The invention relates to a method and a motor vehicle comprising a sensor assembly for a 360° detection of the motor vehicle surroundings. The sensor assembly has multiple sensors of the same type, wherein each of the multiple sensors has a specified detection region and the sensors are distributed around the exterior of the motor vehicle such that the detection regions collectively provide a complete detection zone which covers the surroundings in a complete angle about the motor vehicle at a specified distance from the motor vehicle. The sensors are each designed to detect the surroundings in their respective detection region as respective sensor data in respective successive synchronized time increments. The sensor assembly has a pre-processing mechanism which fuses the sensor data of each of the sensors in order to generate a three-dimensional image of the surroundings for a respective identical time increment and provides same in a common database.

Some embodiments include a method of generating an environment reference model for positioning comprising: receiving multiple data sets representing a scanned environment including information about a type of sensor used and data for determining an absolute position of objects or feature points represented by the data sets; extracting one or more objects or feature points from each data set; determining a position of each object or feature point in a reference coordinate system; generating a three-dimensional vector representation of the scanned environment aligned with the reference coordinate system including representation of the objects or feature points at corresponding locations; creating links between the objects or feature points in the three-dimensional vector model with an identified type of sensor by which they can be detected in the environment; and storing the three-dimensional vector model representation and the links in a retrievable manner.

A system for intelligently implementing an autonomous agent that includes an autonomous agent, a plurality of infrastructure devices, and a communication interface. A method for intelligently calibrating infrastructure (sensing) devices using onboard sensors of an autonomous agent includes identifying a state of calibration of an infrastructure device, collecting observation data from one or more data sources, identifying or selecting mutually optimal observation data, specifically localizing a subject autonomous agent based on granular mutually optimal observation data, identifying dissonance in observation data from a perspective of a subject infrastructure device, and recalibrating a subject infrastructure device.

An outside sensor unit includes an outside sensor, a main bracket, a support bracket, a rotation device, and a position adjustment device. The outside sensor detects the outside of a vehicle. The main bracket is attached to a vehicle body. The support bracket supports the outside sensor and is attached to the main bracket. The rotation device has a rotation axis line which is substantially parallel to a roll axis of the vehicle and connects the support bracket and the main bracket together rotatably around the rotation axis line. The position adjustment device is capable of adjusting the relative rotation position between the support bracket and the main bracket around the rotation axis line.

A method for obstacle detection and navigation of a vehicle using resolution-adaptive fusion includes performing, by a processor, a resolution-adaptive fusion of at least a first three-dimensional (3D) point cloud and a second 3D point cloud to generate a fused, denoised, and resolution-optimized 3D point cloud that represents an environment associated with the vehicle. The first 3D point cloud is generated by a first-type 3D scanning sensor, and the second 3D point cloud is generated by a second-type 3D scanning sensor. The second-type 3D scanning sensor includes a different resolution in each of a plurality of different measurement dimensions relative to the first-type 3D scanning sensor. The method also includes detecting obstacles and navigating the vehicle using the fused, denoised, and resolution-optimized 3D point cloud.

A computer implemented method for determining the position of a vehicle, wherein the method comprises: determining at least one scan comprising a plurality of detection points, wherein each detection point is evaluated from a signal received at the at least one sensor and representing a location in the vehicle environment; determining, from a database, a predefined map, wherein the map comprises a plurality of elements in a map environment, each of the elements representing a respective one of a plurality of static landmarks in the vehicle environment, and the map environment representing the vehicle environment; matching the plurality of detection points and the plurality of elements of the map; determining the position of the vehicle based on the matching; wherein the predefined map further comprises a spatial assignment of a plurality of parts of the map environment to the plurality of elements, and wherein the spatial assignment is used for the matching.

According to one aspect, a system for robust localization may include a scan accumulator, a scan matcher, a transform maintainer, and a location fuser. The scan accumulator may receive a set of sensor data from a set of sensors mounted on a vehicle. The scan accumulator may generate a sensor scan point cloud output by transforming the set of sensor data from each sensor frame to a corresponding vehicle frame and calculate a fitness score, a transformation probability, and a mean elevation angle used to determine a scan confidence for the sensor data. The transform maintainer may receive GPS data, the scan confidence, and the matched sensor scan point cloud output and map tile point cloud data from the scan matcher, and determine whether the GPS data or the matched sensor scan point cloud output and map tile point cloud data is utilized for a map-to-odometer transformation output.

A computer implemented method for determining the position of a vehicle, wherein the method comprises: determining at least one scan comprising a plurality of detection points, wherein each detection point is evaluated from a signal received at the at least one sensor and representing a location in the vehicle environment; determining, from a database, a predefined map, wherein the map comprises a plurality of elements in a map environment, each of the elements representing a respective one of a plurality of static landmarks in the vehicle environment, and the map environment representing the vehicle environment; matching the plurality of detection points and the plurality of elements of the map; determining the position of the vehicle based on the matching; wherein the predefined map further comprises a spatial assignment of a plurality of parts of the map environment to the plurality of elements, and wherein the spatial assignment is used for the matching.

A system for intelligently implementing an autonomous agent that includes an autonomous agent, a plurality of infrastructure devices, and a communication interface. A method for intelligently calibrating infrastructure (sensing) devices using onboard sensors of an autonomous agent includes identifying a state of calibration of an infrastructure device, collecting observation data from one or more data sources, identifying or selecting mutually optimal observation data, specifically localizing a subject autonomous agent based on granular mutually optimal observation data, identifying dissonance in observation data from a perspective of a subject infrastructure device, and recalibrating a subject infrastructure device.

A radar authentication method includes after obtaining output data of a to-be-authenticated radar, a computer device that first invokes a prediction model to obtain predicted data of the to-be-authenticated radar based on the output data of the to-be-authenticated radar, where the prediction model is obtained through training based on output data of a target radar. Then the computer device verifies, based on the predicted data of the to-be-authenticated radar and the output data of the to-be-authenticated radar, whether the to-be-authenticated radar and the target radar are the same radar.