A Method for Predicting Vehicles Motion Based on Road Scene Reconstruction and Neural Networks in Real Time

D. Sc. Nina Krapukhina, Nikolay Kamenov

Abstract


The suggested method helps predicting vehicles movement in order to give the driver more time to react and avoid collisions on roads. The algorithm is dynamically modelling the road scene around the vehicle based on the data from the onboard camera. All moving objects are monitored and represented by the dynamic model on a 2D map. After analyzing every object’s movement, the algorithm predicts its possible behavior.

Keywords


prediction; neural networks; road scene reconstruction; real time

References


Bo Wahlberg “Automated Transport Systems (ATS)” KTH Royal Institute of Technology, Sweden, 2000

Gereon Meyer, Jadranka Dokic, Beate Müller “ European Roadmap Smart Systems for Automated Driving”, The European Technology Platform on Smart Systems Integration, Berlin, April, 2015

David Burton, Amanda Delaney, Stuart Newstead, David Logan, Brian Fildes “Evaluation of Anti-lock Braking Systems Effectiveness”; Royal Club of Victoria (RACV) Ltd; April 2004, ISBN 1 875963 39 1

Batavia, P.H. “Driver-adaptive lane departure warning systems” CMU-RI-TR-99-25, Thesis 1999

Neal E. Boudette “Building a Road Map for the Self-Driving Car” The New York Times, March 2017

Diana Gornea, Dan Popescu, Grigore Stamatescu, Radu Fratila „Mono-camera robotic system for tracking moving objects“ 9th IEEE Conference on Industrial Electronics and Applications 2014, pp:1820 – 1825

N. Molton, S. Se, J. M. Brady, D. Lee, and P. Probert, “A stereo vision-based aid for the visually impaired,” Image Vis. Comput., vol. 16, Mar. 1998, pp. 251 – 263

Zhencheng Hu, and Keiichi Uchimura „U-V-Disparity: An efficient algorithm for Stereovision Based Scene Analysis“ Proceedings of Intelligent Vehicles Symposium IEEE June 2005 pp: 48 - 54

Mohammad Sohrab Hossan Monsi, "Laser Radar for Precise Vehicle Velocity Measurement," Kasel University, Kasel, PhD Thesis 978-3-89958-736-4, 2009.

Garcia F., Cerri P., Broggi A., Armingol J.M., de la Escalera A. “Vehicle Detection Based on Laser Radar" Lecture Notes in Computer Science, vol 5717. Springer, Berlin 2009

Daniel Gohring, Miao Wang, Michael Schnurmacher, Tinosch Ganjineh “Radar/Lidar sensor fusion for car-following on highways” 5th International Conference on Automation, Robotics and Applications (ICARA), 2011 pp: 407 – 412

Nico Kaempchen, Klaus Dietmayer, “Data synchronization strategies for multi-sensor fusion”, University of Ulm, Department of Measurement, Control and Microtechnology Albert-Einstein-Allee 41, D-89081 Ulm, Germany, Nov 2015

Richard van der Horst & Jeroen Hogema, “Time to-collision and collision aboidance systems”, Institute for Road Safety Research, SWOV,pp: 59 – 66, 1997 ISBN: 90-6807-293-5

Kusano, K. and Gabler, H., "Method for Estimating Time to Collision at Braking in Real-World, Lead Vehicle Stopped Rear-End Crashes for Use in Pre-Crash System Design," SAE Int. J. Passeng. Cars – Mech. Syst. 4(1):435-443, doi:10.4271/2011-01-0576. 2011

P. Kapsalas ; K. Rapantzikos ; A. Sofou ; Y. Avrithis – “Regions of interest for accurate object detection” Content-Based Multimedia Indexing CBMI, pp:147 – 154 2008

Philip Lenz, Julius Ziegler, Andreas Geiger, Martin Roser “Sparse scene flow segmentation for moving object detection in urban environments” 4th Intelligent Vehicles Symposium 2011 pp: 926 - 932

U.S. Patent 6,711,293, "Method and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image", David Lowe's patent for the SIFT algorithm, March 23, 2004

M. A. Fischler, R. C. Bolles. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM, Vol 24,1981, pp 381-395

Senchenko R.V., Kamenov N.V., Krapukhina N.V., “The approach of transport security using multi-agent simulation and machine vision methods,”, In Proceedings of the XXIV International Conference Problems Of Safety Management Of Complex Systems, Moscow Russia 2016, pp: 329 – 332

Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese; “Social LSTM: Human Trajectory Prediction in Crowded Spaces”, “The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 961-971

Anurag Sharma, Ashish Chaturvedi “Gradient Descent Feed Forward Neural Networks for Forecasting the Trajectories” International Journal of Advanced Science and Technology Vol. 34, 2011

Pierre Payeur, Hoang Le-Huy, Clement M. Gosslin “Trajectory Prediction for moving objects using artifical neural networks” IEEE Transactions on industrial electronics, vol. 42, 1995 pp: 147 – 158

B. Kosko, Neural Networks and Fuzzy Systems. Englewood Cliffs, NJ: Prentice-HALL, 1992


Full Text: PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

IT in Innovation IT in Business IT in Engineering IT in Health IT in Science IT in Design IT in Fashion

IT in Industry @ (2012 - ) . http://www.it-in-industry.com . ISSN (Online): 2203-1731; ISSN (Print): 2204-0595