This paper describes a simulation model for autonomous vehicles operating in highly uncertain environments. Two elements of uncertainty are studied – rain and pedestrian interaction – and their effects on autonomous mobility. The model itself consists of all the essential elements of an autonomous vehicle: Scene - roads, buildings, etc., Environment - sunlight, rain, snow, etc., Sensors - gps, camera, radar, lidar, etc., Algorithms - lane detection, pedestrian detection, etc., Control - lane keeping, obstacle avoidance, etc., Vehicle Dynamics – mass, drivetrain, tires, etc., and Actuation - throttle, braking, steering, etc. Using this model, the paper presents results that assess the autonomous mobility of a Polaris GEM E6 type of vehicle in varying amounts of rain, and when the vehicle encounters multiple pedestrians crossing in front. Rain has been chosen as it impacts both situational awareness and trafficability conditions. Mobility is measured by the average speed of the vehicle. This work is part of MDAS.ai, a multi-disciplinary autonomous shuttle development project.