DOI: https://doi.org/10.15587/1729-4061.2018.141298

A hybrid liar/radar-based deep learning and vehicle recognition engine for autonomous vehicle precrash control

Bassant Mohamed Elbagoury, Rytis Maskeliunas, Abdel Badeeh Mohamed M. Salem

Abstract


PreCrash problem of Intelligent Control of autonomous vehicles robot is a very complex problem, especially vehicle pre-crash scenarios and at points of intersections in real-time environments.

The goal of this research is to develop a new artificial intelligent adaptive controller for autonomous vehicle Pre-Crash system along with vehicle recognition module and tested in MATLAB including some detailed modules. Following tasks were set: finding Objects in sensor Data (LiDAR. RADAR), Speed and Steering control, vehicle Recognition using convolution neural network and Alexnet.

In this research paper, we implemented a real-time image/Lidar processing. At the beginning, we presented a real-time system which is composed of comprehensive modules, these modules are 3d object detection, object clustering and search, ground removal, deep learning using convolutional neural networks. Starting with nearest vehicle  module our target is to find the nearest ahead car and consider it as our primary obstacle.

This paper presents an Adaptive cruise pre-crash system and vehicle recognition. The Adaptive cruise pre-crash system module depends on Deep Learning and LiDAR sensor data, which meant to control the driver reckless behavior on the road by adjusting the vehicle speed to maintain a safe distance from objects ahead (such as cars, humans, bicycle or whatever the object) when the driver tries to raise speed. At the very moment the vehicle recognition module, detects and recognizes the vehicles surrounding to the car.


Keywords


Deep Learning; LiDAR sensor Dataset; KD Tree Algorithms; Point Cloud; Vehicle Recognition Module

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References


Hsu, C. W., Liang, C. N., Ke, L. Y., Huang, F. Y. (2009). Verification of On-Line Vehicle Collision Avoidance Warning System using DSRC. World Academy of Science, Engineering and Technology, 3 (7), 808–814.

Chang, B. R., Tsai, H. F., Young, C.-P. (2010). Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing. Expert Systems with Applications, 37 (3), 2439–2450. doi: https://doi.org/10.1016/j.eswa.2009.07.036

Köhler, M. Accurate PreCrash Detection. IBEO Automobile Sensor GmbH, System Development. Hamburg.

Schouten, N. (2008). Pre-Crash Testing in the VeHIL Facility. TNO Automotive, Integrated Safety Department, 28.

Navet, N., Simonot-Lion, F. (Eds.) (2009). Automotive Embedded Systems Handbook. CRC Press. doi: https://doi.org/10.1201/9780849380273

Jansson, J., Johansson, J., Gustafsson, F. (2002). Decision Making for Collision Avoidance Systems. SAE Technical Paper Series. doi: https://doi.org/10.4271/2002-01-0403

Evans, C. (2009). Notes on the open surf library. University of Bristol, Tech. Rep. CSTR-09-001.

Popirlan, C., Dupac, M. (2009). An Optimal Path Algorithm for Autonomous Searching Robots. Annals of University of Craiova, Math. Comp. Sci. Ser., 36 (1), 37–48.

Dolgov, D., Thrun, S., Montemerlo, M., Diebel, J. (2010). Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments. The International Journal of Robotics Research, 29 (5), 485–501. doi: https://doi.org/10.1177/0278364909359210

Buehler, M., Iagnemma, K., Singh, S. (Eds.) (2007). The 2005 DARPA Grand Challenge: The Great Robot Race. Springer. doi: https://doi.org/10.1007/978-3-540-73429-1

Urban challenge rules, revision (2007). DARPA, 28. http://archive.darpa.mil/grandchallenge/docs/Urban_Challenge_Rules_102707.pdf

Urmson, C. (2007). Tartan Racing: A Multi-Modal Approach to the DARPA Urban Challenge.

Tavel, P. (2007). Modeling and Simulation Design. AK Peters Ltd.

Welcome to the KITTI Vision Benchmark Suite! Available at: http://www.cvlibs.net/datasets/kitti/

Pandey G., McBride J. R., Eustice R. M. Ford Campus Vision and Lidar Data Set. Available at: http://robots.engin.umich.edu/publications/gpandey-2010b.pdf

Robotics: Estimation and Learning. Available at: https://www.coursera.org/learn/robotics-learning


GOST Style Citations


Verification of On-Line Vehicle Collision Avoidance Warning System using DSRC / Hsu C. W., Liang C. N., Ke L. Y., Huang F. Y. // World Academy of Science, Engineering and Technology. 2009. Vol. 3, Issue 7. P. 808–814.

Chang B. R., Tsai H. F., Young C.-P. Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing // Expert Systems with Applications. 2010. Vol. 37, Issue 3. P. 2439–2450. doi: https://doi.org/10.1016/j.eswa.2009.07.036 

Köhler M. Accurate PreCrash Detection. IBEO Automobile Sensor GmbH, System Development. Hamburg.

Schouten N. Pre-Crash Testing in the VeHIL Facility. TNO Automotive, Integrated Safety Department. 2008. 28 p.

Automotive Embedded Systems Handbook / N. Navet, F. Simonot-Lion (Eds.). CRC Press, 2009. doi: https://doi.org/10.1201/9780849380273 

Jansson J., Johansson J., Gustafsson F. Decision Making for Collision Avoidance Systems // SAE Technical Paper Series. 2002. doi: https://doi.org/10.4271/2002-01-0403 

Evans C. Notes on the open surf library. University of Bristol, Tech. Rep. CSTR-09-001, 2009.

Popirlan C., Dupac M. An Optimal Path Algorithm for Autonomous Searching Robots //Annals of University of Craiova, Math. Comp. Sci. Ser. 2009. Vol. 36, Issue 1. P. 37–48.

Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments / Dolgov D., Thrun S., Montemerlo M., Diebel J. // The International Journal of Robotics Research. 2010. Vol. 29, Issue 5. P. 485–501. doi: https://doi.org/10.1177/0278364909359210 

The 2005 DARPA Grand Challenge: The Great Robot Race / M. Buehler, K. Iagnemma, S. Singh (Eds.). Springer, 2007. doi: https://doi.org/10.1007/978-3-540-73429-1 

Urban challenge rules, revision. DARPA. 2007. 28 p. http://archive.darpa.mil/grandchallenge/docs/Urban_Challenge_Rules_102707.pdf

Urmson C. Tartan Racing: A Multi-Modal Approach to the DARPA Urban Challenge. 2007.

Tavel P. Modeling and Simulation Design. AK Peters Ltd., 2007.

Welcome to the KITTI Vision Benchmark Suite! URL: http://www.cvlibs.net/datasets/kitti/

Pandey G., McBride J. R., Eustice R. M. Ford Campus Vision and Lidar Data Set. URL: http://robots.engin.umich.edu/publications/gpandey-2010b.pdf

Robotics: Estimation and Learning. URL: https://www.coursera.org/learn/robotics-learning






Copyright (c) 2018 Bassant Mohamed Elbagoury, Abdel Badeeh Mohamed M. Salem, Rytis Maskeliunas

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061