A vehicle computing system validates location data received from a Global Navigation Satellite System receiver with other sensor data. In one embodiment, the system calculates velocities with the location data and the other sensor data. The system generates a probabilistic model for velocity with a velocity calculated with location data and variance associated with the location data. The system determines a confidence score by applying the probabilistic model to one or more of the velocities calculated with other sensor data. In another embodiment, the system implements a machine learning model that considers features extracted from the sensor data. The system generates a feature vector for the location data and determines a confidence score for the location data by applying the machine learning model to the feature vector. Based on the confidence score, the system can validate the location data. The validated location data is useful for navigation and map updates.

A radar sensing system including transmit antennas and receive antennas, transmitters, receivers, and a controller. The system further includes a transmit antenna switch selectively coupling each of the transmitters to a respective transmit antenna, and a receive antenna switch selectively coupling at least one receiver of the receivers to respective receive antennas. A quantity of receivers is different from a quantity of the receive antennas. The controller is operable to select a quantity of receivers to be coupled to receive antennas to realize a desired quantity of virtual receivers. The controller is operable to select an antenna pattern as defined by the selected quantity of receivers coupled to receive antennas.

An object detection apparatus detects a target object present in a periphery of a moving body. The object detection apparatus derives recognition information indicating a state of a target object, and predicts a state of the target object at a next second observation timing, based on the recognition information derived at a first observation timing. The object detection apparatus derives a score based on a degree of difference between a state of the target object observed at the second observation timing and a next state of the target object predicted at the first observation timing. The object detection apparatus derives a reliability level by statistically processing scores related to the target object derived at a plurality of observation timings from past to present. In response to the reliability level satisfying a predetermined reference, the object detection apparatus determines that the target object related to the reliability level is actually present.

In many applications such as automobiles on busy highways, if a lot of vehicles on road are equipped with Doppler radars to help improve driving safety, no matter human-driven or auto-driven, if the radars use same frequency band, avoiding interference between them is a hard task. Assigning distinct frequencies is one of the solutions, however not only it wastes expensive spectrum resource, but also the task itself to dynamically assign frequency to vehicles randomly come together becomes a hard one to do. The disclosed invention of Doppler group radar will allow radar devices to work together using shared frequency band without interfering one another, without sacrificing performance, and without much increase in costs.

Image recording device provided with a fixed part for at least partial inclusion in and/or on the body of a motor vehicle, and a movable part which comprises at least one optical element, the optical element comprising at least one optical sensor and/or a lens unit, and an adjustment device provided with a power source, for adjusting the movable part between a first position and a second position, wherein during use in the first position the movable part is wholly or partly included in the body, and in the second position the movable part extends at an angle with respect to the body, such that at least the optical element carried by the movable part extends at least partly outside the body, wherein the movable part pivots from the second position about a, preferably substantially vertical, axis to the first position and vice versa.

An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.

Techniques for estimating a height range of an object in an environment are discussed herein. For example, a sensor, such as a lidar sensor, can capture three-dimensional data of an environment. The sensor data can be associated with a two-dimensional representation. A ground surface can be removed from the sensor data, and clustering techniques can be used to cluster remaining sensor data provided in a two-dimensional representation to determine object(s) represented therein. A height of a sensor object can be represented as a first height based on an extent of the sensor data associated with the object and can be represented as a second height based on beam spreading aspects of the sensor data and/or sensor data associated with additional objects. Thus, a minimum and/or maximum height of an object can be determined in a robust manner. Such height ranges can be used to control an autonomous vehicle.

In some implementations, a method of verifying operation of a sensor is provided. The method includes causing a sensor to obtain sensor data at a first time, wherein the sensor obtains the sensor data by emitting waves towards a detector. The method also includes determining that the detector has detected the waves at a second time. The method further includes receiving the sensor data from the sensor at a third time. The method further includes verifying operation of the sensor based on at least one of the first time, the second time, or the third time.

Certain aspects of the present disclosure provide techniques for sensor calibration. First sensor data is received from a first sensor and second sensor data is received from a second sensor, where the first sensor data and the second sensor data each indicate detected objects in a space. The first sensor data is transformed using a first transformation profile to convert the first sensor data to a coordinate frame of the second sensor data. The first transformation profile is refined based on a difference between the transformed first sensor data and the second sensor data.

The present disclosure relates to an information processing apparatus, an information processing method, and a program that allow for appropriately pulling over to a safe road shoulder when an emergency occurs during automated driving. On the basis of distance information from a depth sensor or the like, a travelable region available for a vehicle to travel is set like an occupancy map, and image attribute information is generated from an image by semantic segmentation. On the basis of the image attribute information, an evacuation space is set in the travelable region in accordance with the situation of the road surface of the travelable region, and thus an evacuation space map is created. The present disclosure can be applied to a mobile object.