving for visual localization for autonomous vehicles: An ORB-SLAM 2 extension, in 2020 Fifteenth Int. Conf. on Ecological Vehicles and Renewable Energies (EVER), IEEE, 2020, pp. 1–9.
140.
Kasmi, A., Laconte, J., Aufrère, R., Denis, D., and Chapuis, R., End-to-end probabilistic ego-vehicle localization framework, IEEE Trans. Intell. Veh., 2020, vol. 6, no. 1, pp. 146–158.CrossRef
141.
Ramm, F., Topf, J., and Chilton, S., OpenStreetMap: using and enhancing the free map of the world, SoC Bulletin, Cambridge: UIT Cambridge, 2011, vol. 45, p. 55.
142.
Rozenberszki, D. and Majdik, A.L., May. LOL: Lidar-only odometry and localization in 3D point cloud maps, in 2020 IEEE Int. Conf. on Robotics and Automation (ICRA), IEEE, 2020, pp. 4379–4385.
143.
Koide, K., Miura, J., and Menegatti, E., A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement, Int. J. Adv. Rob. Syst. 2019, vol. 16, no. 2, p. 1729881419841532.
144.
Chetverikov, D., Svirko, D., Stepanov, D., and Krsek, P., The trimmed iterative closest point algorithm, in Object Recognition Supported by User Interaction for Service Robots, IEEE, 2002, vol. 3, pp. 545–548.
145.
Zhang, J. and Singh, S., LOAM: Lidar odometry and mapping in real-time, in Robotics: Science and Systems, 2014, vol. 2, no. 9.
146.
Shan, T. and Englot, B., Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain, in 2018 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), IEEE, 2018, pp. 4758–4765.
147.
Elfes, A., Using occupancy grids for mobile robot perception and navigation, Computer, 1989, vol. 22, no. 6, pp. 46–57.CrossRef
148.
Levinson, J., Montemerlo, M., and Thrun, S., Map-based precision vehicle localization in urban environments, in Robotics: Science and Systems, 2007.MATH
149.
Castorena, J. and Agarwal, S., Ground-edge-based LIDAR localization without a reflectivity calibration for autonomous driving, IEEE Rob. Autom. Lett., 2017, vol. 3, no. 1, pp. 344–351.CrossRef
150.
de Paula Veronese, L., Auat-Cheein, F., Mutz, F., Oliveira-Santos, T., Guivant, J.E., de Aguiar, E., Badue, C., and De Souza, A.F., Evaluating the limits of a LiDAR for an autonomous driving localization, IEEE Trans. Intell. Transp. Syst., 2020, vol. 22, no. 3, pp. 1449–1458.CrossRef
151.
Dubé, R., Gollub, M.G., Sommer, H., Gilitschenski, I., Siegwart, R., Cadena, C., and Nieto, J., Incremental-segment-based localization in 3-d point clouds, IEEE Rob. Autom. Lett., 2018, vol. 3, no. 3, pp. 1832–1839.CrossRef
152.
Whelan, T., Ma, L., Bondarev, E., de With, P.H.N., and McDonald, J., Incremental and batch planar simplification of dense point cloud maps, Rob. Autonom. Syst., 2015, vol. 69, pp. 3–14.CrossRef
153.
Lu, W., Wan, G., Zhou, Y., Fu, X., Yuan, P., and Song, S., DeepICP: An end-to-end deep neural network for 3D point cloud registration, IEEE Int. Conf. on Computer Vision (ICCV), 2019, pp. 12–21.
154.
Khan, S., Wollherr, D., and Buss, M., Modeling laser intensities for simultaneous localization and mapping, IEEE Rob. Autom. Lett., 2016, vol. 1, no. 2, pp. 692–699.CrossRef
155.
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., and Leonard, J.J., Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age, IEEE Trans. Rob., 2016, vol. 32, no. 6, pp. 1309–1332.CrossRef
156.
Kohlbrecher, S., Von Stryk, O., Meyer, J., and Klingauf, U., A flexible and scalable SLAM system with full 3D motion estimation, in 2011 IEEE Int. Symposium on Safety, Security, and Rescue Robotics, IEEE, 2011, pp. 155–160.
157.
Sun, L., Zhao, J., He, X., and Ye, C., June. Dlo: Direct lidar odometry for 2.5 d outdoor environment, in 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2018, pp. 1–5.
158.
Li, J., Zhao, J., Kang, Y., He, X., Ye, C., and Sun, L., DL-SLAM: Direct 2.5 D LiDAR SLAM for Autonomous Driving, in 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019, pp. 1205–1210.
Metadaten
Titel
Advanced Techniques for Perception and Localization in Autonomous Driving Systems: A Survey
verfasst von
Qusay Sellat
Kanagachidambaresan Ramasubramanian
Publikationsdatum
01.06.2022
Verlag
Pleiades Publishing
Erschienen in
Optical Memory and Neural Networks / Ausgabe 2/2022
Print ISSN: 1060-992X
Elektronische ISSN: 1934-7898
DOI
https://doi.org/10.3103/S1060992X22020084

Weitere Artikel der Ausgabe 2/2022

Optical Memory and Neural Networks 2/2022 Zur Ausgabe

OriginalPaper

Performance Analysis of a DPSK Modulated Ultra-Dense WDM System at Different Bit Rates

OriginalPaper

Impact of Hybrid Optical Amplifier for Super Dense Optical Wireless Communication System for Least Fluctuations

OriginalPaper

Federated Learning Using Simple Voting Scheme

OriginalPaper

Survey of Computational Modeling of the Functional Parts of the Brain

OriginalPaper

Four Frequencies Filtering By the One-Dimensional Photonic Defectives Star Waveguides Structure

OriginalPaper

Skin Cancer Severity Prediction Model Based on Modified Deep Neural Network with Horse Herd Optimization

Premium Partner

    Bildnachweise