# US 11,079,593 B2 · Heads up display system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,079,593 B2 - Document ID: US11079593B2 - Status: Granted - Kind: B2 - Application number: 16/199,793 - Filed: 2018-11-26 - Published: 2020-05-28 - Granted: 2021-08-03 - Earliest claimed: 2018-11-26 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Byron S. Green; John S. Werner; Arkadiy O. Tsfasman - Web page: https://patentgrep.com/patent/US11079593B2 ## CPC - G02B 27/0101: {characterised by optical features (G02B27/0172 takes precedence)} - B60K 35/23: Head-up displays [HUD] (optical aspects of head-up displays G02B27/01) - B60K 35/28: characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver - B60K 35/29: Instruments characterised by the way in which information is handled, e.g. showing information on plural displays or prioritising information according to driving conditions - B60Q 9/008: {for anti-collision purposes} - B60W 30/095: Predicting travel path or likelihood of collision - B60W 40/09: Driving style or behaviour - B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention - G08G 1/0962: having an indicator mounted inside the vehicle, e.g. giving voice messages - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - B60K 2360/177: Augmented reality - B60K 2360/178: Warnings - B60K 2360/191: Highlight information - B60K 2360/21: using cameras - B60K 2360/48: Sensors - B60W 2050/143: {Alarm means} - B60W 2050/146: {Display means} - B60W 2540/26: Incapacity - B60W 2554/00: Input parameters relating to objects - B60W 2554/4046: Behavior, e.g. aggressive or erratic - B60W 2554/4049: Relationship among other objects, e.g. converging dynamic objects - B60W 2555/20: Ambient conditions, e.g. wind or rain - G02B 2027/0138: {comprising image capture systems, e.g. camera} - G02B 2027/014: {comprising information/image processing systems} - G02B: OPTICAL ELEMENTS, SYSTEMS OR APPARATUS - B60K: ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES - B60Q: ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) ## Abstract Methods, systems and computer program products for identifying non-standard driving behavior are provided. Aspects include receiving, from one or more sensors associated with a vehicle, vehicle operation data for a vehicle driving environment associated with the vehicle, analyzing the vehicle operation data to identify one or more other vehicles engaged in non-standard operation, and overlaying, on a display, an alert indicia highlighting the one or more other vehicles. ## Claims ### Claim 1 A system for identifying non-standard driving behavior, the system comprising: a memory having computer readable instructions; and a processor for executing the computer readable instructions, the computer readable instructions including: determining, from one or more sensors associated with a vehicle, vehicle operation data associated with the vehicle; determining, by accessing an outside server, a vehicle driving environment associated with the vehicle, wherein the vehicle driving environment is determined based on a location of the vehicle determined by the one or more sensors; analyzing, by a learning model, the vehicle operation data to determine one or more other vehicles engaged in non-standard operation within a pre-defined proximity to the vehicle, wherein the non-standard operation is determined based at least in part on the vehicle driving environment; overlaying, on a display, a first alert indicium highlighting the one or more other vehicles engaged in non-standard operation; receiving feedback, from a user, indicating an acceptance or a rejection of the one or more other vehicles engaged in non-standard operation; updating the learning model based at least in part on the feedback; and adjusting a route on a global positioning system (GPS) for the vehicle based at least in part on the identifying one or more other vehicles engaged in non-standard operation; determining a first vehicle exhibiting habitual non-standard driving behavior over a period of time; storing a characteristic of the first vehicle in a memory, wherein the characteristic comprises a license plate; automatically overlaying, on the display, a second alert indicium highlighting the first vehicle at a new time when encountering the first vehicle in the driving environment. ### Claim 2 (depends on 1) The system of claim 1, wherein the non-standard operation comprises the one or more other vehicles violating at least one traffic guideline. ### Claim 3 (depends on 1) The system of claim 1, wherein the alert indicia further highlights one or more characteristics of the one or more other vehicles causing the non-standard operation. ### Claim 4 (depends on 1) The system of claim 1 further comprising comparing the non-standard operation to a threshold operation level; and based at least in part on the non-standard operation exceeding the threshold operation level, increasing an intensity of the alert indicia. ### Claim 5 (depends on 4) The system of claim 4 further comprising based at least in part on the non-standard operation exceeding the threshold operation level, changing a color of the alert indicia. ### Claim 6 (depends on 1) The system of claim 1, wherein analyzing, by the learning model, the vehicle operation data to identify one or more other vehicles engaged in non-standard operation comprises analyzing a feature vector, generated by the learning model, the feature vector comprising a plurality of features extracted from the vehicle operation data. ### Claim 7 (depends on 1) The system of claim 1, wherein the non-standard operation includes a deviation in speed exceeding a speed threshold of the one or more other vehicles compared to an average speed in the vehicle driving environment. ### Claim 8 (depends on 1) The system of claim 1, wherein the vehicle driving environment comprises a weather condition present; and wherein the non-standard operation includes an operation deemed non-standard based at least in part on the weather condition present. ### Claim 9 (depends on 1) The system of claim 1, wherein the vehicle driving environment comprises a plurality of local driving patterns; and wherein the non-standard operation includes an operation deemed non-standard based at least in part on the plurality of local driving patterns. ### Claim 10 (depends on 1) The system of claim 1, wherein the display is a heads up display in the vehicle. ### Claim 11 A method for identifying non-standard driving behavior, the method comprising: determining, from one or more sensors associated with a vehicle, vehicle operation data associated with the vehicle; determining, by accessing an outside server, a vehicle driving environment associated with the vehicle, wherein the vehicle driving environment is determined based on a location of the vehicle determined by the one or more sensors; analyzing, by a learning model, the vehicle operation data to determine one or more other vehicles engaged in non-standard operation within a pre-defined proximity to the vehicle, wherein the non-standard operation is determined based at least in part on the vehicle driving environment; overlaying, on a display, a first alert indicium highlighting the one or more other vehicles engaged in non-standard operation; receiving feedback, from a user, indicating an acceptance or a rejection of the one or more other vehicles engaged in non-standard operation; updating the learning model based at least in part on the feedback; and adjusting a route on a global positioning system (GPS) for the vehicle based at least in part on the identifying one or more other vehicles engaged in non-standard operation; determining a first vehicle exhibiting habitual non-standard driving behavior over a period of time; storing a characteristic of the first vehicle in a memory, wherein the characteristic comprises a license plate; automatically overlaying, on the display, a second alert indicium highlighting the first vehicle at a new time when encountering the first vehicle in the driving environment. ### Claim 12 (depends on 11) The method of claim 11, wherein the non-standard operation comprises the one or more other vehicles violating at least one traffic guideline. ### Claim 13 (depends on 11) The method of claim 11, wherein the alert indicia further highlights one or more characteristics of the one or more other vehicles causing the non-standard operation. ### Claim 14 (depends on 11) The method of claim 11 further comprising comparing the non-standard operation to a threshold operation level; and based at least in part on the non-standard operation exceeding the threshold operation level, increasing an intensity of the alert indicia. ### Claim 15 (depends on 14) The method of claim 14 further comprising based at least in part on the non-standard operation exceeding the threshold operation level, changing a color of the alert indicia. ### Claim 16 (depends on 11) The method of claim 11, wherein analyzing, by the learning model, the vehicle operation data to identify one or more other vehicles engaged in non-standard operation comprises analyzing a feature vector, generated by the learning model, the feature vector comprising a plurality of features extracted from the vehicle operation data. ### Claim 17 A computer program product for identifying non-standard driving behavior comprising a non-transitory computer readable storage medium having program instructions embodied therewith the program instructions executable by a computer processor to cause the computer processor to perform a method, comprising: determining, from one or more sensors associated with a vehicle, vehicle operation data associated with the vehicle; determining, by accessing an outside server, a vehicle driving environment associated with the vehicle, wherein the vehicle driving environment is determined based on a location of the vehicle determined by the one or more sensors; analyzing, by a learning model, the vehicle operation data to determine one or more other vehicles engaged in non-standard operation within a pre-defined proximity to the vehicle, wherein the non-standard operation is determined based at least in part on the vehicle driving environment; and overlaying, on a display, a first alert indicium highlighting the one or more other vehicles engaged in non-standard operation; receiving feedback, from a user, indicating an acceptance or a rejection of the one or more other vehicles engaged in non-standard operation; updating the learning model based at least in part on the feedback; and adjusting a route on a global positioning system (GPS) for the vehicle based at least in part on the identifying one or more other vehicles engaged in non-standard operation; determining a first vehicle exhibiting habitual non-standard driving behavior over a period of time; storing a characteristic of the first vehicle in a memory, wherein the characteristic comprises a license plate; automatically overlaying, on the display, a second alert indicium highlighting the first vehicle at a new time when encountering the first vehicle in the driving environment. ### Claim 18 (depends on 17) The computer program product of claim 17, wherein the non-standard operation comprises the one or more other vehicles violating at least one traffic guidelines. ### Claim 19 (depends on 17) The computer program product of claim 17, wherein the alert indicia further highlights one or more characteristics of the one or more other vehicles causing the non-standard operation.