The described aspects and implementations enable fast and accurate verification of radar detection of objects in autonomous vehicle (AV) applications using combined processing of radar data and camera images. In one implementation, disclosed is a method and a system to perform the method that includes obtaining a radar data characterizing intensity of radar reflections from an environment of the AV, identifying, based on the radar data, a candidate object, obtaining a camera image depicting a region where the candidate object is located, and processing the radar data and the camera image using one or more machine-learning models to obtain a classification measure representing a likelihood that the candidate object is a real object.

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.

An object detection method using a lidar sensor of an embodiment includes determining whether a box of a target object is a box in which an overlapping object present therein can be deleted on the basis of shape information of the target object obtained by the lidar sensor, and generating a box track of the target object after removing the overlapping object according to a determination result.

An object detection method using a lidar sensor of an embodiment includes determining whether a box of a target object is a box in which an overlapping object present therein can be deleted on the basis of shape information of the target object obtained by the lidar sensor, and generating a box track of the target object after removing the overlapping object according to a determination result.

Deep learning to improve or gauge the performance of a surface-penetrating radar (SPR) system for localization or navigation. A vehicle may employ a terrain monitoring system including SPR for obtaining SPR signals as the vehicle travels along a route. An on-board computer including a processor and electronically stored instructions, executable by the processor, may analyze the acquired SPR images and computationally identify subsurface structures therein by using the acquired image as input to a predictor that has been computationally trained to identify subsurface structures in SPR images.

A radar detection method may include: transmitting a first radar signal in a field of view and receiving a second radar signal originated from reflections of the first radar signal in the field of view; generating a detection profile by processing the first and second radar signals, the detection profile representing intensities of the second radar signal as a function of positions in the field of view; and analyzing the detection profile to identify targets in the field of view. Analyzing the detection profile may include: using a first mode of analysis, with lower sensitivity, for first cycles, wherein the first mode of analysis is configured to detect a target entering the field of view; using a second mode of analysis, with higher sensitivity, for second cycles following the first cycles, wherein the second mode of analysis is configured to detect stay of the target in the field of view.

A radar system is provided that includes a radar transceiver integrated circuit (IC) configurable to transmit a first frame of chirps, and another radar transceiver IC configurable to transmit a second frame of chirps at a time delay ΔT, wherein ΔT=T.sub.c/K, K≥2 and T.sub.c is an elapsed time from a start of one chirp in the first frame and the second frame and a start of a next chirp in the first frame and the second frame, wherein the radar system is configured to determine a velocity of an object in a field of view of the radar system based on first digital intermediate frequency signals generated responsive to receiving reflected chirps of the first frame and second digital IF signals generated responsive to receiving reflected chirps of the time delayed second frame, wherein the maximum measurable velocity is increased by a factor of K.

The present application provides a method, apparatus and electronic equipment for recognizing a posture of a target, a first receiving signal and a second receiving signal upon scattering of a transmitting signal from a target to be recognized are acquired, a first baseband signal is determined according to the first receiving signal and the transmitting signal, and a second baseband signal is determined according to the second receiving signal and the transmitting signal; and a category of the posture of the target to be recognized is finally determined according to the first baseband signal and the second baseband signal. The first baseband signal and the second baseband signal carry various feature values related to the posture of the target, including but not limited to transversal velocity information and radial velocity information, etc.

A device for emitting and receiving electromagnetic radiation, in which different antennas are used for the emitting and receiving, a first antenna or first group being used for the transmission in a first polarization form, a second antenna or second group being used for the transmission in a second polarization form, and a third antenna or third group being used for receiving the reflected electromagnetic radiation that was emitted by the first antenna or first group and by the second antenna or second group. The device may be fixed in place on a motor vehicle and used for object detection within the framework of a distance and speed control or a collision avoidance, and the polarimetric information obtained from the different receiving levels during the propagation of the two differently polarized electromagnetic waves via different propagation paths is able to be used for ascertaining a weather-related road condition.

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.