  Vision-based navigation of autonomous vehicles primarily depends on the Deep
Neural Network (DNN) based systems in which the controller obtains input from
sensors/detectors, such as cameras and produces a vehicle control output, such
as a steering wheel angle to navigate the vehicle safely in a roadway traffic
environment. Typically, these DNN-based systems of the autonomous vehicle are
trained through supervised learning; however, recent studies show that a
trained DNN-based system can be compromised by perturbation or adversarial
inputs. Similarly, this perturbation can be introduced into the DNN-based
systems of autonomous vehicle by unexpected roadway hazards, such as debris and
roadblocks. In this study, we first introduce a roadway hazardous environment
(both intentional and unintentional roadway hazards) that can compromise the
DNN-based navigational system of an autonomous vehicle, and produces an
incorrect steering wheel angle, which can cause crashes resulting in fatality
and injury. Then, we develop a DNN-based autonomous vehicle driving system
using object detection and semantic segmentation to mitigate the adverse effect
of this type of hazardous environment, which helps the autonomous vehicle to
navigate safely around such hazards. We find that our developed DNN-based
autonomous vehicle driving system including hazardous object detection and
semantic segmentation improves the navigational ability of an autonomous
vehicle to avoid a potential hazard by 21% compared to the traditional
DNN-based autonomous vehicle driving system.
