Abstract: The present disclosure provides systems and methods to obtain autonomous vehicle passenger feedback. In particular, the systems and methods of the present disclosure can detect an occurrence of a driving event performed by an autonomous vehicle and, in response, provide an interactive user interface that enables a passenger of the autonomous vehicle to enter passenger feedback regarding the occurrence of the driving event. Thus, the systems and methods of the present disclosure can actively prompt passenger feedback in response to a particular instance of a specific type of driving event, thereby enabling improved collection of information about autonomous vehicle ride quality relative to specific types of driving events.

Abstract: A laser imaging, detection and ranging (LIDAR) system for an autonomous vehicle (AV) includes a LIDAR sensor comprising a plurality of configurable parameters, and a sensor controller. The sensor controller can execute sensor configuration logic to adjust one or more of the plurality of configurable parameters of the LIDAR sensor in response to AV feedback from a control system of the AV.

Abstract: A network system analyzes data samples using embeddings based on, for example, symbolic representations of the data samples or representations in latent dimension space. The network system coordinates providers who provide geographical location-based services to users. The network system may receive data samples from the client device of a provider. For instance, a sensor of the client device captures the data samples during a transportation service along a particular route. To verify that the data samples accurately indicate the location or movement of the provider, the network system can generate a test embedding representing the data samples and compare the test embedding with a reference embedding. The reference embedding is generated based on data samples captured for other similar services, e.g., corresponding to providers who also provided transportation services along the same particular route.

Abstract: The present disclosure provides systems and methods for controlling autonomous vehicles. In one example implementation, a method includes providing for display to a passenger of an autonomous vehicle, by a computing system having one or more computing devices, a user interface on a display device. The user interface includes at least one interface element associated with directing movement of the autonomous vehicle. The method includes receiving, by the computing system, a user interaction for a time period directed to the interface element. During the time period, the method includes providing, by the computing system, one or more signals indicative of the user interaction to control the autonomous vehicle to autonomously travel along a predetermined path.

Abstract: A network system dynamically determines a route, including start and end points, for vehicles in a transportation network. The transportation network receives a service request from a user of the transportation network including an origin location for the trip and a destination location for the trip. The transportation network then generates a waypoint plan for one or more vehicles, which includes the requested origin and destination in addition to any previously requested origins and destinations included in the vehicles current route. The network system then determines a directionality for each of the waypoints in the waypoint plan and retrieves candidate start and end points that have an associated directionality within a threshold angle of the directionality of each waypoint and are proximate to the waypoint. The network system evaluates each combination of retrieved candidate points to select a route for the vehicle.

Abstract: A system for monitoring a vehicle used in providing a service is disclosed. The system detects when the service is complete, and upon detecting that the service is complete, the system determines, from one or more sensors of the vehicle, that an object which should have been removed from the vehicle after completion of the transport service, remains in the vehicle. The system then automatically initiates performance of a remedial action.

Abstract: Methods and systems for alerting a vehicle driver and/or a passenger via wireless communications. One or more conditions with respect to a vehicle are monitored. A change in the condition(s) is detected and a signal automatically and wirelessly transmitted to a computing device, wherein the signal is indicative of the change in condition(s). The driver and/or the passenger is then alerted to the change in condition(s) in response to transmitting the signal to the computing device (e.g., a tablet computing device, smartphone, smartwatch, other wearable device, in-vehicle system, etc.) associated with the driver and/or the vehicle passenger.

Abstract: A transport facilitation can identify a mass egress area within a given region and receive a pick-up request from a rider device within the mass egress area. In response to receiving the pick-up request, the transport facilitation system transmit a match code to the rider device. The system may then receive an indication of the match code from a driver device, the indication signifying a pick-up event between a requesting user of the rider device and an available driver of the driver device within the mass egress area, and record a state for both the rider device and the driver device indicating an initiation of a ride to a destination of the requesting user.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a method can include receiving trip data that includes information associated with a request for the autonomous vehicle at a pick-up location. The autonomous vehicle can then travel to the pick-up location. The autonomous vehicle can detect one or more signals associated with the trip data. In response to determining that the one or more signals satisfy one or more broadcast criteria associated with authorizing access to the autonomous vehicle, the autonomous vehicle can activate one or more vehicle systems associated with fulfilling the request for the autonomous vehicle at the pick-up location. The one or more broadcast criteria can be based in part on one or more properties of the one or more signals.

Abstract: Systems and methods for detecting and classifying objects that are proximate to an autonomous vehicle can include receiving, by one or more computing devices, LIDAR data from one or more LIDAR sensors configured to transmit ranging signals relative to an autonomous vehicle, generating, by the one or more computing devices, a data matrix comprising a plurality of data channels based at least in part on the LIDAR data, and inputting the data matrix to a machine-learned model. A class prediction for each of one or more different portions of the data matrix and/or a properties estimation associated with each class prediction generated for the data matrix can be received as an output of the machine-learned model. One or more object segments can be generated based at least in part on the class predictions and properties estimations. The one or more object segments can be provided to an object classification and tracking application.

Abstract: A method for providing on-demand service information is provided. One or more processors determine, for a given geographic region, position information for each of a plurality of requesters for an on-demand service and position information for each of a plurality of service providers that can provide the on-demand service. A plurality of sub-regions is identified for the given geographic region. Based, at least in part, on the position information of the requesters and the service providers, one or more sub-regions are determined as being under-supplied by the plurality of service providers as compared to one or more other sub-regions. Information identifying the under-supplied sub-regions are provided to one or more service provider devices.

Abstract: A computing system can receive a first transport request from a computing device of a respective user while a rider application is operating in the default mode, and transmit a confirmation indicating information about a paired driver that is assigned to the first transport request to the computing device of the respective user. The computing system may further receive a second transport request from the computing device of the respective user while the rider application is operating in the secondary mode, and transmit data comprising a unique match code to the computing device of the respective user, causing the rider application to generate and output digital signature.

Abstract: A control system of a self-driving vehicle (SDV) can process sensor data from the sensor system to autonomously operate acceleration, braking, and steering systems of the SDV throughout a given region. The control system can receive a transport directive from a transport facilitation system to service a pick-up request from a requesting user, the transport directive indicating an inputted pick-up location by the requesting user. The control system can then autonomously operate the acceleration, braking, and steering systems along a current route to a pick-up area encompassing the inputted pick-up location. The control system can further determine a corresponding set of pick-up location options for the pick-up area, and as the SDV approaches the pick-up area, perform a hierarchical operation to identify, via the sensor data, an optimal pick-up location to rendezvous with the requesting user.

Abstract: An anomaly detection system is provided in connection with a transport service. The anomaly detection system can monitor progress of the transport service using received location data from driver and/or user mobile devices. During the progress of the transport service, the anomaly detection system can determine a probable anomaly by determining that a geographic position of the driver's vehicle has remained static for a period of time exceeding a threshold period of time, and in response to determining the probable anomaly, transmit a notification message to at least one of the driver and user mobile devices.

Abstract: A server provides communications to users and evaluates actions of the users after the provision of the communications to determine how receptive the various users are to such communications. Receptiveness is determined through a training process involving the formation of training sets based on whether users a test set of users were receptive to a previous set of communications, the derivation of relevant features for the users of the training set, the training of a model based on the derived features, and the use of the model for users whose receptiveness is to be estimated. With the most receptive users identified, the server can focus in future on communicating with those most receptive users.

Abstract: A system that incorporates teachings of the present disclosure may include, for example, a communication device having a controller to detect a selection of an image from a graphical user interface, identify an entry in a contact book responsive to the detected selection of the image, and retrieve one or more communication identifiers associated with the party from the entry of the contact book for initiating a communication session. Other embodiments are disclosed.

Abstract: Examples include a network computer system and/or service which operates to remotely monitor vehicles to detect and characterize driving actions of drivers with respect to specific road segments of a roadway, enabling driving actions performed by multiple drivers at a specific road segment to be characterized and modeled. Models can inform municipalities about potential traffic hazards or other safety challenges. Individual drivers can also be measured against the model to help understand driver performance. Validating vehicle data against baseline values can also detect spoofing of that data.

Abstract: A control system of an autonomous vehicle (AV) can receive sensor data from a sensor array of the AV. The sensor data can comprise raw radar data from a radar system of the sensor array, and in many examples, a live LIDAR data from a LIDAR system of the sensor array. In certain implementations the control system can access a current localization map or a live LIDAR map of a surrounding area of the AV, and compare the raw radar data with the current localization map or the live LIDAR map to identify multipath objects in the raw radar data. The control system may then remove the multipath objects or track the actual objects corresponding to the multipath objects accordingly.