Exploring big volume sensor data with Vroom


State of the art sensors within a single autonomous vehicle (AV) produce video and lidar data at more than 30 GB/hour. Not surprisingly, even small AV research teams easily accumulate tens of terabytes of sensor data from multiple trips and multiple vehicles. AV practitioners would like to extract information about specific locations, or specific situations for further study, but are often unable to. Queries over AV sensor data are different from generic analytics or spatial queries because they demand reasoning about fields of view as well as heavy computation to extract features from scenes. In this demo we present Vroom, a system for ad-hoc queries over AV sensor databases. Vroom combines domain specific properties of AV datasets with selective indexing and multi-query optimization to rise to the challenges posed by AV sensor data.

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