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A Survey of Scene Understanding by Event Reasoning in Autonomous Driving

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Abstract

Realizing autonomy is a hot research topic for automatic vehicles in recent years. For a long time, most of the efforts to this goal concentrate on understanding the scenes surrounding the ego-vehicle (autonomous vehicle itself). By completing low-level vision tasks, such as detection, tracking and segmentation of the surrounding traffic participants, e.g., pedestrian, cyclists and vehicles, the scenes can be interpreted. However, for an autonomous vehicle, low-level vision tasks are largely insufficient to give help to comprehensive scene understanding. What are and how about the past, the on-going and the future of the scene participants? This deep question actually steers the vehicles towards truly full automation, just like human beings. Based on this thoughtfulness, this paper attempts to investigate the interpretation of traffic scene in autonomous driving from an event reasoning view. To reach this goal, we study the most relevant literatures and the state-of-the-arts on scene representation, event detection and intention prediction in autonomous driving. In addition, we also discuss the open challenges and problems in this field and endeavor to provide possible solutions.

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References

  1. M. M. Waldrop. Autonomous vehicles: No drivers required. Nature, vol.518, no.7537, pp.20–23, 2015. DOI: 10.1038/518020a.

    Article  Google Scholar 

  2. J. Mervis. Are We Going Too Fast on Driverless Cars? http://www.sciencemag.org/news/2017/12/are-wegoing-too-fast-driverless-cars, December 14, 2017.

    Google Scholar 

  3. Y. Y. Zheng, J. Yao. Multi-angle face detection based on DP-adaboost. International Journal of Automation and Computing, vol.12, no.4, pp.421–431, 2015. DOI: 10.1007/s11633-014-0872-8.

    Article  Google Scholar 

  4. H. G. Ren, W. M. Liu, T. Shi, F. J. Li. Compressive tracking based on online Hough forest. International Journal of Automation and Computing, vol. 14, no.4, pp.396–406, 2017. DOI: 10.1007/s11633-017-1083-x.

    Article  Google Scholar 

  5. J. W. Fang, H. K. Xu, Q. Wang, T. J. Wu. Online hash tracking with spatio-temporal saliency auxiliary. Computer Vision and Image Understanding, vol. 160, pp. 57–72, 2017. DOI: 10.1016/j.cviu.2017.03.006.

    Article  Google Scholar 

  6. S. Arumugadevi, V. Seenivasagam. Color image segmentation using feedforward neural networks with FCM. International Journal ofAutomation and Computing, vol. 13, no. 5, pp. 491–500, 2016. DOI: 10.1007/s11633-016-0975-5.

    Article  Google Scholar 

  7. J. F. Bonnefon, A. Shariff, I. Rahwan. The social dilemma of autonomous vehicles. Science, vol. 352, no. 6293, pp. 1573–1576, 2016. DOI: 10.1126/science.aaf2654.

    Article  Google Scholar 

  8. J. Janai, F. Guney, A. Behl, A. Geiger. Computer vision for autonomous vehicles: Problems, datasets and state-ofthe-art. arXiv:1704.05519, 2017.

    Google Scholar 

  9. J. R. Xue, D. Wang, S. Y. Du, D. X. Cui, Y. Huang, N. N. Zheng. A vision-centered multi-sensor fusing approach to self-localization and obstacle perception for robotic cars. Frontiers of Information Technology & Electronic Engineering, vol. 18, no. 1, pp. 122–138, 2017. DOI: 10.1631/FITEE. 1601873.

    Article  Google Scholar 

  10. H. Zhu, K. V. Yuen, L. Mihaylova, H. Leung. Overview of environment perception for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 10, pp. 2584–2601, 2017. DOI: 10.1109/TITS.2017.2658662.

    Article  Google Scholar 

  11. D. L. Waltz. Understanding scene descriptions as event simulations. In Proceedings of the 18th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, Philadelphia, USA, pp.7–11, 1980. DOI: 10.3115/981436.981439.

    Chapter  Google Scholar 

  12. Y. Q. Hou, S. Hornauer, K. Zipser. Fast recurrent fully convolutional networks for direct perception in autonomous driving. arXiv:1711.06459, 2017.

    Google Scholar 

  13. H. Z. Xu, Y. Gao, F. Yu, T. Darrell. End-to-end learning of driving models from large-scale video datasets. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp.3530–3538, 2017. DOI: 10.1109/CVPR.2017.376.

    Google Scholar 

  14. T. Fernando, S. Denman, S. Sridharan, C. Fookes. Going deeper: Autonomous steering with neural memory networks. In Proceedings of IEEE International Conference on Computer Vision Workshop, IEEE, Venice, Italy, pp.214–221, 2017. DOI: 10.1109/ICCVW.2017.34.

    Google Scholar 

  15. C. Thorpe, M. H. Hebert, T. Kanade, S. A. Shafer. Vision and navigation for the Carnegie-Mellon Navlab. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 3, pp. 362–372, 1988. DOI: 10.1109/34.3900.

    Article  Google Scholar 

  16. M. Buehler, K. Iagnemma, S. Singh. The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, Berlin, Heidelberg, Germany: Springer, 2009. DOI: 10.1007/9783-642-03991-1.

    Book  Google Scholar 

  17. J. Hooper. From DARPA Grand Challenge 2004DARPA’ s Debacle in The Desert. https://www.popsci.com/scitech/article/2004-06/darpagrand-challenge-2004darpas-debacle-desert, June 4, 2004.

    Google Scholar 

  18. S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L. E. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. Van Niekerk, E. Jensen, P. Alessandrini, G. Bradski, B. Davies, S. Ettinger, A. Kaehle, A. Nefian, P. Mahoney. Stanley: The robot that won the DARPA grand challenge. The 2005 DARPA Grand Challenge, M. Buehler, K. Iagnemma, S. Singh, Eds., Berlin, Heidelberg, Germany: Springer, 2007. DOI: 10.1007/978-3-540-73429-1_1.

    Google Scholar 

  19. A. Geiger, P. Lenz, R. Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Providence, USA, pp. 33543361, 2012. DOI: 10.1109/CVPR.2012.6248074.

    Google Scholar 

  20. G. J. Brostow, J. Fauqueur, R. Cipolla. Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, vol.30, no.2, pp.88, 2009. DOI: 10.1016/j.patrec.2008.04.005.

    Article  Google Scholar 

  21. M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, B. Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 3213–3223, 2016. DOI: 10.1109/CVPR.2016.350.

    Google Scholar 

  22. A. Gaidon, Q. Wang, Y. Cabon, E. Vig. Virtual worlds as proxy for multi-object tracking analysis. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4340–4349, 2016. DOI: 10.1109/CVPR.2016.470.

    Google Scholar 

  23. W. Maddern, G. Pascoe, C. Linegar, P. Newman. 1 year, 1000 km: The Oxford RobotCar dataset. International Journal of Robotics Research, vol. 36, no. 1, pp. 3–15, 2017. DOI: 10.1177/0278364916679498.

    Article  Google Scholar 

  24. J. V. Dueholm, M. S. Kristoffersen, R. K. Satzoda, E. Ohn-Bar, T. B. Moeslund, M. M. Trivedi. Multiperspective vehicle detection and tracking: Challenges, dataset, and metrics. In Proceedings of the 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, pp.959–964, 2016. DOI: 10.1109/ITSC.2016.7795671.

    Google Scholar 

  25. C. Wang, Y. K. Fang, H. J. Zhao, C. Z. Guo, S. Mita, H. B. Zha. Probabilistic inference for occluded and Multiview on-road vehicle detection. IEEE Transactions on Intelligent Transportation Systems, vol.17, no.1, pp.215–229, 2015. DOI: 10.1109/TITS.2015.2466109.

    Article  Google Scholar 

  26. D. Hoiem, S. K. Divvala, J. H. Hays. Pascal VOC 2008 challenge. World Literature Today, 2009.

    Google Scholar 

  27. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. H. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, F. F. Li. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015. DOI: 10.1007/s11263-015-0816-y.

    Article  MathSciNet  Google Scholar 

  28. A. Milan, L. Leal-Taixe, I. Reid, S. Roth, K. Schindler. MOT16: A benchmark for multi-object tracking. arXiv:1603.00831, 2016.

    Google Scholar 

  29. F. C. Heilbron, V. Escorcia, B. Ghanem, J. C. Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, USA, pp. 961–970, 2015. DOI: 10.1109/CVPR.2015.7298698.

    Google Scholar 

  30. T. Deng, K. F. Yang, Y. J. Li, H. M. Yan. Where does the driver look? Top-down-based saliency detection in a traffic driving environment. IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 7, pp. 2051–2062, 2016. DOI: 10.1109/TITS.2016.2535402.

    Article  Google Scholar 

  31. A. Geiger, M. Lauer, R. Urtasun. A generative model for 3D urban scene understanding from movable platforms. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Colorado Springs, USA, pp. 19451952, 2011. DOI: 10.1109/CVPR.2011.5995641.

    Google Scholar 

  32. J. M. Zhang, S. Sclaroff. Exploiting surroundedness for saliency detection: A Boolean map approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, no.5, pp.889, 2016. DOI: 10.1109/TPAMI.2015.2473844.

    Article  Google Scholar 

  33. L. Zhou, Y. F. Ju, J. W. Fang, J. R. Xue. Saliency detection via background invariance in scale space. Journal of Electronic Imaging, vol.26, no.4, Article number 043021, 2017. DOI: 10.1117/1.JEI.26.4.043021.

    Google Scholar 

  34. Q. Wang, Y. Yuan, P. K. Yan, X. L. Li. Saliency detection by multiple-instance learning. IEEE Transactions on Cybernetics, vol.43, no.2, pp.660–672, 2013. DOI: 10.1109/TSMCB.2012.2214210.

    Article  Google Scholar 

  35. S. F. He, R. W. H. Lau. Exemplar-driven top-down saliency detection via deep association. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 5723–5732, 2016. DOI: 10.1109/CVPR.2016.617.

    Google Scholar 

  36. J. M. Yang, M. H. Yang. Top-down visual saliency via joint CRF and dictionary learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 3, pp. 576–588, 2017. DOI: 10.1109/TPAMI.2016.2547384.

    Article  Google Scholar 

  37. J. T. Pan, E. Sayrol, X. Giro-I-Nieto, K. McGuinness, N. E. O’Connor. Shallow and deep convolutional networks for saliency prediction. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, NV, USA, pp.598–606, 2016. DOI: 10.1109/CVPR.2016.71.

    Google Scholar 

  38. Y. Xia, D. Q. Zhang, A. Pozdnoukhov, K. Nakayama, K. Zipser, D. Whitney. Training a network to attend like human drivers saves it from common but misleading loss functions. arXiv:1711.06406, 2017.

    Google Scholar 

  39. Y. Xie, L. F. Liu, C. H. Li, Y. Y. Qu. Unifying visual saliency with hog feature learning for traffic sign detection. In Proceedings of IEEE Intelligent Vehicles Symposium, IEEE, Xi’an, China, pp. 24–29, 2009. DOI: 10.1109/IVS.2009.5164247.

    Google Scholar 

  40. W. J. Won, M. Lee, J. W. Son. Implementation of road traffic signs detection based on saliency map model. In Proceedings of IEEE Intelligent Vehicles Symposium, IEEE, Eindhoven, Netherlands, pp. 542–547, 2008. DOI: 10.1109/IVS.2008.4621144.

    Google Scholar 

  41. D. D. Wang, X. W. Hou, J. W. Xu, S. G. Yue, C. L. Liu. Traffic sign detection using a cascade method with fast feature extraction and saliency test. IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 12, pp. 32903302, 2017. DOI: 10.1109/TITS.2017.2682181.

    Google Scholar 

  42. J. Kim, S. Kim, R. Mallipeddi, G. Jang, M. Lee. Adaptive driver assistance system based on traffic information saliency map. In Proceedings of International Joint Conference on Neural Networks, IEEE, Vancouver, Canada, pp. 1918–1923, 2016. DOI: 10.1109/IJCNN.2016.7727434.

    Google Scholar 

  43. V. John, K. Yoneda, Z. Liu, S. Mita. Saliency map generation by the convolutional neural network for real-time traffic light detection using template matching. IEEE Transactions on Computational Imaging, vol. 1, no. 3, pp. 159–173, 2015. DOI: 10.1109/TCI.2015.2480006.

    Article  MathSciNet  Google Scholar 

  44. H. L. Kuang, K. F. Yang, L. Chen, Y. J. Li, L. L. H. Chan, H. Yan. Bayes saliency-based object proposal generator for nighttime traffic images. IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 814–825, 2017. DOI: 10.1109/TITS.2017.2702665.

    Article  Google Scholar 

  45. R. Timofte, K. Zimmermann, L. V. Gool. Multi-view traffic sign detection, recognition, and 3D localisation. Machine Vision and Applications, vol.25, no.3, pp.633–647, 2014. DOI: 10.1007/s00138-011-0391-3.

    Article  Google Scholar 

  46. S. Alletto, A. Palazzi, F. Solera, S. Calderara, R. Cucchiara. DR(eye)VE: A dataset for attention-based tasks with applications to autonomous and assisted driving. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE, LasVegas, USA, 2016. DOI: 10.1109/CVPRW.2016.14.

    Google Scholar 

  47. A. Palazzi, F. Solera, S. Calderara, S. Alletto, R. Cucchiara. Where should you attend while driving? arXiv:1611.08215, 2016.

    Google Scholar 

  48. C. Landsiedel, D. Wollherr. Road geometry estimation for urban semantic maps using open data. Advanced Robotics, vol.31, no.5, pp.282–290, 2017. DOI: 10.1080/01691864.2016.1250675.

    Article  Google Scholar 

  49. E. Levinkov, M. Fritz. Sequential Bayesian model update under structured scene prior for semantic road scenes labeling. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Sydney, Australia, pp. 1321–1328, 2013. DOI: 10.1109/ICCV.2013.167.

    Google Scholar 

  50. Z. Y. Zhang, S. Fidler, R. Urtasun. Instance-level segmentation for autonomous driving with deep densely connected MRFs. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 669–677, 2016. DOI: 10.1109/CVPR.2016.79.

    Google Scholar 

  51. T. Cavallari, Semantic Slam: A New Paradigm for Object Recognition and Scene Reconstruction, Ph. D. dissertation, University of Bologna, Italy, 2017.

    Google Scholar 

  52. S. C. Zhou, R. Yan, J. X. Li, Y. K. Chen, H. J. Tang. A brain-inspired SLAM system based on ORB features. International Journal of Automation and Computing, vol. 14, no.5, pp. 564–575, 2017. DOI: 10.1007/s11633-017-1090-y.

    Article  Google Scholar 

  53. B. Zhao, J. S. Feng, X. Wu, S. C. Yan. A survey on deep learning-based fine-grained object classification and semantic segmentation. International Journal of Automation and Computing, vol. 14, no. 2, pp. 119–135, 2017. DOI: 10.1007/s11633-017-1053-3.

    Article  Google Scholar 

  54. H. Kong, J. Y. Audibert, J. Ponce. Vanishing point detection for road detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Miami, USA, pp. 96–103, 2009. DOI: 10.1109/CVPR.2009.5206787.

    Google Scholar 

  55. H. Kong, S. E. Sarma, F. Tang. Generalizing Laplacian of Gaussian filters for vanishing-point detection. IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 408–418, 2013. DOI: 10.1109/TITS.2012.2216878.

    Article  Google Scholar 

  56. J. J. Shi, J. X. Wang, F. F. Fu. Fast and robust vanishing point detection for unstructured road following. IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 4, pp. 970–979, 2016. DOI: 10.1109/TITS.2015.2490556.

    Article  Google Scholar 

  57. J. M. Alvarez, T. Gevers, A. M. Lopez. 3D scene priors for road detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, San Francisco, USA, pp.57–64, 2010. DOI: 10.1109/CVPR.2010.5540228.

    Google Scholar 

  58. E. Casapietra, T. H. Weisswange, C. Goerick, F. Kummert. Enriching a spatial road representation with lanes and driving directions. In Proceedings of the 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, pp. 1579–1585, 2016. DOI: 10.1109/ITSC.2016.7795768.

    Google Scholar 

  59. A. Seff, J. X. Xiao. Learning from maps: Visual common sense for autonomous driving. arXiv:1611.08583, 2016.

    Google Scholar 

  60. M. Y. Liu, S. X. Lin, S. Ramalingam, O. Tuzel. Layered interpretation of street view images. arXiv:1506.04723, 2015.

    Book  Google Scholar 

  61. A. Geiger, M. Lauer, C. Wojek, C. Stiller, R. Urtasun. 3D traffic scene understanding from movable platforms. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 5, pp. 1012–1025, 2014. DOI: 10.1109/TPAMI.2013.185.

    Article  Google Scholar 

  62. A. Ess, T. Mueller, H. Grabner, L. Van Gool. Segmentationbased urban traffic scene understanding. In Proceedings of British Machine Vision Conference, London, UK, 2009. DOI: 10.5244/C.23.84.

    Google Scholar 

  63. B. Kitt, A. Geiger, H. Lategahn. Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. In Proceedings of IEEE Intelligent Vehicles Symposium, IEEE, San Diego, USA, pp. 486–492, 2010. DOI: 10.1109/IVS.2010.5548123.

    Google Scholar 

  64. S. Thrun, W. Burgard, D. Fox. Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), Cambridge, Mass, UK: MIT, 2005.

    MATH  Google Scholar 

  65. H. Y. Zhang, A. Geiger, R. Urtasun. Understanding high-level semantics by modeling traffic patterns. In Proceedings of IEEE Conference on Computer Vision, IEEE, Sydney, Australia, pp. 3056–3063, 2013. DOI: 10.1109/ICCV.2013.379.

    Google Scholar 

  66. C. Y. Chen, A. Seff, A. Kornhauser, J. X. Xiao. Deep-Driving: Learning affordance for direct perception in autonomous driving. In Proceedings of IEEE International Conference on Computer Vision, IEEE, Santiago, Chile, pp.2722–2730, 2015. DOI: 10.1109/ICCV.2015.312.

    Google Scholar 

  67. P. Stahl, B. Donmez, G. A. Jamieson. Anticipation in driving: The role of experience in the efficacy of pre-event conflict cues. IEEE Transactions on Human-Machine Systems, vol.44, no.5, pp.603–613, 2014. DOI: 10.1109/THMS.2014.2325558.

    Article  Google Scholar 

  68. S. J. Pan, Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, 2010. DOI: 10.1109/TKDE.2009.191.

    Article  Google Scholar 

  69. N. Segev, M. Harel, S. Mannor, K. Crammer, R. El-Yaniv. Learn on source, refine on target: A model transfer learning framework with random forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 9, pp.1811–1824, 2017. DOI: 10.1109/TPAMI.2016.2618118.

    Article  Google Scholar 

  70. D. Mitrovic. Reliable method for driving events recognition. IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 2, pp. 198–205, 2005. DOI: 10.1109/TITS.2005.848367.

    Article  Google Scholar 

  71. B. F. Wu, Y. H. Chen, C. H. Yeh, Y. F. Li. Reasoningbased framework for driving safety monitoring using driving event recognition. IEEE Transactions on Intelligent Transportation Systems, vol.14, no. 3, pp. 1231–1241, 2013. DOI: 10.1109/TITS.2013.2257759.

    Google Scholar 

  72. A. Ramirez, E. Ohn-Bar, M. Trivedi. Integrating motion and appearance for overtaking vehicle detection. In Proceedings of IEEE Intelligent Vehicles Symposium Proceedings, IEEE, Dearborn, USA, pp.96–101, 2014. DOI: 10.1109/IVS.2014.6856598.

    Google Scholar 

  73. J. D. Alonso, E. R. Vidal, A. Rotter, M. Muhlenberg. Lane-change decision aid system based on motiondriven vehicle tracking. IEEE Transactions on Vehicular Technology, vol. 57, no. 5, pp. 2736–2746, 2008. DOI: 10.1109/TVT.2008.917220.

    Article  Google Scholar 

  74. Y. Zhu, D. Comaniciu, M. Pellkofer, T. Koehler. Reliable detection of overtaking vehicles using robust information fusion. IEEE Transactions on Intelligent Transportation Systems, vol.7, no.4, pp.401–414, 2006. DOI: 10.1109/TITS.2006.883936.

    Article  Google Scholar 

  75. F. Garcia, P. Cerri, A. Broggi, A. De La Escalera, J. M. Armingol. Data fusion for overtaking vehicle detection based on radar and optical flow. In Proceedings of IEEE Intelligent Vehicles Symposium, IEEE, Alcala de Henares, Spain, pp. 494–499, 2012. DOI: 10.1109/IVS.2012.6232199.

    Google Scholar 

  76. Deutscher Verkehrssicherheitsrat. DVR-Report: Fachmagazin für Verkehrssicherheit. https://www. dvr.de/presse/dvr-report/2017-04.

  77. Auto Club Europa (ACE). Reviere der blinkmuffel. http: //www.ace-online.de/fileadmin/user_uploads/Der_Club/Dokumente/10.07.2008_Grafik_Blinkmuffel_l.pdf.

  78. D. Kasper, G. Weidl, T. Dang, G. Breuel, A. Tamke, A. Wedel, W. Rosenstiel. Object-oriented Bayesian networks for detection of lane change maneuvers. IEEE Intelligent Transportation Systems Magazine, vol.4, no.3, pp.19–31, 2012. DOI: 10.1109/MITS.2012.2203229.

    Article  Google Scholar 

  79. W. Yao, Q. Q. Zeng, Y. P. Lin, D. H. Xu, H. J. Zhao, F. Guillemard, S. Geronimi, F. Aioun. On-road vehicle trajectory collection and scene-based lane change analysis: Part II. IEEE Transactions on Intelligent Transportation Systems, vol.18, no.1, pp.206–220, 2017. DOI: 10.1109/TITS.2016.2571724.

    Article  Google Scholar 

  80. T. Gindele, S. Brechtel, R. Dillmann. A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. In Proceedings of the 13th International Conference on Intelligent Transportation Systems, IEEE, Funchal, Portugal, pp. 1625–1631, 2010. DOI: 10.1109/ITSC.2010.5625262.

    Chapter  Google Scholar 

  81. S. Sivaraman, B. Morris, M. Trivedi. Learning multi-lane trajectories using vehicle-based vision. In Proceedings of IEEE Conference on Computer Vision Workshops, IEEE, Barcelona, Spain, pp. 2070–2076, 2011. DOI: 10.1109/ICCVW. 2011.6130503.

    Google Scholar 

  82. R. K. Satzoda, M. M. Trivedi. Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies. In Proceedings of the 17th International Conference on Intelligent Transportation Systems, IEEE, Qingdao, China, pp.697–702, 2014. DOI: 10.1109/ITSC.2014.6957771.

    Google Scholar 

  83. M. S. Kristoffersen, J. V. Dueholm, R. K. Satzoda, M. M. Trivedi, A. Mogelmose, T. B. Moeslund. Towards semantic understanding of surrounding vehicular maneuvers: A panoramic vision-based framework for realworld highway studies. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE, Las Vegas, USA, pp. 1584–1591, 2016. DOI: 10.1109/CVPRW.2016.197.

    Google Scholar 

  84. J. V. Dueholm, M. S. Kristoffersen, R. K. Satzoda, T. B. Moeslund, M. M. Trivedi. Trajectories and maneuvers of surrounding vehicles with panoramic camera arrays. IEEE Transactions on Intelligent Vehicles, vol. 1, no. 2, pp. 203214, 2016. DOI: 10.1109/TIV.2016.2622921.

    Article  Google Scholar 

  85. A. Khosroshahi, E. Ohn-Bar, M. M. Trivedi. Surround vehicles trajectory analysis with recurrent neural networks. In Proceedings of the 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, pp. 2267–2272, 2016. DOI: 10.1109/ITSC.2016.7795922.

    Google Scholar 

  86. S. Ernst, J. Rieken, M. Maurer. Behaviour recognition of traffic participants by using manoeuvre primitives for automated vehicles in urban traffic. In Proceedings of the 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, 2016. DOI: 10.1109/ITSC.2016.7795674.

    Google Scholar 

  87. S. Busch, T. Schindler, T. Klinger, C. Brenner. Analysis of Spatio-temporal traffic patterns based on pedestrian trajectories. In Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, vol.XLI-B2, pp.497–503, 2016. DOI: 10.5194/isprsarchives-XLI-B2-497-2016.

    Article  Google Scholar 

  88. J. Hariyono, K. H. Jo. Detection of pedestrian crossing road: A study on pedestrian pose recognition. Neurocomputing, vol.234, pp.144–153, 2017. DOI: 10.1016/j.neucom.2016.12.050.

    Google Scholar 

  89. R. M. Mueid, C. Ahmed, M. A. R. Ahad. Pedestrian activity classification using patterns of motion and histogram of oriented gradient. Journal on Multimodal User Interfaces, vol. 10, no. 4, pp. 299–305, 2016. DOI: 10.1007/s12193-015-0178-3.

    Article  Google Scholar 

  90. R. Quintero, I. Parra, D. F. Llorca, M. A. Sotelo. Pedestrian intention and pose prediction through dynamical models and behaviour classification. In Proceedings of the 18th International Conference on Intelligent Transportation Systems, IEEE, Las Palmas, Spain, pp. 83–88, 2015. DOI: 10.1109/ITSC.2015.22.

    Google Scholar 

  91. M. Ogawa, H. Fukamachi, R. Funayama, T. Kindo. CYKLS: Detect pedestrian’s dart focusing on an appearance change. In Proceedings of the 12th International Conference on Computer Vision, Springer-Verlag, Florence, Italy, pp. 556–565, 2012. DOI: 10.1007/978-3-642-33868-7_55.

    Google Scholar 

  92. F. H. Chan, Y. T. Chen, Y. Xiang, M. Sun. Anticipating accidents in dashcam videos. In Proceedings of the 13th Asian Conference on Computer Vision, Springer, Taipei, China, pp. 136–153, 2016. DOI: 10.1007/978-3-319-54190-7_9.

    Google Scholar 

  93. Y. J. Xia, W. W. Xu, L. M. Zhang, X. M. Shi, K. Mao. Integrating 3D structure into traffic scene understanding with RGB-D data. Neurocomputing, vol. 151, pp. 700–709, 2015. DOI: 10.1016/j.neucom.2014.05.091.

    Article  Google Scholar 

  94. A. Tageldin, M. H. Zaki, T. Sayed. Examining pedestrian evasive actions as a potential indicator for traffic conflicts. IET Intelligent Transport Systems, vol.11, no.5, pp.282–289, 2017. DOI: 10.1049/iet-its.2016.0066.

    Article  Google Scholar 

  95. F. Westerhuis, D. De Waard. Reading cyclist intentions: Can a lead cyclists behaviour be predicted? Accident Analysis & Prevention, vol. 105, pp. 146–155, 2017. DOI: 10.1016/j.aap.2016.06.026.

    Article  Google Scholar 

  96. D. Manstetten. Behaviour prediction and intention detection in UR:BAN VIE -overview and introduction. URBAN Human Factors in Traffic, K. Bengler, J. Druke, S. Hoffmann, D. Manstetten, A. Neukum, Eds., Wiesbaden, Germany: Springer, 2018. DOI: 10.1007/978-3-658-15418-9_8.

    Google Scholar 

  97. F. Schneemann, P. Heinemann. Context-based detection of pedestrian crossing intention for autonomous driving in urban environments. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, South Korea, pp. 2243–2248, 2016. DOI: 10.1109/IROS.2016.7759351.

    Google Scholar 

  98. T. Fugger, B. Randles, A. Stein, W. Whiting, B. Gallagher. Analysis of pedestrian gait and Perception-reaction at signal-controlled crosswalk intersections. Transportation Research Record: Journal of the Transportation Research Board, vol. 1705, no. 1, pp.20–25, 2000. DOI: 10.3141/170504.

    Google Scholar 

  99. N. Schneider, D. M. Gavrila. Pedestrian path prediction with recursive Bayesian filters: A comparative study. In Proceedings of the 35th German Conference on Pattern Recognition, Springer, Saarbrücken, Germany, pp. 174–183, 2013. DOI: 10.1007/978-3-642-40602-7_18.

    Google Scholar 

  100. M. Goldhammer, M. Gerhard, S. Zernetsch, K. Doll, U. Brunsmann. Early prediction of a pedestrian’s trajectory at intersections. In Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems, IEEE, The Hague, The Netherlands, pp.237–242, 2013. DOI: 10.1109/ITSC.2013.6728239.

    Google Scholar 

  101. M. Goldhammer, K. Doll, U. Brunsmann, A. Gensler, B. Sick. Pedestrians trajectory forecast in public traffic with artificial neural networks. In Proceedings of the 22nd International Conference on Pattern Recognition, IEEE, Stockholm, Sweden, pp.4110–4115, 2014. DOI: 10.1109/ICPR.2014.704.

    Google Scholar 

  102. C. G. Keller, D. M. Gavrila. Will the pedestrian cross? A study on pedestrian path prediction. IEEE Transactions on Intelligent Transportation Systems, vol.15, no.2, pp.494–506, 2014. DOI: 10.1109/TITS.2013.2280766.

    Article  Google Scholar 

  103. S. Koehler, M. Goldhammer, S. Bauer, S. Zecha, K. Doll, U. Brunsmann, K. Dietmayer. Stationary detection of the Pedestrians intention at intersections. IEEE Intelligent Transportation Systems Magazine, vol. 5, no. 4, pp. 87–99, 2013. DOI: 10.1109/MITS.2013.2276939.

    Article  Google Scholar 

  104. J. F. P. Kooij, N. Schneider, F. Flohr, D. M. Gavrila. Context-based pedestrian path prediction. In Proceedings of the 13th European Conference on Computer Vision, Springer, Zurich, Switzerland, pp.618–633, 2014. DOI: 10.1007/978-3-319-10599-4_40.

    Google Scholar 

  105. J. Y. Kwak, B. C. Ko, J. Y. Nam. Pedestrian intention prediction based on dynamic fuzzy automata for vehicle driving at nighttime. Infrared Physics & Technology, vol.81, pp. 41–51, 2017. DOI: 10.1016/j.infrared.2016.12.014.

    Google Scholar 

  106. G. Q. Xu, L. Liu, Y. S. Ou, Z. J. Song. Dynamic modeling of driver control strategy of lane-change behavior and trajectory planning for collision prediction. IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 3, pp. 1138–1155, 2012. DOI: 10.1109/TITS.2012.2187447.

    Article  Google Scholar 

  107. R. N. Dang, J. Q. Wang, S. E. Li, K. Q. Li. Coordinated adaptive cruise control system with lane-change assistance. IEEE Transactions on Intelligent Transportation Systems, vol.16, no.5, pp.2373–2383, 2015. DOI: 10.1109/TITS.2015.2389527.

    Article  Google Scholar 

  108. W. Liu, S. W. Kim, K. Marczuk, M. H. Ang. Vehicle motion intention reasoning using cooperative perception on urban road. In Proceedings of the 17th International Conference on Intelligent Transportation Systems, IEEE, Qingdao, China, pp.424–430, 2014. DOI: 10.1109/ITSC.2014.6957727.

    Google Scholar 

  109. Y. Hou, P. Edara, C. Sun. Modeling mandatory lane changing using Bayes classifier and decision trees. IEEE Transactions on Intelligent Transportation Systems, vol. 15, no.2, pp. 647–655, 2014. DOI: 10.1109/TITS.2013.2285337.

    Article  Google Scholar 

  110. D. Lee, A. Hansen, J. K. Hedrick. Probabilistic inference of traffic participants lane change intention for enhancing adaptive cruise control. In Proceedings of IEEE Intelligent Vehicles Symposium, IEEE, Los Angeles, USA, pp.855–860, 2017. DOI: 10.1109/IVS.2017.7995823.

    Google Scholar 

  111. Y. L. Gu, Y. Hashimoto, L. T. Hsu, S. Kamijo. Motion planning based on learning models of pedestrian and driver behaviors. In Proceedings of the 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, pp.808–813, 2016. DOI: 10.1109/ITSC.2016.7795648.

    Google Scholar 

  112. W. D. Xu, J. Pan, J. Q. Wei, J. M. Dolan. Motion planning under uncertainty for on-road autonomous driving. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Hong Kong, China, pp. 2507–2512, 2014. DOI: 10.1109/ICRA.2014.6907209.

    Google Scholar 

  113. T. Y. Gu, J. M. Dolan, J. W. Lee. Automated tactical maneuver discovery, reasoning and trajectory planning for autonomous driving. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, South Korea, pp.5474–5480, 2016. DOI: 10.1109/IROS.2016.7759805.

    Google Scholar 

  114. N. Nagasaka, M. Harada. Towards safe, smooth, and stable path planning for on-road autonomous driving under uncertainty. In Proceedings of the 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, pp.795–801, 2016. DOI: 10.1109/ITSC.2016.7795646.

    Google Scholar 

  115. K. Jo, M. Lee, J. Kim, M. Sunwoo. Tracking and behavior reasoning of moving vehicles based on roadway geometry constraints. IEEE Transactions on Intelligent Transportation Systems, vol.18, no.2, pp.460–476, 2017. DOI: 10.1109/TITS.2016.2605163.

    Article  Google Scholar 

  116. E. A. I. Pool, J. F. P. Kooij, D. M. Gavrila. Using road topology to improve cyclist path prediction. In Proceedings of IEEE Intelligent Vehicles Symposium, IEEE, Los Angeles, USA, pp. 289–296, 2017. DOI: 10.1109/IVS.2017.7995734.

    Google Scholar 

  117. N. Evestedt, E. Ward, J. Folkesson, D. Axehill. Interaction aware trajectory planning for merge scenarios in congested traffic situations. In Proceedings of the 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, pp. 465–472, 2016. DOI: 10.1109/ITSC.2016.7795596.

    Google Scholar 

  118. H. M. Eraqi, M. N. Moustafa, J. Honer. End-to-end deep learning for steering autonomous vehicles considering temporal dependencies. arXiv:1710.03804, 2017.

    Google Scholar 

  119. L. Caltagirone, M. Bellone, L. Svensson, M. Wahde. Simultaneous perception and path generation using fully convolutional neural networks. arXiv:1703.08987, 2017.

    Google Scholar 

  120. Z. Bylinskii, T. Judd, A. Oliva, A. Torralba, F. Durand. What do different evaluation metrics tell us about saliency models? arXiv:1604.03605, 2016.

    Google Scholar 

  121. B. W. Tatler. The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. Journal of Vision, vol. 7, no. 14–17, pp. 4.1–17, 2007. DOI: 10.1167/7.14.4.

    Google Scholar 

  122. R. J. Peters, A. Iyer, L. Itti, C. Koch. Components of bottom-up gaze allocation in natural images. Vision Research, vol. 45, no. 18, pp. 2397–2416, 2005. DOI: 10.1016/j.visres.2005.03.019.

    Article  Google Scholar 

  123. M. Kümmerer, T. S. A. Wallis, M. Bethge. Informationtheoretic model comparison unifies saliency metrics. In Proceedings of the National Academy of Sciences of the United States of America, vol. 112, no.52, pp. 16054–16059, 2015. DOI: 10.1073/pnas. 1510393112.

    Article  Google Scholar 

  124. M. J. Swain, D. H. Ballard. Color indexing. International Journal of Computer Vision, vol.7, no.1, pp.11–32, 1991. DOI: 10.1007/BF00130487.

    Article  Google Scholar 

  125. O. Le Meur, P. Le Callet, D. Barba. Predicting visual fixations on video based on low-level visual features. Vision Research, vol.47, no.19, pp.2483, 2498. DOI: 10.1016/j.visres.2007.06.015.

    Article  Google Scholar 

  126. O. Pele, M. Werman. A linear time histogram metric for improved sift matching. In Proceedings of the 10th European Conference on Computer Vision: Part III, Marseille, France, pp.495–508, 2008. DOI: 10.1007/978-3-540-88690-7_37.

    Google Scholar 

  127. Y. Rubner, C. Tomasi, L. J. Guibas. The earth movers distance as a metric for image retrieval. International Journal of Computer Vision, vol.40, no.2, pp.99–121, 2000. DOI: 10.1023/A:1026543900054.

    Article  MATH  Google Scholar 

  128. C. Sammut, G. I. Webb. Encyclopedia of Machine Learning, Boston, MA: Springer, 2010.

    Book  MATH  Google Scholar 

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Correspondence to Jian-Wu Fang.

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This work was supported by National Key R&D Program Project of China (No. 2016YFB1001004), Natural Science Foundation of China (Nos. 61751308, 61603057, 61773311), China Postdoctoral Science Foundation (No. 2017M613152), and Collaborative Research with MSRA.

Recommended by Associate Editor Matjaz Gams

Jian-Ru Xue received the M. Sc. and Ph.D. degrees from Xi’an Jiaotong University (XJTU), China in 1999 and 2003, respectively. He was with FujiXerox, Japan from 2002 to 2003, and visited the University of California at Los Angeles, USA from 2008 to 2009. He is currently a professor with the Institute of Artificial Intelligence and Robotics at XJTU. He served as a coorganization chair of the Asian Conference on Computer Vision and Virtual System and Multimedia Conference. He also served as a PC member of the Pattern Recognition Conference in 2012, and Asian Conference on Computer Vision in 2010 and 2012.

His research interests include computer vision, visual navigation, and scene understanding for autonomous system.

Jian-Wu Fang received the Ph.D. degree in signal and information processing from Univerisity of Chinese Academy of Sciences, China in 2015. He is currently an assistant professor in School of Electronic and Control Engineering, Chang-an University, China, and is also a postdoctor in Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, China.

His research interests include computer vision, pattern recognition and scene understanding.

Pu Zhang received the B. Sc. degree in automation from Southeast University, China in 2016. She is currently a Ph.D. degree candidate at Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, China.

Her research interests include computer vision and on-road scene understanding.

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Xue, JR., Fang, JW. & Zhang, P. A Survey of Scene Understanding by Event Reasoning in Autonomous Driving. Int. J. Autom. Comput. 15, 249–266 (2018). https://doi.org/10.1007/s11633-018-1126-y

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