A method is provided for determining a road condition by using a radar system having transmitter and receiving units for transmitting and receiving radar waves having two different polarizations and providing transmit and receive signals indicating an intensity of the transmitted and received radar waves. Co-polarized backscattering coefficients and at least one cross-polarized backscattering coefficient are determined based on the transmit and receive signals. If the cross-polarized backscattering coefficient is greater than or equal to a threshold, the road condition is determined based on a ratio of the co-polarized backscattering coefficients and based on a difference of one of the co-polarized backscattering coefficients and the cross-polarized backscattering coefficient. If the cross-polarized backscattering coefficient is smaller than the threshold, the road condition is determined based on the ratio and a difference of the co-polarized backscattering coefficients.

Example embodiments relate to techniques for detecting adverse road conditions using radar. A computing device may generate a first radar representation that represents a field of view for a radar unit coupled to a vehicle and during clear weather conditions and store the first radar representation in memory. The computing device may receive radar data from the radar unit during navigation of the vehicle on a road and determine a second radar representation based on the radar data. The computing device may also perform a comparison between the first radar representation and the second radar representation and determine a road condition for the road based on the comparison. The road condition may represent a quantity of precipitation located on the road and provide control instructions to the vehicle based on the road condition for the road.

A radar-based system for determining the local position of a vehicle uses markers with at least one radar-reflective element, as well as a radar system and vehicle controller. The radar system transmits radio waves, which are reflected by nearby objects, including the radar markers. The radar system receives the reflected radio waves and detects the unique radar signatures from the radar markers, as well as range, azimuth, and/or elevation dimensions of the vehicle with respect to the radar markers. The unique radar signatures and dimensions are communicated to the vehicle controller, which then determines the local position of the vehicle from the unique radar signatures and dimensions.

Commercial personal mobile vehicles (PMVs) managed by a fleet management system are sometimes equipped with a radar sensor to detect objects in an environment external to the PMVs. Specifically, the PMV may be equipped with a variety of sensors, such as a radar, a sonar sensor, a (optional) camera, an inertia measurement unit (IMU), and/or the like. The combination of a radar reflection signal and a sonar signal may provide measurements of characteristics such as a Doppler velocity and height information of a nearby object, which may be input to a machine learning classifier to determine the probability that the nearby object is a VRU. For another example, the reflection pattern from radar and ultrasonic may be used to input to a machine learning classifier to determine a type of the road surface, e.g., an asphalt road surface, a concrete sidewalk surface, a wet-grass lawn surface, and/or the like.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

Systems, methods, and other embodiments described herein relate to automatically determining the size of a trailer being towed by a vehicle. In one embodiment, a method includes, responsive to determining that a trailer is aligned with a towing vehicle, analyzing radar returns from a radar of the towing vehicle to identify radar features within an area behind the towing vehicle. The method includes determining a trailer size of the trailer from the radar features. The method includes adjusting operation of the towing vehicle according to the trailer size.

A system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals. Decision fusion is used to integrate classification results from feature-based classifier and deep learning AI to reach optimal decision.

A vehicular sensing system includes at least one radar sensor disposed at a vehicle and having a field of sensing forward, rearward or sideward of the vehicle. Radar data captured by the radar sensor is received at an electronic control unit (ECU). Received transmitted signals reflected off objects and received at the receiving antennas are evaluated at the ECU to establish surface responses for the objects present in the field of sensing of the radar sensor. A data set of radar data that is representative of an object present in the field of sensing of the radar sensor is compared to stored data sets to determine if the data set corresponds to a particular stored data set of the stored data sets. Responsive to the data set of radar data being determined to correspond to the particular stored data set, the vehicular sensing system classifies the detected object.

The present disclosure generally relates to a multistatic radar system and a method for a spatially resolved detection of an object under test. The multistatic radar system includes an at least two-dimensional multistatic array of antenna elements having an at least partially shared coverage area. At least one data processing circuit is coupled to the array. Analog and/or digital beamforming is performed thereby obtaining at least one image of the object under test at least partially being located within the shared coverage area. Processing the image obtained is used to resolve at least one scattering center of the object under test. A spatially resolved scattering center distribution is determined based on the image obtained.

Described herein are examples of evaluating electromagnetic energy reflection data of security scans. In embodiments, a method to evaluate electromagnetic energy reflection data determines whether electronic information of a security scan contains an anomaly, and identifies an anomaly location in the electronic information corresponding to the anomaly. The method determines a subset of the electronic information corresponding to the anomaly location, determines anomaly attributes using the subset of the electronic information, and evaluates the anomaly attributes using a database of reference items by comparing anomaly attributes to respective reference characteristics of reference items or identity information. When a comparison meets the respective match criterion for the given reference item, the method assigns to the anomaly the respective identifier as an anomaly identifier.