Among other things, techniques are described for operating an autonomous vehicle station. One technique involves receiving information indicating arrival of a vehicle at a station designated for a primary service. The technique further involves measuring, using at least one sensor located in the station, a first parameter associated with the vehicle. Also, the technique involves performing, based on the information and the first parameter, a first action to provide the primary service to the vehicle. Additionally, the technique involves determining, while performing the first action, a secondary service to provide to the vehicle.

A first off-network wireless device transmits first media traffic to a third off-network wireless device employing a first session. The first off-network wireless device receives a floor request message from a second off-network wireless device. The floor request message comprises an organization field identifier and an organization identifier. The first off-network wireless device determines a call priority based on the organization field identifier and the organization identifier. Transmission of the first media traffic to the third off-network wireless device is terminated based on the call priority. The first off-network wireless device starts transmission of second media traffic to the second off-network wireless device employing a second session.

According to an embodiment of the present disclosure, a safety equipment controlling apparatus of a vehicle may include an acceleration sensor, a collision detection sensor, a brake controller, a steering controller, an airbag, a seat belt actuator, and a control circuit electrically connected to the acceleration sensor, the collision detection sensor, the brake controller, the steering controller, the airbag, and the seat belt actuator. The control circuit may be configured to obtain a longitudinal acceleration and a lateral acceleration, which are generated by a brake of the brake controller and a steering of the steering controller, using the acceleration sensor and to calculate a predicted behavior of a user of the vehicle, based on the longitudinal acceleration and the lateral acceleration.

The driving diagnostic device includes a processor, in which the processor acquires state information indicating an open or closed state of a door of a vehicle from a sensor mounted on the vehicle, determines that there is an event that indicates a change in the state when the state information satisfies a predetermined condition, and evaluates a driving operation of the vehicle in a predetermined period of time associated with the event.

Described herein are systems, methods, and non-transitory computer-readable media for implementing automated vehicle safety response measures to ensure continued safe automated vehicle operation for a limited period of time after a vehicle component or vehicle system that supports an automated vehicle driving function fails. When a critical vehicle component/system such as a vehicle computing platform fails, the vehicle is likely no longer capable of performing calculations required to safely operate and navigate the vehicle in an autonomous manner, or at a minimum, is no longer able to ensure the accuracy of such calculations. In such a scenario, the automated vehicle safety response measures disclosed herein can ensure - despite failure of the vehicle component/system -continued safe automated operation of the vehicle for a limited period of time in order to bring the vehicle to a safe stop.

This application describes a method, system, and apparatus for measuring the depth of a body of water ahead of the user's location or position. The user can be a driver of a vehicle. The apparatus includes a fording depth sensor, a second fording depth sensor, a proximity sensor to determine road angle or position ahead of the vehicle, wherein the proximity sensor is designed to operate underneath the water surface and a control unit configured to use signals of the wading depth and sensors to compute a wading depth at a location ahead of the direction of vehicle movement and/or to compute a distance ahead of the direction of vehicle movement to maximum wading depth. A method of building the apparatus, system, and vehicle is also provided.

A vehicle display control system according to the present disclosure includes an obstacle sensor, an environmental factor sensor, a memory, and a hardware processor coupled to the memory. The obstacle sensor detects an obstacle around a vehicle. The environmental factor sensor detects an environmental factor that changes a detection range of the obstacle sensor with respect to the obstacle. The hardware processor is configured to: control a display device that converts a detection indication region falling within the detection range of the obstacle sensor with respect to the obstacle, into a detection indication image representing the detection indication region from a viewpoint of a driver of the vehicle, and superimposes and displays the detection indication image on a scene around the vehicle; correct the detection range in accordance with the environmental factor; and update the detection indication region in accordance with the detection range that has been corrected.

The present invention relates to determination of a state of a vehicle on a road portion. The vehicle includes an Automated Driving System (ADS) feature. At first, map data associated with the road portion, positioning data indicating a pose of the vehicle on the road, and sensor data of the vehicle are obtained. Then, a plurality of filters for the road portion are initialized. Further, one or more sensor data point(s) in the obtained sensor data is associated to a corresponding map-element of the obtained map data to determine one or more normalized similarity score(s). Now, based on the determined one or more normalized similarity score(s), one or more multivariate time-series data are also determined and provided as input to a trained machine-learning algorithm. Then, one of the initialized filters is selected by the machine learning algorithm to indicate a current state of the vehicle on the road portion.

A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.