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公开(公告)号：US10296816B2
公开(公告)日：2019-05-21
申请号：US15404031
申请日：2017-01-11
Applicant: Ford Global Technologies, LLC
IPC: G06K9/66 , G06N3/08 , G06K9/00 , G06K9/62 , G06T11/60 , G06K9/46 , B60R1/00 , H04N13/275 , G06T7/00
Abstract: A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated. A machine learning algorithm for generating classifications from image data may be trained using arrival and departure images obtained by rendering of a three-dimensional model or by adding simulated fluid leaks to two-dimensional images.
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公开(公告)号：US20180341265A1
公开(公告)日：2018-11-29
申请号：US15605821
申请日：2017-05-25
Applicant: Ford Global Technologies, LLC
Inventor： Scott Vincent Myers , Harpreetsingh Banvait , Joshua Scott Smith
CPC classification number: G05D1/0214 , G01C13/008 , G01C21/3407 , G05D2201/0213
Abstract: Techniques and examples pertaining to vehicle water wading safety are described. A processor implementable to a vehicle approaching a waterbody may receive data related to the waterbody from one or more above-water or under-water sensors. The processor may determine a top surface and a bottom profile of the waterbody, and calculate one or more critical trajectories of water-sensitive components of the vehicle if the vehicle is to wade through the waterbody by traversing the bottom profile. The processor may then determine the wading safety based on the critical trajectories and the top surface of the waterbody. The processor may further determine a wading route, and autonomously drive the vehicle to wade the waterbody via the optimal wading route.
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公开(公告)号：US10113351B2
公开(公告)日：2018-10-30
申请号：US14923038
申请日：2015-10-26
Applicant: Ford Global Technologies, LLC
Inventor： Harpreetsingh Banvait , Jinesh J Jain
Abstract: Methods and apparatus pertaining to an intelligent vehicle access point opening system are provided. A method may involve detecting a presence of an object in a vicinity of a cover of an access point of a vehicle. The method may also involve receiving a command to open the cover and activating a mechanism to open the cover responsive to receiving the command. The method may further involve determining whether the object is likely to fall as the cover is being opened. The method may additionally involve pausing opening of the cover responsive to a determination that the object is likely to fall as the cover is being opened.
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公开(公告)号：US10095229B2
公开(公告)日：2018-10-09
申请号：US15264329
申请日：2016-09-13
Applicant: Ford Global Technologies, LLC
Inventor： Scott Vincent Myers , Praveen Narayanan , Harpreetsingh Banvait , Mark Crawford , Alexandru Mihai Gurghian , David Silver
Abstract: Example passenger validation systems and methods are described. In one implementation, a method receives, at a vehicle, a transport request indicating a passenger and a pick-up location. The vehicle drives to the pick-up location and authenticates the passenger at the pick-up location. If the passenger is successfully authenticated, the method unlocks the vehicle doors to allow access to the vehicle, determines a number of people entering the vehicle, and confirms that the number of people entering the vehicle matches a number of passengers associated with the transport request.
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公开(公告)号：US10049284B2
公开(公告)日：2018-08-14
申请号：US15095876
申请日：2016-04-11
Applicant: Ford Global Technologies, LLC
Abstract: A method is disclosed for using a camera on-board a vehicle to determine whether precipitation is failing near the vehicle. The method may include obtaining multiple images. Each of the multiple images may be known to photographically depict a “rain” or a “no rain” condition. An artificial neural network may be trained on the multiple images. Later, the artificial neural network may analyze one or more images captured by a first camera secured to a first vehicle. Based on that analysis, the artificial neural network may classify the first vehicle as being in “rain” or “no rain” weather.
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Patent Agency Ranking
公开(公告)号：US20180203457A1
公开(公告)日：2018-07-19
申请号：US15406121
申请日：2017-01-13
Applicant: Ford Global Technologies, LLC
Inventor： Maryam Moosaei , Jinesh J. Jain , Harpreetsingh Banvait
Abstract: A method for avoiding interference with a bus. The method includes detecting a bus and obtaining image data from the bus, such as information displayed on the bus. A deep neural network trained on bus images may process the information to associate the bus with a bus route and stop locations. Map data corresponding to the stop locations may also be obtained and used to initiate a lane change or safety response in response to proximity of the bus to a stop location. A corresponding system and computer program product is also disclosed and claimed herein.
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公开(公告)号：US09873428B2
公开(公告)日：2018-01-23
申请号：US14924187
申请日：2015-10-27
Applicant: Ford Global Technologies, LLC
Inventor： Harpreetsingh Banvait , Kyu Jeong Han , Jinesh J Jain
IPC: B60W30/09 , H04L29/08 , B60W30/095
CPC classification number: B60W30/09 , B60W30/0956 , B60W2420/42 , B60W2420/54 , B60W2550/10 , H04L67/12 , H04W4/046
Abstract: A controller for an autonomous vehicle receives audio signals from one or more microphones. The outputs of the microphones are pre-processed to enhance audio features that originated from vehicles. The outputs may also be processed to remove noise. The audio features are input to a machine learning model that classifies the source of the audio features. For example, features may be classified as originating from a vehicle. A direction to a source of the audio features is determined based on relative delays of the audio features in signals from multiple microphones. Where audio features are classified with an above-threshold confidence as originating from a vehicle, collision avoidance is performed with respect to the direction to the source of the audio features.
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公开(公告)号：US20170364776A1
公开(公告)日：2017-12-21
申请号：US15183610
申请日：2016-06-15
Applicant: Ford Global Technologies, LLC
Inventor： Ashley Elizabeth Micks , Jinesh J. Jain , Kyu Jeong Han , Harpreetsingh Banvait
CPC classification number: G06K9/66 , G01S13/006 , G01S13/865 , G01S13/931 , G01S17/006 , G01S17/936 , G05D1/0088 , G06F17/5009 , G06K9/00825 , G06N99/005 , G09B9/54
Abstract: A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a parked vehicle with its engine running. The location of the parked vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of the parked vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a parked vehicle with its engine running based on actual sensor outputs input to the machine learning model.
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公开(公告)号：US20170294121A1
公开(公告)日：2017-10-12
申请号：US15096803
申请日：2016-04-12
Applicant: Ford Global Technologies, LLC
Inventor： Jinesh J Jain , Sneha Kadetotad , Harpreetsingh Banvait , Vidya Nariyambut Murali , Peter Gyumyeong Joh
IPC: G08G1/14
CPC classification number: G08G1/14 , G01S7/295 , G01S7/417 , G01S13/90 , G01S13/91 , G01S13/931 , G01S2013/9314 , G01S2013/9375 , G06K9/00812 , G08G1/143 , G08G1/146 , G08G1/147
Abstract: The present invention extends to methods, systems, and computer program products for detecting available parking spaces in a parking environment. Radar systems are utilized to gather data about a parking lot environment. The radar data is provided to a neural network model as an input. Algorithms employing neural networks can be trained to recognize parked vehicles and conflicting data regarding debris, shopping carts, street lamps, traffic signs, pedestrians, etc. The neural network model processes the radar data to estimate parking space boundaries and to approximate the parking space boundaries as splines. The neural network model outputs spline estimations to a vehicle computer system. The vehicle computer system utilizes the spline estimates to detect available parking spaces. The spline estimates are updated as the vehicle navigates the parking environment.
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公开(公告)号：US09740944B2
公开(公告)日：2017-08-22
申请号：US14975177
申请日：2015-12-18
Applicant: Ford Global Technologies, LLC
Inventor： Ashley Elizabeth Micks , Venkatapathi Raju Nallapa , Harpreetsingh Banvait , Scott Vincent Myers
CPC classification number: G06K9/00805 , B60W2400/00 , B60W2420/00 , G06K9/00812 , G06K9/209 , G06K9/6256 , G06K9/6262 , G06T7/251 , G06T11/00 , G06T17/05 , G06T2207/20081 , G06T2207/30261 , G06T2207/30264 , G08G1/165 , G08G1/168
Abstract: The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles, such as wheel stops or parking barriers. A method for generating virtual sensor data includes simulating a three-dimensional (3D) environment comprising one or more objects. The method includes generating virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth includes information about at least one object within the virtual sensor data. The method also includes storing and associating the virtual sensor data and the virtual ground truth.
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