Abstract: Aspects of the technology relate to providing service area maps for an autonomous vehicle transportation service having a fleet of vehicles. For instance, each vehicle of the fleet is associated with a polygon corresponding to a service area for that vehicle. A first location may be received from a client computing device, and a set of vehicles of the fleet of vehicles that are currently available to provide transportation services may be identified based on the first location. The polygons associated with each of the set of vehicles may be used to determine a first polygon having a geographic area. A first portion of map information corresponding to the geographic area of the first polygon may be identified, and the first portion may be provided to the client computing device for display to a user such that the portion represents a currently available service area for the user.

Abstract: A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Abstract: Aspects of the technology relate to providing service area maps for an autonomous vehicle transportation service having a fleet of vehicles. For instance, each vehicle of the fleet is associated with a polygon corresponding to a service area for that vehicle. A first location may be received from a client computing device, and a set of vehicles of the fleet of vehicles that are currently available to provide transportation services may be identified based on the first location. The polygons associated with each of the set of vehicles may be used to determine a first polygon having a geographic area. A first portion of map information corresponding to the geographic area of the first polygon may be identified, and the first portion may be provided to the client computing device for display to a user such that the portion represents a currently available service area for the user.

Abstract: Aspects of the technology relate to providing service area maps for an autonomous vehicle transportation service having a fleet of vehicles. For instance, each vehicle of the fleet is associated with a polygon corresponding to a service area for that vehicle. A first location may be received from a client computing device, and a set of vehicles of the fleet of vehicles that are currently available to provide transportation services may be identified based on the first location. The polygons associated with each of the set of vehicles may be used to determine a first polygon having a geographic area. A first portion of map information corresponding to the geographic area of the first polygon may be identified, and the first portion may be provided to the client computing device for display to a user such that the portion represents a currently available service area for the user.

Abstract: A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Abstract: Aspects of the disclosure relate to determining vehicle operation modes for a fleet of vehicles. This may include maintaining a storage system including vehicle parameters for a vehicle fleet such that each vehicle of the fleet is associated with a set of vehicle parameters. The vehicle operation mode may be determined by selecting one of a plurality of vehicle operation levels arranged in a hierarchy or by identifying a set of tags where each level or tag corresponds to a vehicle driving capability. The determined vehicle operation mode may be communicated to the given vehicle in order to cause the vehicle to operate according to the determined vehicle operation mode.

Abstract: Aspects of the technology relate to providing service area maps for an autonomous vehicle transportation service having a fleet of vehicles. For instance, each vehicle of the fleet is associated with a polygon corresponding to a service area for that vehicle. A first location may be received from a client computing device, and a set of vehicles of the fleet of vehicles that are currently available to provide transportation services may be identified based on the first location. The polygons associated with each of the set of vehicles may be used to determine a first polygon having a geographic area. A first portion of map information corresponding to the geographic area of the first polygon may be identified, and the first portion may be provided to the client computing device for display to a user such that the portion represents a currently available service area for the user.

Abstract: A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Abstract: Aspects of the disclosure relate to determining vehicle operation modes for a fleet of vehicles. This may include maintaining a storage system including vehicle parameters for a vehicle fleet such that each vehicle of the fleet is associated with a set of vehicle parameters. The vehicle operation mode may be determined by selecting one of a plurality of vehicle operation levels arranged in a hierarchy or by identifying a set of tags where each level or tag corresponds to a vehicle driving capability. The determined vehicle operation mode may be communicated to the given vehicle in order to cause the vehicle to operate according to the determined vehicle operation mode.

Abstract: A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Abstract: Aspects of the disclosure relate generally to maneuvering autonomous vehicles. Specifically, the vehicle may use a laser to collect scan data for a section of roadway. The vehicle may access a detailed map including the section of the roadway. A disturbance indicative of an object and including a set of data points data may be identified from the scan data based on the detailed map. The detailed map may also be used to estimate a heading of the disturbance. A bounding box for the disturbance may be estimated using the set of data points as well as the estimated heading. The parameters of the bounding box may then be adjusted in order to increase or maximize the average density of data points of the disturbance along the edges of the bounding box visible to the laser. This adjusted bounding box may then used to maneuver the vehicle.

Abstract: A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Abstract: Aspects of the disclosure relate generally to maneuvering autonomous vehicles. Specifically, the vehicle may use a laser to collect scan data for a section of roadway. The vehicle may access a detailed map including the section of the roadway. A disturbance indicative of an object and including a set of data points data may be identified from the scan data based on the detailed map. The detailed map may also be used to estimate a heading of the disturbance. A bounding box for the disturbance may be estimated using the set of data points as well as the estimated heading. The parameters of the bounding box may then be adjusted in order to increase or maximize the average density of data points of the disturbance along the edges of the bounding box visible to the laser. This adjusted bounding box may then used to maneuver the vehicle.

Abstract: A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Abstract: Aspects of the disclosure relate generally to maneuvering autonomous vehicles. Specifically, the vehicle may use a laser to collect scan data for a section of roadway. The vehicle may access a detailed map including the section of the roadway. A disturbance indicative of an object and including a set of data points data may be identified from the scan data based on the detailed map. The detailed map may also be used to estimate a heading of the disturbance. A bounding box for the disturbance may be estimated using the set of data points as well as the estimated heading. The parameters of the bounding box may then be adjusted in order to increase or maximize the average density of data points of the disturbance along the edges of the bounding box visible to the laser. This adjusted bounding box may then used to maneuver the vehicle.

Abstract: Methods and systems for detection of a construction zone using information from a plurality of sources are described. In an example, a computing device, configured to control the vehicle, may be configured to receive information, from a plurality of sources, relating to detection of a construction zone on the road on which the vehicle is travelling. Also, the computing device may be configured to determine a likelihood of existence of the construction zone on the road, based on the information. Further the computing device may be configured to modify a control strategy associated with a driving behavior of the vehicle, based on the likelihood; and control the vehicle based on the modified control strategy.

Abstract: Methods and systems for construction zone object detection are described. A computing device may be configured to receive, from a LIDAR, a 3D point cloud of a road on which a vehicle is travelling. The 3D point cloud may comprise points corresponding to light reflected from objects on the road. Also, the computing device may be configured to determine sets of points in the 3D point cloud representing an area within a threshold distance from a surface of the road. Further, the computing device may be configured to identify construction zone objects in the sets of points. Further, the computing device may be configured to determine a likelihood of existence of a construction zone, based on the identification. Based on the likelihood, the computing device may be configured to modify a control strategy of the vehicle; and control the vehicle based on the modified control strategy.

Abstract: Methods and systems for construction zone sign detection are described. A computing device may be configured to receive a 3D point cloud of a vicinity of a road on which a vehicle is travelling. The 3D point cloud may include points corresponding to light reflected from objects in the vicinity of the road. The computing device may be configured to determine a set of points representing an area at a given height from a surface of the road, and estimate a shape associated with the set of points. Further, the computing device may be configured to determine a likelihood that the set of points represents a construction zone sign, based on the estimated shape. Based on the likelihood, the computing device may be configured to modify a control strategy associated with a driving behavior of the vehicle; and control the vehicle based on the modified control strategy.

Abstract: Methods and systems for construction zone object detection are described. A computing device may be configured to receive, from a LIDAR, a 3D point cloud of a road on which a vehicle is travelling. The 3D point cloud may comprise points corresponding to light reflected from objects on the road. Also, the computing device may be configured to determine sets of points in the 3D point cloud representing an area within a threshold distance from a surface of the road. Further, the computing device may be configured to identify construction zone objects in the sets of points. Further, the computing device may be configured to determine a likelihood of existence of a construction zone, based on the identification. Based on the likelihood, the computing device may be configured to modify a control strategy of the vehicle; and control the vehicle based on the modified control strategy.

Abstract: Methods and systems for construction zone sign detection are described. A computing device may be configured to receive a 3D point cloud of a vicinity of a road on which a vehicle is travelling. The 3D point cloud may include points corresponding to light reflected from objects in the vicinity of the road. The computing device may be configured to determine a set of points representing an area at a given height from a surface of the road, and estimate a shape associated with the set of points. Further, the computing device may be configured to determine a likelihood that the set of points represents a construction zone sign, based on the estimated shape. Based on the likelihood, the computing device may be configured to modify a control strategy associated with a driving behavior of the vehicle; and control the vehicle based on the modified control strategy.