A traffic radar system comprises a first radar transceiver, a second radar transceiver, a speed determining element, and a processing element. The first radar transceiver transmits and receives radar beams and generates a first electronic signal corresponding to the received radar beam. The second radar transceiver transmits and receives radar beams and generates a second electronic signal corresponding to the received radar beam. The speed determining element determines and outputs a speed of the patrol vehicle. The processing element is configured to receive a plurality of digital data samples derived from the first or second electronic signals, receive the speed of the patrol vehicle, process the digital data samples to determine a relative speed of at least one target vehicle in the front zone or the rear zone, and convert the relative speed of the target vehicle to an absolute speed using the speed of the patrol vehicle.

A method for detecting a traffic law violation due to the allowable distance between a following vehicle and a guide vehicle being undershot, the following vehicle traveling behind the guide vehicle. At least the respective speed of the following vehicle is identified and the guide vehicle in a detection region in the surroundings of a sensor which supplies speed measurement values. A reference distance is detected and/or ascertained between the following vehicle and the guide vehicle at a reference measurement point. At least one following distance is determined between the following vehicle and the guide vehicle in the detection region using the identified speeds of the following vehicle and the guide vehicle and/or the detected reference distance. A traffic law violation is detected if the following distance in the detection region continuously falls short of a distance threshold.

Methods and systems for estimating vehicle velocity based on radar data. The methods and systems include receiving a set of range-Doppler-beam, RDB, maps from radars located on a vehicle and performing an optimization process that adjusts an estimate of vehicle velocity so as to optimize a correlation score. The optimization process includes iteratively: spatially registering the set of RDB maps based on the current estimate of vehicle velocity, determining the correlation score based on the spatially registered set of RDB maps, and outputting an optimized estimate of vehicle velocity from the optimization process when the correlation score has been optimized. The methods and systems control the vehicle based at least in part on the optimized estimate of vehicle velocity.

Methods and systems for estimating vehicle velocity based on radar data. The methods and systems include receiving a set of range-Doppler-beam, RDB, maps from radars located on a vehicle and performing an optimization process that adjusts an estimate of vehicle velocity so as to optimize a correlation score. The optimization process includes iteratively: spatially registering the set of RDB maps based on the current estimate of vehicle velocity, determining the correlation score based on the spatially registered set of RDB maps, and outputting an optimized estimate of vehicle velocity from the optimization process when the correlation score has been optimized. The methods and systems control the vehicle based at least in part on the optimized estimate of vehicle velocity.

Systems and methods for training and using machine learning models to classify vehicles from highway radar systems are provided. The training systems may use auxiliary radar processing to separate events by lane, length, and/or speed, and then use separate event data groups pooled from similar or proximate lanes, lengths, and/or speeds to train multiple models. At estimation time, incoming events may be grouped using similar groupings as those used during training to select which model to use. An incoming event may be applied to the neural network operations of the selected model to generate an estimate. Generating an estimate may involve successive applications of multiple linear convolutions and other steps along varying or alternating dimensions of the in-process data.

An in-vehicle sensor device has an active sensor, an odometry sensor and a processing part. The processing part calculates estimated detection values of a stationary object by using position information parameters, a mounting angle of the active sensor, and a detection error of the odometry sensor. The position information parameters specify a relative position relationship between the stationary object and the active sensor. The processing part updates the position information parameters, the mounting angle, and the detection error simultaneously on the basis of a difference between the estimated detection value which has been calculated, and the position values of the stationary object detected by the active sensor.

A method for measuring, using a radar or sonar, the velocity with respect to the ground of a carrier moving parallel to the ground, includes the following steps: a) orienting the line of sight of the radar or sonar toward the ground; b) emitting a plurality of radar or sonar signals (P.sub.1-P.sub.N) that are directed toward the ground, and acquiring respective echo signals (E.sub.1-E.sub.N); c) processing the acquired echo signals so as to obtain, for one or more echo delay values, a corresponding Doppler spectrum; d) for the or at least one the echo delay value, determining a high cut-off frequency of the corresponding Doppler spectrum; and e) computing the velocity of the carrier with respect to the ground on the basis of the one or more high cut-off frequencies. A system allowing such a method to be implemented.

A system for determining a stationary state of a rail vehicle on a track includes a first radar mounted at an end of the rail vehicle and a second radar mounted at another end of the rail vehicle. A speed sensor is mounted on the rail vehicle. A series of fixed reflective track features are found along the track. A processing unit, communicably connected with the speed sensor, the first radar and the second radar receives data from the first radar and the second radar corresponding to the distance to the fixed reflective track features and determines the stationary state or low-speed condition of the rail vehicle and checks the state or condition by comparing it with an output of the speed sensor.

Architectures and techniques for near field beamforming are disclosed. RADAR waveform data is received from a radio frequency front end. Range and movement information for one or more objects is generated from the received RADAR waveform data. A spatial frequency representation of the received RADAR waveform data is calculated. The spatial frequency representation of the received RADAR waveform data is migrated to a spatial space representation using a mapping function and interpolation. Signal processing operations are performed on the spatial space representation of the received RADAR waveform data. The spatial space representation of the received RADAR waveform data is converted to a Cartesian space representation. Information corresponding to the one or more objects in the Cartesian space representation is generated.

Architectures and techniques for near field beamforming are disclosed. RADAR waveform data is received from a radio frequency front end. Range and movement information for one or more objects is generated from the received RADAR waveform data. A spatial frequency representation of the received RADAR waveform data is calculated. The spatial frequency representation of the received RADAR waveform data is migrated to a spatial space representation using a mapping function and interpolation. Signal processing operations are performed on the spatial space representation of the received RADAR waveform data. The spatial space representation of the received RADAR waveform data is converted to a Cartesian space representation. Information corresponding to the one or more objects in the Cartesian space representation is generated.