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公开(公告)号：US20190371171A1
公开(公告)日：2019-12-05
申请号：US16000634
申请日：2018-06-05
Applicant: Ford Global Technologies, LLC
Inventor： Gautham Sholingar , Jinesh J. Jain
IPC: G08G1/0967 , B60Q9/00 , G05D1/00 , G05D1/02 , B60W50/14
Abstract: A method for mitigating hazards to access to passenger vehicles. The method includes detecting, with one or more sensors, a hazardous condition in an area proximate a vehicle. A processor may calculate a safety metric corresponding to the hazardous condition and analyze the safety metric relative to a predetermined threshold. A vehicle occupant may be automatically notified of the hazardous condition in the event the safety metric satisfies the predetermined threshold. A corresponding system is also disclosed and claimed herein.
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公开(公告)号：US20190291750A1
公开(公告)日：2019-09-26
申请号：US16437712
申请日：2019-06-11
Applicant: Ford Global Technologies, LLC.
Inventor： Jinesh J. Jain , Daniel Levine
Abstract: Methods, devices and apparatuses pertaining to aberrant driver classification and reporting are described. A method may involve receiving a message from a user of a first vehicle, the message indicating an instance of aberrant driving of a second vehicle. The method may also involve determining that the instance has occurred using one or more classifiers. The method may further involve collecting information of the second vehicle and generating a warning message based on the information.
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公开(公告)号：US20190080184A1
公开(公告)日：2019-03-14
申请号：US16184680
申请日：2018-11-08
Applicant: Ford Global Technologies, LLC
Inventor： Marcos Paul Gerardo Castro , Sneha Kadetotad , Dongran Liu , Jinesh J. Jain
Abstract: A system for detecting and identifying foliage includes a tracking component, a tracking parameters component, and a classification component. The tracking component is configured to detect and track one or more features within range data from one or more sensors. The tracking parameters component is configured to determine tracking parameters for each of the one or more features. The tracking parameters include a tracking age and one or more of a detection consistency and a position variability. The classification component is configured to classify a feature of the one or more features as corresponding to foliage based on the tracking parameters.
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公开(公告)号：US20180281680A1
公开(公告)日：2018-10-04
申请号：US15478118
申请日：2017-04-03
Applicant: FORD GLOBAL TECHNOLOGIES, LLC
Inventor： Marcos Paul Gerardo Castro , Dongran Liu , Sneha Kadetotad , Jinesh J. Jain
CPC classification number: B60R1/00 , B60R2300/802 , B60R2300/8093 , G01S7/4808 , G01S17/936 , G01S2013/9317 , G06F17/11 , G06N7/005
Abstract: Example obstacle detection systems and methods are described. In one implementation, a method receives data from at least one sensor mounted to a vehicle and creates a probabilistic grid-based map associated with an area near the vehicle. The method also determines a confidence associated with each probability in the grid-based map and determines a likelihood that an obstacle exists in the area near the vehicle based on the probabilistic grid-based map.
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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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Patent Agency Ranking
公开(公告)号：US20180126984A1
公开(公告)日：2018-05-10
申请号：US15346210
申请日：2016-11-08
Applicant: Ford Global Technologies, LLC
Inventor： Dongran Liu , Sneha Kadetotad , Marcos Paul Gerardo Castro , Jinesh J. Jain
Abstract: A controller receives outputs form a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor. Sensor outputs corresponding to an object are assigned to a tracklet. Subsequent outputs by any of the sensors corresponding to that object are also assigned to the tracklet. A trajectory of the object is calculated from the sensor outputs assigned to the tracklet, such as by means of Kalman filtering. For each sensor output assigned to the tracklet, a probability is updated, such as using a Bayesian probability update. When the probability meets a threshold condition, the object is determined to be present and an alert is generated or autonomous obstacle avoidance is performed with respect to an expected location of the object.
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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：US11214280B2
公开(公告)日：2022-01-04
申请号：US16479961
申请日：2017-01-26
Applicant: Ford Global Technologies, LLC
Inventor： Scott Vincent Myers , Parsa Mahmoudieh , Jinesh J. Jain , Connie Zeng , Maryam Moosaei , Mohamed Ahmad
Abstract: 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.
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公开(公告)号：US10315649B2
公开(公告)日：2019-06-11
申请号：US15363763
申请日：2016-11-29
Applicant: Ford Global Technologies, LLC
Inventor： Sneha Kadetotad , Jinesh J. Jain , Vidya Nariyambut Murali , Dongran Liu , Marcos Paul Gerardo Castro , Adil Nizam Siddiqui
Abstract: A controller receives outputs from a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor, which may be rearward facing. A probability is updated each time a feature in a sensor output indicates presence of an object. The probability may be updated as a function of a variance of the sensor providing the output and a distance to the feature. Where the variance of a sensor is directional, directional probabilities may be updated according to these variances and the distance to the feature. If the probability meets a threshold condition, actions may be taken such as a perceptible alert or automatic braking. The probability may be decayed in the absence of detection of objects. Increasing or decreasing trends in the probability may be amplified by further increasing or decreasing the probability.
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公开(公告)号：US20180197048A1
公开(公告)日：2018-07-12
申请号：US15404031
申请日：2017-01-11
Applicant: Ford Global Technologies, LLC
Inventor： Ashley Elizabeth Micks , Jinesh J. Jain , Harpreetsingh Banvait , Bruno Sielly Jales Costa
IPC: G06K9/66 , G06N3/08 , G06K9/00 , G06K9/62 , H04N13/02 , G06T19/20 , G06K9/46 , G06T7/40 , G06T11/60 , G06T7/90 , B60R1/00
CPC classification number: G06K9/66 , B60R1/00 , B60R2300/80 , G06K9/00214 , G06K9/00791 , G06K9/00812 , G06K9/4652 , G06K9/4661 , G06K9/6256 , G06K9/628 , G06N3/08 , G06T7/001 , G06T7/40 , G06T7/90 , G06T11/60 , G06T2207/10004 , G06T2207/20081 , G06T2207/30252 , G06T2207/30264 , G06T2210/24 , G06T2219/20 , H04N13/275
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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