Abstract: The present disclosure is directed to a computer system and techniques for updating map data used for navigating an autonomous vehicle. In some embodiments, the computer system is configured to obtain map data including a starting location and a destination location; obtain a plurality of travel times corresponding to a plurality of respective possible routes for traveling from the starting location to the destination location; obtain benefit information for at least a first respective possible route; and determine a selected route based on the travel times and the benefit information. In some embodiments, the computer system is configured to obtain a route between a starting location and a destination location; obtain map-age information for the route; determine a first set of driving parameters for the autonomous vehicle based on the map-age information; and navigate the autonomous vehicle along the route using the first set of driving parameters.

Abstract: Among other things, sensor signals are received using a vehicle comprising an autonomous driving capability. A risk associated with operating the vehicle is identified based on the sensor signals. The autonomous driving capability is modified in response to the risk. The operation of the vehicle is updated based on the modifying of the autonomous capability.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: In general, an indication is received through a user interface of an intention of a potential rider to use an autonomous vehicle. In response to the receipt of the indication, a hailing request is sent by a signaling mode to at least one autonomous vehicle that can receive the hailing request directly in accordance with the signaling mode.

Abstract: Provided are methods for maintaining calibration of an IBIS camera, which can include receiving a first initial extrinsic calibration of a first camera having a first sensor; determining a first translation of the first sensor based on a second position thereof when the first camera captures a first image; determining a first extrinsic translation shift based at least upon the first translation; and updating the first initial extrinsic calibration to yield a first modified extrinsic calibration for the first camera. Systems and computer program products are also provided.

Abstract: The subject matter described in this specification is directed to a computer system and techniques for determining a location of an autonomous vehicle. The computer system is configured to determine the location using localization data from multiple data sources. When the localization data from the multiple data sources is unavailable or inaccurate, the computer system is configured to determine the location using predefined features of the environment.

Abstract: Among other things, techniques are described for cross-modality active learning for object detection. In an example, a first set of predicted bounding boxes and a second set of predicted bounding boxes is generated. The first set of predicted bounding boxes and the second set of predicted bounding boxes are projected into a same representation. The projections are filtered, wherein predicted bounding boxes satisfying a maximum confidence score are selected for inconsistency calculations. Inconsistencies are calculated across the projected bounding boxes based on filtering the projections. An informative scene is extracted based on the calculated inconsistencies. A first object detection neural network or a second object detection neural network is trained using the informative scenes.

Abstract: Techniques are disclosed for creating and presenting a graphical user interface for display of autonomous vehicle behaviors. The techniques include determining a trajectory of a vehicle operating in a real-world environment. Sensors of the vehicle obtain sensor data representing an object in the real-world environment. A maneuver of the vehicle to avoid a collision with the object is predicted based on the sensor data and the trajectory of the vehicle. It is determined that a passenger comfort level of a passenger riding in the vehicle will decrease based on the maneuver of the vehicle. The passenger comfort level is measured by passenger sensors of the vehicle. A graphical user interface is generated including representations of the vehicle, the object, and a graphic, text, or a symbol alerting the passenger of the predicted maneuver. The graphical user interface is transmitted to a display device of the vehicle.

Abstract: Among other things, techniques are described for receiving, from at least one sensor of a vehicle, a sensor measurement indicative of at least one of a sound or a vibration associated with a road element; identifying the road element based on a pattern in the sensor measurement; determining, based on the road element, a vehicle behavior for the vehicle; and controlling the vehicle to operate according to vehicle behavior.

Abstract: An over-the-air (OTA) mobility service platform (MSP) is disclosed that provides a variety of OTA services, including but not limited to: updating software OTA (SOTA), updating firmware OTA (FOTA), client connectivity, remote control and operation monitoring. In some exemplary embodiments, the MSP is a distributed computing platform that delivers and/or updates one or more of configuration data, rules, scripts and other services to vehicles and IoT devices. In some exemplary embodiments, the MSP optionally provides data ingestion, storage and management, data analytics, real-time data processing, remote control of data retrieving, insurance fraud verification, predictive maintenance and social media support.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Techniques for determination of an optimal spatiotemporal sensor configuration for navigation of a vehicle include generating a model of a virtual vehicle operating in an environment. The model of the virtual vehicle includes a virtual sensor having a virtual viewing range. The virtual viewing range of the virtual sensor is segregated into frustums. The virtual viewing range of the virtual sensor corresponds to a viewing range of a sensor of a vehicle operating in the environment. A geometric viewport is generated including pixels. The geometric viewport has a height corresponding to a number of rays emitted from the virtual sensor. The geometric viewport is segregated into sections. Each section corresponds to a frustum. A virtual point cloud of the virtual sensor is rendered. The virtual point cloud includes coordinate positions representing a portion of the environment located within the virtual viewing range of the virtual sensor.

Abstract: Embodiments for operational envelope detection (OED) with situational assessment are disclosed. In some embodiments, a method comprises: determining at least one current or future constraint for a trajectory of a vehicle in an environment that is associated with the vehicle becoming immobile for an extended period of time; determining a stopping-reason for immobility of the vehicle based on determining the at least one current or future constraint for the trajectory of the vehicle in the environment; identifying a timeout threshold based on the stopping-reason, wherein the timeout threshold is an amount of time a planning system of the vehicle will wait before initiating at least one remedial action to address the immobility; identifying that the timeout threshold is satisfied; and initiating the at least one remedial action for the vehicle based on the identifying that the timeout threshold is satisfied.

Abstract: A method for operating a vehicle includes detecting a traffic light located at a first spatiotemporal location based on a first digital video stream captured by a first camera and a second digital video stream captured by a second camera. It is determined that the vehicle is located at a second spatiotemporal location by validating first location data received from sensors against second location data obtained by filtering the first location data. It is determined that the traffic light is expected at the first spatiotemporal location based on a semantic map referenced by the second spatiotemporal location. Responsive to determining that the traffic light is expected at the first spatiotemporal location, a traffic signal of the traffic light is detected based on the two digital video streams. A trajectory is determined in accordance with the traffic signal. A control circuit operates the vehicle in accordance with the trajectory.

Abstract: Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

Abstract: Among other things, we describe systems and method for improving vehicle operations using movable sensors. A vehicle can be configured with one or more sensors having the capability to be extended and/or rotated. The one or more movable sensors can be caused to move based on a determined context of the vehicle, to capture additional data associated with the environment in which the vehicle is operating.

Abstract: The subject matter described in this specification is directed to a system and techniques for operating an autonomous vehicle (AV) at a multi-way stop intersection. After detecting the AV is at a primary stopline of the multi-way stop intersection, a planned travel path though the multi-way stop intersection is obtained. If the planned travel path of the AV through the multi-way stop intersection satisfies a set of one or more clearance criteria, the AV proceeds past the primary stopline. The clearance criteria include a criterion that is satisfied in response to detecting the AV is clear to safely merge into a travel lane corresponding to the planned travel path.

Abstract: Among other things, we describe techniques for adjusting lateral clearance for a vehicle using a multi-dimensional envelope. A trajectory is generated for the vehicle. A multi-dimensional envelope is generated indicating a drivable region for the vehicle and containing the trajectory. One or more objects are identified located along or adjacent to the trajectory. At least one dimension of the generated multi-dimensional envelope is adjusted to adjust a lateral clearance between the vehicle and the identified one or more objects. A control module of the vehicle navigates the vehicle along the multi-dimensional envelope.

Abstract: Among other things, we describe techniques for estimating a speed profile for a proposed trajectory for a vehicle and operating the vehicle along the proposed trajectory according to the speed profile, including a method for: obtaining, by a planning circuit on a vehicle, a proposed trajectory for the vehicle in response to a driving scenario; obtaining, by the planning circuit, an estimated speed profile, and a confidence score, wherein the confidence score represents a similarity of the estimated speed profile to an actual speed profile that would be generated by a control circuit for the proposed trajectory; determining whether the confidence score meets a confidence threshold; and in accordance with a determination that the confidence score exceeds the confidence threshold, operating, by a control circuit on the vehicle, the vehicle along the proposed trajectory.