Anomaly detection framework for Autonomous vehicles using deep Learning techniques

Abstract

Vehicles have become an intrinsic part of our lives as one of the newlinemost popular ways of private transportation. Even though it provides comfort newlineand safety, private transportation poses road safety risks. Road fatalities are newlineincreasing due to traffic, high speed, and driver error. As a result, safety is a newlinetop priority in vehicle manufacturing and operation. The advancements in the newlineautomobile industry strive to provide increased safety benefits compared to its newlineprevious generations. Many modern vehicles include driver assistance newlinesystems that aid drivers in various ways. These systems offer helpful newlineinformation about traffic, congestion levels, blockage, alternative routes to newlineavoid congestion, etc. When a threat is detected, the driver assistance systems newlinemay take control of the vehicle from the driver and undertake simple tasks to newlinecomplex maneuvers. It also enables road safety, better driving, and reduce newlinefatalities by limiting human error. Such vehicles incorporating the automated newlinedriving systems to communicate with the outside world are called Connected newlineand Autonomous Vehicles (CAVs). newlineCAV has emerged as a transformative technology in the automobile newlinesector that has a great potential to change our daily life. Although the everincreasing newlineuse of CAV has numerous advantages, the potential drawbacks, newlinesuch as security and vulnerability to hacking, are not negligible. CAVs use a newlinevariety of sensors to build a virtual map of their surroundings to drive in the newlinecorrect lane within the speed limit, avoid collisions, and detect obstacles in newlinetheir immediate physical environment. newline