Motive Technologies, Inc.

Ali Hassan

Ahmed Ali

Afsheen Rafaqat Ali

Ijaz Akhter

Syed Wajahat Ali Shah Kazmi

Muhammad Faisal

Hussam Ullah Khan

Aamer Zaheer

Described are embodiments for training and using a close following classifier. In the example embodiments, a system includes a backbone network configured to receive an image; and at least one prediction head communicatively coupled to the backbone network, the at least one prediction head configured to receive an output from the backbone network, wherein the at least one prediction head includes a classifier configured to classify the image as including a close-following event, the classifier receiving the output of the backbone network and a vehicle speed as inputs.

Close following detection using machine learning models

Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a sample set of image frames; detecting a plurality of markers in the sample set of image frames using a convolutional neural network (CNN); fitting lines based on the plurality of markers; detecting a plurality of vanishing points based on the lines; identifying a best fitting horizon for the sample set of image frames via a RANSAC algorithm; computing an inverse perspective mapping (IPM) based on the best fitting horizon; and computing a lane width estimate based on the sample set of image frames using the IPM in a rectified view and the parallel line fitting.

Lane detection and distance estimation using single-view geometry

Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a plurality of images; identifying a horizon in the plurality of images by inputting the plurality of images into a deep learning (DL) model (either stored on a local device or via a network call); determining one or more camera parameters based on the horizon; and storing or using the camera parameters to initialize a camera.

Camera initialization for lane detection and distance estimation using single-view geometry

Disclosed are a multi-task training technique and resulting model for detecting distracted driving. In one embodiment, a method is disclosed comprising inputting a plurality of labeled examples into a multi-task network, the multi-task network comprising: a backbone network, the backbone network generating one or more feature vectors corresponding to each of the labeled examples, and a plurality of prediction heads coupled to the backbone network; minimizing a joint loss based on outputs of the plurality of prediction heads, the minimizing the joint loss causing a change in parameters of the backbone network; and storing a distraction classification model after minimizing the joint loss, the distraction classification model comprising the parameters of the backbone network and parameters of at least one of the prediction heads.

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Lahore, Pakistan

Ali Hassan