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Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV’s decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. Consumer-ready AVs use motion planning algorithms that generate smooth trajectories.

Digital mapping tools have become indispensable for road navigation. Applications like Waze and Google Maps harness the power of satellite imagery to provide precise visualization of GPS coordinates. The field advanced significantly in May 2023 with the introduction of dynamic 3D representations of the Earth. Companies such as Cesium now offer Unity3D and Unreal Engine Application Programming Interface that can be applied to geospatial applications. These images are no longer static and offer the opportunity to provide seamless continuous navigation. Driving simulation has been widely used for training and research. We investigate with this project the potential of this new geospatial database as a tool for scenario development to study manual and autonomous driving. We present an in-vehicle driving simulation integration that employs a real steering wheel and pedals from a stationary vehicle as controls.