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.

Camera data and radar echoes are received from the surroundings. At least one radar echo is assigned to a delimiting frame of an object detected on the basis of a camera, the delimiting frame being generated using the camera data by comparing corresponding azimuth angles and specified distances of the radar echo and the object detected on the basis of a camera. In the event of a successful assignment, a distance which is assumed on the basis of a camera is corrected according to the distance of the respective detected object in the surroundings, said distance being determined in a radar-based manner. The respective delimiting frame together with the corrected distance is then output as an object data set which indicates a successful object detection.

A method for radar-based monitoring of a rearward area of a truck. The truck comprises a radar device and at least one semitrailer. The method comprises the steps of ascertaining various objects and their position using the radar device, determining an alignment of the trailer relative to the radar device, determining the objects that, based on their position, are concealed for the radar device as a result of the alignment of the semitrailer, and ascertaining, on the basis of the alignment of the semitrailer and an ascertained reflection of the radar waves on the semitrailer, the true position of the objects ascertained as concealed.

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 techniques and systems herein enable target-velocity estimation using position variance. Specifically, a plurality of detections of a target are received for respective times as the target moves relative to a host vehicle. Based on the detections, two-dimensional positions of the target relative to the host vehicle are determined for the respective times. Based on the positions of the target at the respective times, a first variance is determined for a first dimension of the positions, and a second variance is determined for a second dimension of the positions. Based on the first and second variances, an estimated velocity of the target is calculated. By basing the estimated velocity on the variances of the positions, more-accurate estimated velocities may be generated sooner, thus enabling better performance of downstream operations.

A method for detecting objects and labeling the objects with distances in an image includes steps of: obtaining a thermal image from a thermal camera, an RGB image from an RGB camera, and radar information from an mmWave radar; adjusting the thermal image based on the RGB image to generate an adjusted thermal image, and generating a fused image based on the RGB image and the adjusted thermal image; generating a second fused image based on the fused image and the radar information; detecting objects in the images, and generating, based on the fused image, another fused image including bounding boxes marking the objects; and determining motion parameters of the objects.

A track fusion method for an unmanned surface vehicle includes: (a) obtaining perception information of the unmanned surface vehicle, where the perception information includes GPS data information and radar data information; (b) pre-processing the radar data information to obtain target radar information; (c) constructing a track correlation model; and performing track correlation between the GPS data information and the target radar information based on the track correlation model; and (d) constructing a fusion data weight allocation model; and subjecting between the GPS data information and the target radar information correlated therewith to track fusion based on the fusion data weight allocation model. This application further provides a track fusion device for unmanned surface vehicles.

This document describes techniques, apparatuses, and systems for sensor fusion for object-avoidance detection, including stationary-object height estimation. A sensor fusion system may include a two-stage pipeline. In the first stage, time-series radar data passes through a detection model to produce radar range detections. In the second stage, based on the radar range detections and camera detections, an estimation model detects an over-drivable condition associated with stationary objects in a travel path of a vehicle. By projecting radar range detections onto pixels of an image, a histogram tracker can be used to discern pixel-based dimensions of stationary objects and track them across frames. With depth information, a highly accurate pixel-based width and height estimation can be made, which after applying over-drivability thresholds to these estimations, a vehicle can quickly and safely make over-drivability decisions about objects in a road.

A vehicle (AV) includes a radar sensor and a hardware logic component. The radar sensor receives a radar return from a driving environment of the vehicle and outputs radar data that is indicative of the return to the hardware logic component. The hardware logic component further receives data indicative of a velocity of the vehicle from a sensor mounted on the vehicle. The hardware logic component is configured to employ synthetic aperture radar (SAR) techniques to compute a three-dimensional position of a point on a surface of an object in the driving environment of the vehicle based upon the radar data and the velocity of the vehicle.

This document describes techniques, apparatuses, and systems for a parameter defined stepped frequency waveform for a radar system. A radar system transmits radar transmit signals including a parameter defined stepped frequency waveform with a specific change in frequency between chirps. The specified change in frequency may increase the signal to noise ratio of radar receive signals reflected off an object in the field of view. The radar receive signals may then be transformed into the frequency domain to determine a range and range rate of the object in the field of view. The range and range rate determined from the representation of the radar receive signals in the frequency domain may be output to a radar tracker to enable tracking of the object in the field of view. In doing so, accurate radar tracks may be generated that robustly track objects in the field of view of the radar system.