This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Teaching Autonomous Vehicles How to Drive under Sensing Exceptions by Human Driving Demonstrations
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
Annotation ability available
Autonomous driving technologies can provide better safety, comfort and efficiency for future transportation systems. Most research in this area has mainly been focused on developing sensing and control approaches to achieve various autonomous driving functions. Very little of this research, however, has studied how to efficiently handle sensing exceptions. A simple exception measured by any of the sensors may lead to failures in autonomous driving functions. The autonomous vehicles are then supposed to be sent back to manufacturers for repair, which takes both time and money. This paper introduces an efficient approach to make human drivers able to online teach autonomous vehicles to drive under sensing exceptions. A human-vehicle teaching-and-learning framework for autonomous driving is proposed and the human teaching and vehicle learning processes for handling sensing exceptions in autonomous vehicles are designed in detail. Experimental results acquired from a 1/10-scale autonomous driving vehicle illustrate the effectiveness and advantages of the proposed approach.
CitationGuo, L., Manglani, S., Li, X., and Jia, Y., "Teaching Autonomous Vehicles How to Drive under Sensing Exceptions by Human Driving Demonstrations," SAE Technical Paper 2017-01-0070, 2017, https://doi.org/10.4271/2017-01-0070.
- Urmson, C., Anhalt, J., Bagnell, D., Baker, C., et al., "Autonomous driving in urban environments: Boss and the urban challenge." Journal of Field Robotics 25, no. 8 (2008): 425-466, DOI: 10.1002/rob.20255.
- Levinson, J., Askeland, J., Becker, J., Dolson, J., et al., "Towards fully autonomous driving: Systems and algorithms." In Intelligent Vehicles Symposium (IV), 2011 IEEE, pp. 163-168. IEEE, 2011, DOI: 10.1109/IVS.2011.5940562.
- Petrovskaya, A., and Thrun. S., "Model based vehicle detection and tracking for autonomous urban driving." Autonomous Robots 26, no. 2-3 (2009): 123-139, DOI: 10.1007/s10514-009-9115-1.
- McCall, J.C., and Trivedi, M.M., "Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation." IEEE transactions on intelligent transportation systems 7, no. 1 (2006): 20-37., DOI: 10.1109/TITS.2006.869595.
- Shinzato, P.Y., Grassi, V., Osorio, F.S., and Wolf, D.F., "Fast visual road recognition and horizon detection using multiple artificial neural networks." In Intelligent Vehicles Symposium (IV), 2012 IEEE, pp. 1090-1095. IEEE, 2012, DOI: 10.1109/IVS.2012.6232175.
- Shinzato, P.Y., and Wolf, D.F., "A road following approach using artificial neural networks combinations." Journal of Intelligent & Robotic Systems 62, no. 3-4 (2011): 527-546, DOI: 10.1007/s10846-010-9463-2.
- Pomerleau, D.A., "Efficient training of artificial neural networks for autonomous navigation." Neural Computation 3, no. 1 (1991): 88-97, DOI: 10.1162/neco.1922.214.171.124.
- Baluja, S., "Evolution of an artificial neural network based autonomous land vehicle controller." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, no. 3 (1996): 450-463, DOI: 10.1109/3477.499795.
- Moosmann, F., Pink, O., and Stiller, S., "Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion." In Intelligent Vehicles Symposium, 2009 IEEE, pp. 215-220. IEEE, 2009, DOI: 10.1109/IVS.2009.5164280.
- Newman, P., Sibley, G., Smith, M., Cummins, M., et al., "Navigating, recognizing and describing urban spaces with vision and lasers." The International Journal of Robotics Research 28, no. 11-12 (2009): 1406-1433, DOI: 10.1177/0278364909341483.
- Allodi, M., Broggi, A., Giaquinto, D., Patander, M., et al., "Machine learning in tracking associations with stereo vision and lidar observations for an autonomous vehicle." In Intelligent Vehicles Symposium (IV), 2016 IEEE, pp. 648-653. IEEE, 2016, DOI: 10.1109/IVS.2016.7535456.
- Choi, J., Ulbrich, S., Lichte, B., and Maurer, M., "Multi-Target Tracking using a 3D-Lidar sensor for autonomous vehicles." In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 881-886. IEEE, 2013, DOI: 10.1109/ITSC.2013.6728343.
- Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., et al., "End to End Learning for Self-Driving Cars." arXiv preprint arXiv:1604.07316 (2016).
- Moré, J.J., and Sorensen, D.C., "Computing a trust region step." SIAM Journal on Scientific and Statistical Computing 4, no. 3 (1983): 553-572., DOI: 10.1137/0904038.
- Byrd, R.H., Schnabel, R.B., and Shultz, G.A., "Approximate solution of the trust region problem by minimization over two-dimensional subspaces." Mathematical programming 40, no. 1-3 (1988): 247-263, DOI: 10.1007/BF01580735.