Novel Techniques for Hijacking Self-Driving Cars

Dr. Franklin De Santa¹, and Trevor Phillips²

¹ Department of Street Technology, Cranberry-Lemon University, Pittsburgh, PA, USA

² Leader of the Master Thieves Heist Guild

Abstract

The car theft business has been an integral part of the American automotive industry for decades. If it weren’t for widespread grand theft auto and stereo theft, we’d all still be driving reliable cars such as 90’s Toyota Camrys or 00’s Honda CR-V’s. While many fear that the advances in cyber and physical security in newer vehicles may be the end of the theft driven auto industry of inner cities across America, new techniques have been shown to make state of the art self-driving cars even easier to hijack, capture and sell for a profit than their outdated counterparts. The new technology has even driven higher demand for the products from modern chop shops than ever before. This paper will outline methods to confuse, disable and take advantage of the state of the art algorithms and sensors modern autonomous vehicles are now equipped with to revitalize the domestic car-jacking economy. 

Keywords: Self-Driving Cars, Image Classification, Autonomous Systems, Machine-Learning, Animal Behavior

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