Abstract: A system, device, and method for sensing air quality with a sensor platform is disclosed. The method includes (i) directing a set of mobile sensors to a coarse region wherein the coarse region has coarse size based on variance of prior air quality measurements taken at locations within a vicinity of the coarse region, and (ii) directing the set of mobile sensors to a fine region wherein the fine region has a fine size based on variance of prior air quality measurements taken at locations within a vicinity of the fine region. The variance of air quality measurements associated with the fine region is greater than a variance of air quality measurements associated with the coarse region.

Abstract: A system, device, and method for sensing air quality with a sensor platform is disclosed. The method includes (i) directing an air quality measurement system to a predicted high information region wherein the predicted high information region is determined based on high variance of prior air quality measurements taken at different times, (ii) directing the air quality measurement system to a predicted low information region wherein the predicted low information region is determined based on low variance of prior air quality measurements taken at different times. The air quality measurement system is preferentially directed to the predicted high information region.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Logged data from an autonomous vehicle is processed to generate augmented data. The augmented data describes an actor in an environment of the autonomous vehicle, the actor having an associated actor type and an actor motion behavior characteristic. The augmented data may be varied to create different sets of augmented data. The sets of augmented data can be used to create one or more simulation scenarios that in turn are used to produce machine learning models to control the operation of autonomous vehicles.

Abstract: Systems and methods for autonomous vehicle testing are provided. In one example embodiment, a computer implemented method includes obtaining, by a computing system including one or more computing devices, simulated perception data indicative of one or more simulated states of at least one simulated object within a surrounding environment of an autonomous vehicle. The computer-implemented method includes determining, by the computing system, a motion of the autonomous vehicle based at least in part on the simulated perception data. The computer-implemented method includes causing, by the computing system, the autonomous vehicle to travel in accordance with the determined motion of the autonomous vehicle through the surrounding environment of the autonomous vehicle.