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