# US 12,007,783 B2 · Methods and systems for navigating autonomous and semi-autonomous vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,007,783 B2 - Document ID: US12007783B2 - Status: Granted - Kind: B2 - Application number: 17/280,529 - Filed: 2019-09-26 - Published: 2022-02-10 - Granted: 2024-06-11 - Earliest claimed: 2018-10-01 - Assignees: EmergeX, LLC - Applicants: EmergeX, LLC - Inventors: Roy Feinson; Ariel Katz - Web page: https://patentgrep.com/patent/US12007783B2 ## CPC - G05D 1/0251: {extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision (stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593)} - G05D 1/0088: {characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00)} - G05D 1/0214: {in accordance with safety or protection criteria, e.g. avoiding hazardous areas (monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13)} - G05D 1/0221: {involving a learning process} - G05D 1/0238: {using obstacle or wall sensors (G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93)} - G05D 1/0246: {using a video camera in combination with image processing means} - G05D 1/2435: {Extracting 3D information} - G05D 1/249: from positioning sensors located off-board the vehicle, e.g. from cameras - G05D 1/617: Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards (arrangements for controlling the position or course of two or more vehicles for avoiding collisions therebetween G05D1/693; arrangements for reacting to or preventing system or operator failure G05D1/80) - G05D 1/628: following the obstacle profile, e.g. a wall or undulated terrain - G05D 1/81: Handing over between on-board automatic and on-board manual control - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06N 3/02: Neural networks - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/0495: Quantised networks; Sparse networks; Compressed networks - G06N 3/08: Learning methods - G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V 10/62: relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking - G06V 10/772: Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries - G06V 10/82: using neural networks - G06V 20/46: {Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames} - G06V 20/58: Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/045: Combinations of networks - G06N 3/049: Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs - G06V 2201/02: Recognising information on displays, dials, clocks - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A system and method for forecasting perceived transitions to the four annual seasons in geographic areas is disclosed. The perceived transitions are identified by comparing forecasted daily temperatures in each geographic area to thresholds generated based on normal daily temperatures in those geographic areas. The forecasted daily temperatures may be calculated using both forecasted temperatures and forecasted perceived ambient temperatures (calculated using both temperature and humidity, cloud cover, sun intensity, and/or wind speed). ## Claims ### Claim 1 A method of identifying objects while operating autonomous or semi-autonomous vehicles, the method comprising: receiving, using control circuitry, a first delta image of a first vehicular scenario; generating, using the control circuitry, a first pixel array based on the image of the first vehicular scenario; labeling, using the control circuitry, the first pixel array with a known object in the first vehicular scenario; training, using the control circuitry, an artificial neural network to detect the known object in the first vehicular scenario based on the labeled first pixel array; receiving, using the control circuitry, a second delta image of a second vehicular scenario; generating, using the control circuitry, a second pixel array based on the second delta image of the second vehicular scenario; inputting, using the control circuitry, the second pixel array into the trained neural network; and receiving, using the control circuitry, an output from the trained neural network indicating that the second vehicular scenario includes the known object. ### Claim 2 (depends on 1) The method of claim 1, further comprising: storing, using a camera of an autonomous or semi-autonomous vehicle, an image of the second vehicular scenario; rotating the camera; storing, using the camera of an autonomous or semi-autonomous vehicle, a rotated image of the second vehicular scenario; and generating the second delta image based on the image and the rotated image. ### Claim 3 (depends on 1) The method of claim 1, further comprising: receiving an image of the second vehicular scenario and delta image criterion; selecting a look back image based on the criterion, wherein the look back image is a previously stored image of the first vehicular scenario; retrieving the look back image; and generating the second delta image based on the look back image. ### Claim 4 (depends on 1) The method of claim 1, further comprising: generating a periphery image based on the second delta image by removing chromatic information from the second delta image; determining a pixel density of a portion of the periphery image; comparing the pixel density to a threshold density; and in response to the pixel density equaling or exceeding the threshold, selecting a portion of the second delta image corresponding to the portion for generating the second pixel array. ### Claim 5 (depends on 1) The method of claim 1, further comprising: determining a dangerous condition based on the output; determining an area in the second delta image with no delta information; and directing an autonomous or semi-autonomous vehicle to the area based the second delta image having no delta information in that area. ### Claim 6 (depends on 1) The method of claim 1, further comprising: determining a thickness of a delta edge of the known object in the second delta image; and determining a speed of the known object or a distance to the known object based on the thickness. ### Claim 7 (depends on 1) The method of claim 1, further comprising: identifying the known object based on the output; retrieving a size of the known object; mapping a terrain of the second vehicular scenario based on the size and distance of the known object. ### Claim 8 (depends on 1) The method of claim 1, further comprising: determining a first thickness of a delta edge of the known object in the second delta image; receiving a third delta image of the second vehicular scenario; determining a second thickness of a delta edge of the known object in a third delta image; comparing the first thickness and the second thickness; and determining an acceleration or deceleration of the known object based on the comparison. ### Claim 9 A system for identifying objects while operating autonomous or semi-autonomous vehicles, the system comprising: memory configured to store an artificial neural network; and control circuitry configured to: receive a first delta image of a first vehicular scenario; generate a first pixel array based on the image of the first vehicular scenario; label the first pixel array with a known object in the first vehicular scenario; train artificial neural network to detect the known object in the first vehicular scenario based on the labeled first pixel array; receive a second delta image of a second vehicular scenario; generate a second pixel array based on the second delta image of the second vehicular scenario; input the second pixel array into the trained neural network; and receive an output from the trained neural network indicating that the second vehicular scenario includes the known object. ### Claim 10 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: store, using a camera of an autonomous or semi-autonomous vehicle, an image of the second vehicular scenario; rotate the camera; store, using the camera of an autonomous or semi-autonomous vehicle, a rotated image of the second vehicular scenario; generate the second delta image based on the image and the rotated image. ### Claim 11 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: receive an image of the second vehicular scenario and delta image criterion; select a look back image based on the criterion, wherein the look back image is a previously stored image of the first vehicular scenario retrieve the look back image; and generate the second delta image based on the look back image. ### Claim 12 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: generate a periphery image based on the second delta image by removing chromatic information from the second delta image; determine a pixel density of a portion of the periphery image; compare the pixel density to a threshold density; and select a portion of the second delta image corresponding to the portion for generating the second pixel array in response to the pixel density equaling or exceeding the threshold. ### Claim 13 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: determine a dangerous condition based on the output; determine an area in the second delta image with no delta information; and direct an autonomous or semi-autonomous vehicle to the area based the second delta image having no delta information in that area. ### Claim 14 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: determine a thickness of a delta edge of the known object in the second delta image; and determine a speed of the known object or a distance to the known object based on the thickness. ### Claim 15 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: identify the known object based on the output; retrieve a size of the known object; map a terrain of the second vehicular scenario based on the size and distance of the known object. ### Claim 16 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: determine a first thickness of a delta edge of the known object in the second delta image; receive a third delta image of the second vehicular scenario; determine a second thickness of a delta edge of the known object in a third delta image; compare the first thickness and the second thickness; and determine an acceleration or deceleration of the known object based on the comparison. ### Claim 17 (depends on 1) The method of claim 1, further comprising: initiating a control operation for an autonomous or semi-autonomous vehicle based on the output. ### Claim 18 (depends on 9) The system of claim 9, wherein the control circuitry is further configured to: initiate a control operation for an autonomous or semi-autonomous vehicle based on the output.