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Simulation has the potential to transform the development of robustalgorithms for mobile agents deployed in safety-critical scenarios. However,the poor photorealism and lack of diverse sensor modalities of existingsimulation engines remain key hurdles towards realizing this potential. Here,we present VISTA, an open source, data-driven simulator that integratesmultiple types of sensors for autonomous vehicles. Using high fidelity,real-world datasets, VISTA represents and simulates RGB cameras, 3D LiDAR, andevent-based cameras, enabling the rapid generation of novel viewpoints insimulation and thereby enriching the data available for policy learning withcorner cases that are difficult to capture in the physical world. Using VISTA,we demonstrate the ability to train and test perception-to-control policiesacross each of the sensor types and showcase the power of this approach viadeployment on a full scale autonomous vehicle. The policies learned in VISTAexhibit sim-to-real transfer without modification and greater robustness thanthose trained exclusively on real-world data.

VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles