Constraint Decision Optimization Model for Safe Autonomous Vehicle Operation

Numerous researchers and organizations continue to refine technologies that will soon allow vehicles to autonomously drive safely from Point A to Point B. Accuracy determining the position of the self-driving vehicle relative to lane markers and road boundaries specifically during inclement weather conditions continues is of primary importance. This paper presents an investigation and associated results where road land boundary markers are detected in conjunction with the ability decipher the horizon when the front view of the vehicle's path is degraded. Degradation of driving scenes can be attributed to such weather conditions as heavy rain, fog, snow, or dust storms. The detection of lane markers and road boundaries is especially important for roads that exhibit severe curves, aggressive uphill slopes and downhill valleys, respectively. Additionally, we present a model to predict deviations from reference distances associated with roads with such design constraints along with some promising experimental results.