Research on Security of Key Algorithms in Intelligent Driving System
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摘要
With the rapid development of the smart driving technology, the security of smart driving algorithms is becoming more and more important. Four core smart driving algorithms are determined by studying the architecture of smart driving algorithm. These algorithms comprise local path planning, pedestrian detection, lane detection and obstacle detection. The security issues of these algorithms are investigated by closely examining the work carried out by the algorithms. We found that there are vulnerabilities in all four algorithms. These vulnerabilities can cause abnormality and even road accidents for the smart cars. The final experiment shows that the vulnerabilities of these algorithms do exist under certain circumstances and therefore have high security risks. This study will lay a foundation to improve the security of the smart driving system.
With the rapid development of the smart driving technology, the security of smart driving algorithms is becoming more and more important. Four core smart driving algorithms are determined by studying the architecture of smart driving algorithm. These algorithms comprise local path planning, pedestrian detection, lane detection and obstacle detection. The security issues of these algorithms are investigated by closely examining the work carried out by the algorithms. We found that there are vulnerabilities in all four algorithms. These vulnerabilities can cause abnormality and even road accidents for the smart cars. The final experiment shows that the vulnerabilities of these algorithms do exist under certain circumstances and therefore have high security risks. This study will lay a foundation to improve the security of the smart driving system.
With the rapid development of the smart driving technology, the security of smart driving algorithms is becoming more and more important. Four core smart driving algorithms are determined by studying the architecture of smart driving algorithm. These algorithms comprise local path planning, pedestrian detection, lane detection and obstacle detection. The security issues of these algorithms are investigated by closely examining the work carried out by the algorithms. We found that there are vulnerabilities in all four algorithms. These vulnerabilities can cause abnormality and even road accidents for the smart cars. The final experiment shows that the vulnerabilities of these algorithms do exist under certain circumstances and therefore have high security risks. This study will lay a foundation to improve the security of the smart driving system.
引文
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[13]Y.D.Li,H.B.Huang,X.P.Li,et al.,“Nighttime lane markings detection based on canny operator and hough transform”,Science Technology and Engineering,Vol.16,No.31,pp.234-237,2016.
[14]C.Fan,S.Di and L.L.Hou,“Lane marking detection technique based on improved RANSAC algorithm automotive”,Automotive Engineering,Vol.36,No.4,pp.503-508,2014.
[15]J.Li,X.Mei,D.Prokhorov,et al.,“Deep neural network for structural prediction and lane detection in traffic scene”,IEEE Transactions on Neural Networks&Learning Systems,Vol.28,No.3,pp.690-703,2016.
[16]J.Canny,“Collision detection for moving polyhedra”,IEEETransactions on Pattern Analysis&Machine Intelligence,Vol.8,No.2,pp.200,1986.
[17]F.X.Chen and R.S.Wang,“Fast RANSAC with preview model parameters evaluation”,Journal of Software,Vol.16,No.8,pp.1431-1437,2005.
[18]M.F.Zhang,X.Y.Liu and R.Fu,“Laser point cloud clustering algorithm for road obstacle recognition”,Laser and Infrared,Vol.9,pp.1186-1192,2017.
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[21]Z.L.Wang and Y.Niu,“Robotic obstacle detection based on multi-sensor information fusion”,China Test,Vol.43,pp.80-85,2017.
[22]L.N.Zeng,“Research on obstacle detection and recognition method in vehicle vision system”,M.E.Thesis,Nanjing University of Aeronautics and Astronautics,2016.
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[24]J.Shannon,“Advanced Lane Finding Project”,available at https://github.com/jeremy-shannon/CarND-Advanced-LaneLines,2017-2-27.