Abstract: Aspects of the disclosure relate to evaluating pullovers for autonomous vehicles. In one instance, a set of potential pullover locations within a predetermined distance of a destination may be identified. Whether any of the potential pullover locations of the set include one or more of a plurality of predetermined types of regions of interest where a vehicle should not park for an extended period of time may be determined. A pullover location is identified based on the determination. The identified pullover location may be compared to a pullover location identified by autonomous vehicle control software in order to evaluate the pullover location identified by the autonomous vehicle control software.

Abstract: The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.

Abstract: Aspects of the disclosure relate to evaluating pullovers for autonomous vehicles. In one instance, a set of potential pullover locations within a predetermined distance of a destination may be identified. Whether any of the potential pullover locations of the set include one or more of a plurality of predetermined types of regions of interest where a vehicle should not park for an extended period of time may be determined. A pullover location is identified based on the determination. The identified pullover location may be compared to a pullover location identified by autonomous vehicle control software in order to evaluate the pullover location identified by the autonomous vehicle control software.

Abstract: The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.

Abstract: Aspects of the disclosure relate to evaluating pullovers for autonomous vehicles. In one instance, a set of potential pullover locations within a predetermined distance of a destination may be identified. Whether any of the potential pullover locations of the set include one or more of a plurality of predetermined types of regions of interest where a vehicle should not park for an extended period of time may be determined. A pullover location is identified based on the determination. The identified pullover location may be compared to a pullover location identified by autonomous vehicle control software in order to evaluate the pullover location identified by the autonomous vehicle control software.

Abstract: The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.

Abstract: The disclosure relates to controlling a vehicle having an autonomous driving mode. An example method may include identifying a pullover location for the vehicle. As the vehicle approaches the pullover location, information identifying one or more road users may be received. A behavior prediction for each of the one or more road users may be received. One of one of the one or more road users may be identified based on the received behavior predictions for each of the one or more road users and a collision zone for the vehicle. After waiting for the identified one to pass the vehicle, the vehicle may be maneuvered in the autonomous driving mode into the pullover location.

Abstract: Aspects of the disclosure relate to evaluating quality of a predetermined pullover location for an autonomous vehicle. For instance, a plurality of inputs for the predetermined pullover location may be received. The plurality of inputs may each include a value representative of a characteristic of the predetermined pullover location. The plurality of inputs may be combined to determine a pullover quality value for the predetermined pullover location. The pullover quality value may be provided to a vehicle in order to enable the vehicle to select a pullover location for the vehicle.

Abstract: To determine an impression metric for an organization, a server device generates a statistical model for estimating the impression metric using machine learning techniques. The server device obtains training data for the statistical model by randomly selecting geographic locations within a geographic area and determining the number of users eligible to receive a particular type of advertisement for each randomly selected geographic location. For example, a user may be deemed eligible when displaying the geographic location via a mapping application. When an organization requests an estimate of a number of impressions for an advertising campaign, the server device applies data included in the request (e.g., the time period for the advertising campaign, the number of organization locations, identifiers for the organization locations such as geographic coordinates or an address, etc.) to the statistical model to estimate an impression metric for the organization.

Abstract: To determine an impression metric for an organization, a server device generates a statistical model for estimating the impression metric using machine learning techniques. The server device obtains training data for the statistical model by randomly selecting geographic locations within a geographic area and determining the number of users eligible to receive a particular type of advertisement for each randomly selected geographic location. For example, a user may be deemed eligible when displaying the geographic location via a mapping application. When an organization requests an estimate of a number of impressions for an advertising campaign, the server device applies data included in the request (e.g., the time period for the advertising campaign, the number of organization locations, identifiers for the organization locations such as geographic coordinates or an address, etc.) to the statistical model to estimate an impression metric for the organization.