Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards
This addresses safety issues for autonomous vehicles in hazardous environments, but it is incremental as it builds on existing DNN methods with specific enhancements.
The study tackled the problem of autonomous vehicle navigation being compromised by unexpected roadway hazards like debris and roadblocks, which can cause crashes, by developing a DNN-based system with object detection and semantic segmentation, resulting in a 21% improvement in hazard avoidance compared to traditional systems.
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.