Disclosed are systems, apparatuses, processes, and computer-readable media to implement a heterogenous biometric authentication process in a control system. A process includes obtaining radar information identifying measured properties of at least one object in an environment, generating pre-processed radar information for input into a neural network at least in part by processing the obtained radar information, generating an object detection output for the at least one object at least in part by detecting the at least one object using the neural network with the pre-processed radar information as input, and modifying, based on the obtained radar information, the object detection output for the at least one object.

This document describes “Multi-domain Neighborhood Embedding and Weighting” (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.

A computer implemented method for radar data processing includes the following steps carried out by computer hardware components: acquiring radar data from a radar sensor mounted on a vehicle; determining at least one of a speed of the vehicle or a steering wheel angle of the vehicle; and determining a subset of the radar data for processing based on the at least one of the speed of the vehicle or the steering wheel angle of the vehicle.

This document describes techniques and systems for improving accuracy of predictions on radar data using vehicle-to-vehicle (V2V) technology. V2V communications data and the matching sensor data related to one or more vehicles in the vicinity of a host vehicle are collected. The V2V data is used as label data and the radar data is used as the input data for training the model. The training may either occur onboard the host vehicle or remotely. Further, multiple host vehicles may contribute data to train the model. Once the model has been updated with the included training, the updated model is deployed to the sensor tracking system of the host vehicle. By using the dataset that includes the V2V communications data and the matching sensor data, the updated model may accurately track other vehicles and enable the host vehicle to utilize advanced driver-assistance systems safely and reliably.

A device, a memory medium, a computer program, and a computer-implemented method for validating a data-based model for classifying an object into a class for an object type or a function type for a driver assistance system of a vehicle. The classification is determined as a function of a digital signal using the data-based model. A reference classification for the object is determined as a function of the digital signal, using a reference model. It is checked, as a function of the classification and the reference classification, whether or not the classification of the data-based model for the object is correct, and the data-based model is validated or not validated, depending on whether or not the classification is correct. The classification and the reference classification are determined for a set of digital signals that are associated with different distances between the object and a reference point.

Techniques for identifying curbs are discussed herein. For instance, a vehicle may generate sensor data using one or more sensors, where the sensor data represents points associated with a driving surface and a sidewalk. The vehicle may then quantize the points into distance bins that are located laterally along the driving direction of the vehicle in order to generate spatial lines. Next, the vehicle may determine separation points for the spatial lines, where the separation points are configured to separate the points associated with the driving surface from the points associated with the sidewalk. The vehicle may then generate, using the separation points, a curve that represents the curb between the driving surface and the sidewalk. This way, the vehicle may use the curve while navigating, such as to avoid the curb and/or stop at a location that is proximate to the curb.

Sensors, including radar sensors, may be used to detect objects in an environment. In an example, a vehicle may include one or more radar sensors that sense objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. A plurality of radar points from one or more radar scans are associated with a sensed object and a representation of the sensed object is determined from the plurality of radar points. The representation may be compared to track information of previously-identified, tracked objects. Based on the comparison, the sensed object may be associated with one of the tracked objects, and, alternatively, the track information may be updated based on the representation. Conversely, the comparison may indicate that the sensed object is not associated with any of the tracked objects. In this instance, the representation may be used to generate a new track, e.g., for the newly-sensed object.

Sensors, including radar sensors, may be used to detect objects in an environment. In an example, a vehicle may include one or more radar sensors that sense objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. A plurality of radar points from one or more radar scans are associated with a sensed object and a representation of the sensed object is determined from the plurality of radar points. The representation may be compared to track information of previously-identified, tracked objects. Based on the comparison, the sensed object may be associated with one of the tracked objects, and, alternatively, the track information may be updated based on the representation. Conversely, the comparison may indicate that the sensed object is not associated with any of the tracked objects. In this instance, the representation may be used to generate a new track, e.g., for the newly-sensed object.

Systems, devices, system-implemented methods, computer-implemented methods and/or computer program products are provided that can facilitate movement and/or cleaning of a sensor lens of a sensor system for a vehicle body. In one embodiment, the sensor system can comprise a sensor lens having a retractable portion that is moveable at least partially into and out of a chamber. The sensor system also can comprise a cleaning assembly configured to clean the retractable portion disposed within the chamber and at least partially separated from airflow exterior to the chamber. According to another embodiment, a sensor system for a vehicle body can comprise a moveable sensor lens, a cover, and a cleaning assembly configured to clean the sensor lens at least partially concealed by the cover from an environment about the vehicle body.

Disclosed is a method and apparatus for measuring a distance to a target object by using a radar signal in an environment where an obstacle is present. The disclosed method of measuring a distance by using a radar includes: receiving a radar signal reflected from a target object by passing through a target obstacle; estimating material of the target obstacle by using an obstacle material learning result which uses a waveform of a reference radar signal, and by using a waveform of the reflected radar signal; estimating a thickness of the target obstacle by using an obstacle thickness learning result which uses a frequency feature of the reference radar signal, and by using a frequency feature of the reflected radar signal; and calculating a distance to the target object by using a permittivity according to the material of the target obstacle, and the thickness of the target obstacle.