WJET  Vol.3 No.3 C , October 2015
Towards Autonomous Vehicles with Advanced Sensor Solutions
ABSTRACT

Professional truck drivers are an essential part of transportation in keeping the global economy alive and commercial products moving. In order to increase productivity and improve safety, an increasing amount of automation is implemented in modern trucks. Transition to automated heavy good vehicles is intended to make trucks accident-free and, on the other hand, more comfortable to drive. This motivates the automotive industry to bring more embedded ICT into their vehicles in the future. An avenue towards autonomous vehicles requires robust environmental perception and driver monitoring technologies to be introduced. This is the main motivation behind the DESERVE project. This is the study of sensor technology trials in order to minimize blind spots around the truck and, on the other hand, keep the driver’s vigilance at a sufficiently high level. The outcomes are two innovative truck demonstrations: one R & D study for bringing equipment to production in the future and one implementation to the driver training vehicle. The earlier experiments include both driver monitoring technology which works at a 60% - 80% accuracy level and environment perception (stereo and thermal cameras) whose performance rates are 70% - 100%. The results are not sufficient for autonomous vehicles, but are a step forward, since they are in-line even if moved from the lab to real automotive implementations.


Cite this paper
Kutila, M. , Pyykönen, P. , Lybeck, A. , Niemi, P. and Nordin, E. (2015) Towards Autonomous Vehicles with Advanced Sensor Solutions. World Journal of Engineering and Technology, 3, 6-17. doi: 10.4236/wjet.2015.33C002.
References
[1]   Pallaro, P., et al. (2013) Development Platform Requirements. The DESERVE Artemis-JU (295364) Deliverable D12.1.

[2]   Kutila, M., Pyykönen, P., van Koningsbruggen, P., Pallaro, N. and Pérez-Rastelli, J (2014) The DESERVE Project: Towards Future ADAS Functions. Proceedings of the International Conference on Embedded Computer Systems: Architectures, MOdeling and Simulation (SAMOS XIV), Samos, Greece, 14-17 July 2014, 308-313. http://dx.doi.org/10.1109/samos.2014.6893226

[3]   Morignot, P., Perez, J.R. and Nashashibi, F. (2014) Arbitration for Balancing Control between the Driver and ADAS Systems in an Automated Vehicle: Survey and Approach. Proceeding of IEEE Intelligent Vehicles Symposium, Michigan, USA, 8-11 June 2014, 575-580.

[4]   ERTRAC (2015) Automated Driving Roadmap. 3rd Draft for Public Consultation. ERTRACT Task Force Connectivity and Automated Driving.

[5]   Kuchinskas, S (2015) Levelling Up to Driverless Cars. TU-Automotive Magazine Article.

[6]   Dollar, P., Wojek, C., Schiele, B. and Perona, P. (2012) Pedestrian Detection: An Evaluation of the State of the Art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 743-761.

[7]   Enzweiler, M. and Gavrila, D.M. (2009) Monocular Pedestrian Detection: Survey and Experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 2179-2195.

[8]   Benenson, R., Omran, M, Hosang, J. and Schiele, B. (2014) Ten Years of Pedestrian Detection, What Have We Learned? ECCV Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving.

[9]   Gavrila, D.M. (2001) Sensor-Based Pedestrian Protection. IEEE Intelligent Systems, 16, 77-81.

[10]   Barua, N., Natarajan, P.T., Chandrasekar, P. and Singh, S. (2014) Strategic Analysis of the European Mar-ket for V2V and V2I Communication Systems. Frost & Sullivan report MA29-18.

[11]   Pyykönen, P., Virtanen, A. and Kyytinen, A. (2015) Developing Intelligent Blind Spot Detection System for Heavy Goods Vehicles. Submitted to the 18th International IEEE Conference on Intelligent Transportation Systems—ITSC 2015, Las Palmas de Gran Canaria, Spain, 15-18 September 2015. (unpublished)

[12]   Dalal, N. and Triggs, B. (2005) Histograms of Oriented Gradients for Human Detection, Computer Vision and Pattern Recognition. Proceedings of IEEE Computer Society Conference (CVPR), 1, 886-893, 25-25 June 2005.

[13]   HOG Descriptors for OpenCV Library. http://docs.opencv.org/modules/gpu/doc/object_detection.html

[14]   Kutila, M., Jokela, M., Markkula, G. and Rué, M.R. (2008) Driver Distraction Detection with a Camera Vision System. Proceedings of the IEEE International Conference on Image Processing (ICIP 2007), 16-19 September 2007, Texas, San Antonio, Vol. VI, 201-204.

 
 
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