Ford Global Technolgoies, LLC

Maryam Moosaei

Vidya Nariyambut Murali

Parsa Mahmoudieh

Guy Hotson

Madeline Jane Goh

Yi Zhang

Jinesh J Jain

Scott Vincent Myers

Harpreetsingh Banvait

Mohamed Ahmad

Connie Zeng

Systems, methods, and devices for detecting brake lights are disclosed herein. A system includes a mode component, a vehicle region component, and a classification component. The mode component is configured to select a night mode or day mode based on a pixel brightness in an image frame. The vehicle region component is configured to detect a region corresponding to a vehicle based on data from a range sensor when in a night mode or based on camera image data when in the day mode. The classification component is configured to classify a brake light of the vehicle as on or off based on image data in the region corresponding to the vehicle.

Brake light detection

A controller receives sensor data during a ride and provides it to a server system. A passenger further provides feedback concerning the ride in the form of some or all of an overall rating, flagging of ride anomalies, and flagging of road anomalies. The sensor data and feedback are input to a training algorithm, such as a deep reinforcement learning algorithm, which updates an artificial intelligence (AI) model. The updated model is then propagated to controllers of one or more autonomous vehicle which then perform autonomous navigation and collision avoidance using the updated AI model.

Feedback for an autonomous vehicle

A method is disclosed for using an autonomous vehicle to teach a student how to drive. The method may include generating, by the autonomous vehicle during a training session, a plan for navigating a section of road. During the training session, the autonomous vehicle may receive control instructions from the student regarding how the student would like to navigate the section of road. If the autonomous vehicle determines that implementing the instructions would be safe and legal, it may implement those instructions. However, if the autonomous vehicle determines that implementing the instructions would not be safe and legal, it may execute the plan. During a training session, an autonomous vehicle may provide on a head-up display to the student certain visual feedback regarding a driving performance of the student.

Autonomous vehicle providing driver education

A method is disclosed for detecting and classifying one or more traffic lights. The method may include converting an RGB frame to an HSV frame. The HSV frame may be filtered by at least one threshold value to obtain at least one saturation frame. At least one contour may be extracted from the at least one saturation frame. Accordingly, a first portion of the RGB may be cropped in order to encompass an area including the at least one contour. The first portion may then be classified by an artificial neural network to determined whether the first portion corresponds to a not-a-traffic-light class, a red-traffic-light class, a green-traffic-light class, a yellow-traffic-light class, or the like.

Traffic-light detection and classification using computer vision and deep learning

A system and method for assisted or autonomous parking of a vehicle is disclosed. The method may begin when the vehicle approaches a feeder lane within a parking lot. At that point, a computer system may decide whether the vehicle should enter the feeder lane. The computer system may use at least one of machine learning, computer vision, and range measurements to determining whether a condition precedent for entering the feeder lane exists. The condition precedent may include an in-bound arrow on the feeder lane or parking lines and/or a parked vehicle adjacent the feeder lane defining a departure angle less than or equal to ninety degrees. If the condition precedent exists, the vehicle may enter the feeder lane. If the condition precedent does not exist, the vehicle may move on to another feeder lane.

Parking-lot-navigation system and method

A vehicle includes a pair of cameras disposed on the vehicle and oriented to view an entrance to the vehicle. The vehicle also includes a processor configured to compare a profile image of a passenger's facial feature from one of the pair with a frontal image of the passenger's facial feature from the other of the pair. The processor is configured to lock the entrance to the vehicle in response to the facial features being different.

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Mishawaka, IN, United States of America

Maryam Moosaei