A radar module determines a two-dimensional velocity vector of a heavy-duty vehicle with respect to a ground plane supporting the vehicle. The system has a radar transceiver arranged to transmit and to receive a radar signal, via an antenna array, wherein the antenna array is configured to emit the radar signal in a first direction and in a second direction different from the first direction. The radar module has a processing device to detect first and second radar signal components of the received radar signal based on their respective angle of arrival, AoA, where the first radar signal component has an AoA corresponding to the first direction and the second radar signal component has an AoA corresponding to the second direction. The processing device determines the two-dimensional velocity vector of the heavy-duty vehicle based on respective Doppler frequencies of the first and second radar signal components.

Vehicles can include systems and apparatus for performing signal processing on sensor data from radar(s) and LiDAR(s) located on the vehicles. A method includes obtaining and filtering radar point cloud data of an area in an environment in which a vehicle is operating on a road to obtain filtered radar point cloud data; obtaining a light detection and ranging point cloud data of at least some of the area, where the light detection and ranging point cloud data include information about a bounding box that surrounds an object on the road; determining a set of radar point cloud data that are associated with the bounding box that surrounds the object; and causing the vehicle to operate based on one or more characteristics of the object determined from the set of radar point cloud data.

The present teaching relates to different configurations for facilitating calibration of multiple sensors of different types. A plurality of fiducial markers are arranged in space for simultaneously calibrating multiple sensors of different types. Each of the plurality of fiducial markers has a feature point thereon and is provided to enable the multiple sensors to calibrate by detecting the features points and estimating their corresponding 3D coordinates with respect to respective coordinate systems of the multiple sensors.

A sensor system detects objects in an environment ahead of a vehicle. The environment is represented by a predetermined fixed number of position points in an environment model. Initially and when no objects are detected, the position points may be distributed stochastically over the detection area of the sensor system. When objects are detected, the position points are re-distributed based on the detected objects, e.g. with a higher density of position points to represent the detected objects. Because the total number of position points is a predefined fixed number that remains constant, the processing, storage and transmission of the environment model involves a constant data volume and efficient use of memory and transmission bandwidth.

The present disclosure is directed to a plurality of different software models allowing a processor to perform calculations associated with different sets of criteria using data associated with variables that have a strong correlation with one or more of the different criteria sets. Methods consistent with the present disclosure may use several different sets of software that include instructions associated with the collection and evaluation of data associated with a vehicle and with an automated driving system. Different criteria sets may be associated with a range of operating modes, spectral content of received signals, phases of radar signals, and an angular coverage/field of view of the radar apparatus. Results generated by each of the software models may allow a processor to assign weights to the results generated by the different models to generate a combined result that in turn is used to update an operational mode of the radar apparatus.

A radar system has different modes of operation. In a method for operating the radar system, at least one of one or more transmitters are configured to transmit modulated continuous-wave radio signals, while at least one of one or more receivers are configured to receive radio signals. The received radio signals include the transmitted radio signals transmitted by the one or more transmitters and reflected from objects in the environment. The method further includes selectively modifying an operational parameter of at least one of the transmitters or at least one of the receivers. The selected operational parameter is modified to meet changing operational requirements of the radar sensing system.

A radar sensing system including transmit antennas and receive antennas, transmitters, receivers, and a controller. The system further includes a transmit antenna switch selectively coupling each of the transmitters to a respective transmit antenna, and a receive antenna switch selectively coupling at least one receiver of the receivers to respective receive antennas. A quantity of receivers is different from a quantity of the receive antennas. The controller is operable to select a quantity of receivers to be coupled to receive antennas to realize a desired quantity of virtual receivers. The controller is operable to select an antenna pattern as defined by the selected quantity of receivers coupled to receive antennas.

An electronic control unit for radar sensors, in particular for driver assistance systems in motor vehicles, including an integrated component for generating and processing high-frequency signals, and a controller for controlling functions of this component, including monitoring functions for monitoring the operability of the radar sensor, characterized by an error injector integrated into the component for generating defined error conditions for a functional test of the monitoring functions.

Systems, methods and apparatus may be configured to implement automatic semantic classification of a detected object(s) disposed in a region of an environment external to an autonomous vehicle. The automatic semantic classification may include analyzing over a time period, patterns in a predicted behavior of the detected object(s) to infer a semantic classification of the detected object(s). Analysis may include processing of sensor data from the autonomous vehicle to generate heat maps indicative of a location of the detected object(s) in the region during the time period. Probabilistic statistical analysis may be applied to the sensor data to determine a confidence level in the inferred semantic classification. The inferred semantic classification may be applied to the detected object(s) when the confidence level exceeds a predetermined threshold value (e.g., greater than 50%).

Systems and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself. Interior safety systems, exterior safety systems, a drive system or some combination of those systems may be activated to implement active safety measures in the autonomous vehicle.