Abstract: 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.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: 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.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A traffic sensing system for sensing traffic at a roadway includes a first sensor having a first field of view, a second sensor having a second field of view, and a controller. The first and second fields of view at least partially overlap in a common field of view over a portion of the roadway, and the first sensor and the second sensor provide different sensing modalities. The controller is configured to select a sensor data stream for at least a portion of the common field of view from the first and/or second sensor as a function of operating conditions at the roadway.

Abstract: A number of roadway sensing systems are described herein. An example of such is an apparatus to detect and/or track objects at a roadway with a plurality of sensors. The plurality of sensors can include a first sensor that is a radar sensor having a first field of view that is positionable at the roadway and a second sensor that is a machine vision sensor having a second field of view that is positionable at the roadway, where the first and second fields of view at least partially overlap in a common field of view over a portion of the roadway. The example system includes a controller configured to combine sensor data streams for at least a portion of the common field of view from the first and second sensors to detect and/or track the objects.

Abstract: A traffic sensing system for sensing traffic at a roadway includes a first sensor having a first field of view, a second sensor having a second field of view, and a controller. The first and second fields of view at least partially overlap in a common field of view over a portion of the roadway, and the first sensor and the second sensor provide different sensing modalities. The controller is configured to select a sensor data stream for at least a portion of the common field of view from the first and/or second sensor as a function of operating conditions at the roadway.

Abstract: A method of operating a traffic sensor to define ranges of centers of traffic lanes from the traffic sensor is described. The method comprises a) providing a set of lane center variables representing the ranges of the centers of the traffic lanes from the traffic sensor; b) initializing each lane center variable in the set of lane center variables to have an associated starting range value; and then, c) updating the set of lane center variables by, for each vehicle in a plurality of vehicles, i) detecting the vehicle, ii) determining an associated lane center variable having an associated lane center range value closest to the vehicle; iii) estimating a vehicle displacement from the associated lane center range value, and iv) calculating a new lane center range value for the associated lane centre variable using the associated lane center range value and the vehicle displacement.