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Autonomous driving technology has drawn a lot of attention due to its fastdevelopment and extremely high commercial values. The recent technological leapof autonomous driving can be primarily attributed to the progress in theenvironment perception. Good environment perception provides accuratehigh-level environment information which is essential for autonomous vehiclesto make safe and precise driving decisions and strategies. Moreover, suchprogress in accurate environment perception would not be possible without deeplearning models and advanced onboard sensors, such as optical sensors (LiDARsand cameras), radars, GPS. However, the advanced sensors and deep learningmodels are prone to recently invented attack methods. For example, LiDARs andcameras can be compromised by optical attacks, and deep learning models can beattacked by adversarial examples. The attacks on advanced sensors and deeplearning models can largely impact the accuracy of the environment perception,posing great threats to the safety and security of autonomous vehicles. In thisthesis, we study the detection methods against the attacks on onboard sensorsand the linkage between attacked deep learning models and driving safety forautonomous vehicles. To detect the attacks, redundant data sources can beexploited, since information distortions caused by attacks in victim sensordata result in inconsistency with the information from other redundant sources.To study the linkage between attacked deep learning models and drivingsafety...

Sensor Data Validation and Driving Safety in Autonomous Driving Systems