Abstract: In one embodiment, a method includes receiving sensor data associated with an object external to a vehicle. The sensor data includes a sequence of data packets. Each data packet corresponds to a time frame in a sequence of time frames. The method includes determining a classification of the object based at least on the sensor data, determining that a particular data packet corresponding to a particular time frame in the sequence of time frames is corrupt or missing, generating a replacement data packet based on one or more data packets that correspond to one or more time frames adjacent to the particular time frame, generating a sequence of visual representations of the object corresponding to the sequence of time frames. At least one visual representation in the sequence of visual representations corresponding to the particular time frame is generated based on the replacement data packet.

Abstract: In one embodiment, a method includes determining a plurality of available locations for a vehicle to pick up or drop off a user in an area based on sensor data that is captured by the vehicle and is associated with physical characteristics of the area. The method includes calculating a viability value for each of the plurality of available locations. The viability value is calculated based on the physical characteristics of the area as determined from the sensor data. The method includes ranking the plurality of available locations according to the viability values of the plurality of available locations. An available location with a higher viability value is ranked above an available location with a lower viability value. The method includes sending instructions to present, on a computing device, one or more selectable locations of the plurality of available locations based on the ranking.

Abstract: In one embodiment, a method includes sending a set of instructions to present, on a computing device, one or more available locations for a vehicle to pick-up or drop-off a user in an area. The one or more available locations are based on sensor data of the area that is captured by the vehicle. The method includes receiving a user selection to select a location associated with the area for the vehicle to pick-up or drop-off the user. The method includes adjusting a viability value of one or more locations to pick-up or drop-off the user. The viability value is adjusted based at least on the selected location. The method includes, based on the adjusted viability value of the one or more locations, determining a location from the one or more locations. The method includes instructing the vehicle to travel to the determined location.

Abstract: In one embodiment, a method includes receiving sensor data corresponding to an environment external of a vehicle. The sensor data include data points. The method includes determining one or more subsets of the data points. The method includes comparing the one or more subsets of the data points to one or more predetermined data patterns. Each of the one or more predetermined data patterns corresponds to an object classification. The method includes computing a confidence score for each subset of data points of the one or more subsets of the data points as corresponding to each of the one or more predetermined data patterns based on the comparison. The method includes generating a classification for an object in the environment external of the vehicle based on the confidence score.

Abstract: In one embodiment, a method includes receiving sensor data corresponding to an environment external of a vehicle. The sensor data include data points. The method includes determining one or more subsets of the data points. The method includes comparing the one or more subsets of the data points to one or more predetermined data patterns. Each of the one or more predetermined data patterns corresponds to an object classification. The method includes computing a confidence score for each subset of data points of the one or more subsets of the data points as corresponding to each of the one or more predetermined data patterns based on the comparison. The method includes generating a classification for an object in the environment external of the vehicle based on the confidence score.

Abstract: In one embodiment, a method includes receiving sensor data from a sensor array of a vehicle while traveling on a road. The method includes determining a confidence score for a classification of an object based on the sensor data. The method includes generating an object graphic corresponding to the object based on the confidence score for the classification of the object. The method includes retrieving, from one or more third-party systems, third-party data associated with an environment in proximity to the object and associated with the road based on the classification. The method includes generating an overlay graphic corresponding to the environment in proximity to the object based on the third-party data. The method includes providing for display the object graphic rendered in association with the overlay graphic corresponding to the environment.

Abstract: In one embodiment, a method includes receiving autonomous-vehicle sensor data from a sensor array of an autonomous vehicle. The autonomous-vehicle sensor data indicates an object in an external environment of the autonomous vehicle. The method also includes accessing secondary data associated with the object from one or more secondary-data sources that are independent of the sensor array of the autonomous vehicle. The method also includes determining, based on the autonomous-vehicle sensor data and the secondary data, an object graphic to visually represent the object. The method also includes providing for display a visual representation of the external environment of the autonomous vehicle that includes the determined object graphic to visually represent the object.

Abstract: In one embodiment, a method includes receiving autonomous-vehicle sensor data from a sensor array of an autonomous vehicle. The autonomous-vehicle sensor data indicates an object in an external environment of the autonomous vehicle. The method further includes determining a confidence score for a classification of the object. The method further includes determining an object graphic with a level of detail to visually represent the object. The level of detail of the object graphic is based on the confidence score. The method further includes providing for display a visual representation the object graphic.

Abstract: In one embodiment, a method includes sending a set of instructions to present, on a computing device, one or more available locations for a vehicle to pick-up or drop-off a user in an area. The one or more available locations are based on sensor data of the area that is captured by the vehicle. The method includes receiving a user selection to select a location associated with the area for the vehicle to pick-up or drop-off the user. The method includes adjusting a viability value of one or more locations to pick-up or drop-off the user. The viability value is adjusted based at least on the selected location. The method includes, based on the adjusted viability value of the one or more locations, determining a location from the one or more locations. The method includes instructing the vehicle to travel to the determined location.

Abstract: In one embodiment, a method includes identifying, based on map data, an area for pick-up or drop-off of a user by an autonomous vehicle. The method also includes determining, based on autonomous-vehicle sensor data that represents an external environment of the autonomous vehicle, one or more potential pick-up or drop-off locations within the area. The method also includes calculating, based at least in part on the autonomous-vehicle sensor data and historical data a viability score for each of the potential pick-up or drop-off locations. The historical data includes past pick-up or drop-off locations for one or more past users. The method also includes providing for display a visual representation of at least a portion of the area for pick-up or drop-off that indicates at least one of the one or more potential pick-up or drop-off locations.

Abstract: In one embodiment, a method includes identifying, based on map data, an area for pick-up or drop-off of a user by an autonomous vehicle. The method also includes determining, based on autonomous-vehicle sensor data that represents an external environment of the autonomous vehicle, one or more potential pick-up or drop-off locations within the area. The method also includes calculating, based at least in part on the autonomous-vehicle sensor data and historical data a viability score for each of the potential pick-up or drop-off locations. The historical data includes past pick-up or drop-off locations for one or more past users. The method also includes providing for display a visual representation of at least a portion of the area for pick-up or drop-off that indicates at least one of the one or more potential pick-up or drop-off locations.

Abstract: In one embodiment, a method includes receiving autonomous-vehicle sensor data from a sensor array of an autonomous vehicle. The autonomous-vehicle sensor data indicates an object in an external environment of the autonomous vehicle. The method also includes accessing secondary data associated with the object from one or more secondary-data sources that are independent of the sensor array of the autonomous vehicle. The method also includes determining, based on the autonomous-vehicle sensor data and the secondary data, an object graphic to visually represent the object. The method also includes providing for display a visual representation of the external environment of the autonomous vehicle that includes the determined object graphic to visually represent the object.

Abstract: In one embodiment, a method includes receiving autonomous-vehicle sensor data from a sensor array of an autonomous vehicle. The autonomous-vehicle sensor data indicates an object in an external environment of the autonomous vehicle. The method further includes determining a confidence score for a classification of the object. The method further includes determining an object graphic with a level of detail to visually represent the object. The level of detail of the object graphic is based on the confidence score. The method further includes providing for display a visual representation the object graphic.