# US 11,897,497 B2 · School zone alert > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,897,497 B2 - Document ID: US11897497B2 - Status: Granted - Kind: B2 - Application number: 17/301,379 - Filed: 2021-03-31 - Published: 2022-01-27 - Granted: 2024-02-13 - Earliest claimed: 2020-07-23 - Assignees: Autobrains Technologies Ltd - Applicants: Autobrains Technologies Ltd - Inventors: Boaz Mell; Adam Harel; Igal Raichelgauz - Web page: https://patentgrep.com/patent/US11897497B2 ## CPC - B60W 50/16: Tactile feedback to the driver, e.g. vibration or force feedback to the driver on the steering wheel or the accelerator pedal - 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 - B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention - B60W 60/0015: {specially adapted for safety} - B60W 60/0017: {of other traffic participants} - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G08G 1/09623: {Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle (G08G1/0967 takes precedence)} - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - G08G 1/207: {with respect to certain areas, e.g. forbidden or allowed areas with possible alerting when inside or outside boundaries} - B60W 30/143: {Speed control (B60W30/16 takes precedence)} - B60W 30/18009: {related to particular drive situations} - B60W 2552/05: Type of road, e.g. motorways, local streets, paved or unpaved roads - B60W 2552/45: Pedestrian sidewalk - B60W 2554/4023: large-size vehicles, e.g. trucks - B60W 2555/60: Traffic rules, e.g. speed limits or right of way - 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 - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - 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 A method for generating at least one school zone indicator, the method may include receiving by a vehicle computerized system, school zone indicators, wherein the school zone indicators are indicative of school zone elements; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more school zone indicators of the school zone indicators; wherein the school zone element is selected out of (i) a school zone object and (ii) a school zone situation; autonomously determining, when finding at least one of the one or more school zone identifiers, that the vehicle is driving towards a school zone or is within the school zone; and generating an alert when determining that the vehicle is driving towards the school zone or is within the school zone. ## Claims ### Claim 1 A method for school zone alert, the method comprises: receiving by a vehicle computerized system, school zone indicators, wherein the school zone indicators are indicative of school zone objects; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more school zone indicators of the school zone indicators; wherein the school zone indicators are indicative of school zone objects that (a) appear or occur at a first probability in school zones and (b) appear or occur outside the school zones at a second probability, the second probability is lower by at least thirty percent than the first probability; wherein the school zone indicators are learnt by unsupervised learning; wherein the school zone objects comprise one or more school zone objects that differ from a school zone traffic sign, and differ from a school zone text written on a road; autonomously determining, when finding at least one of the one or more school zone identifiers, that the vehicle is within the school zone; and controlling an aspect of driving the vehicle when determining that the vehicle is within the school zone. ### Claim 2 (depends on 1) The method according to claim 1, comprising generating the school zone indicators by: obtaining school zone occurrence information regarding occurrences of school zone objects within school zone sensed information that was obtained in the school zones; obtaining driving session occurrence information regarding occurrences of driving session objects within driving session school zone sensed information; wherein at least a majority of the driving session sensed information differs from school zone sensed information; wherein the driving session sensed information is obtained outside the school zones; and determining the school zone identifiers, based on the school zone occurrence information and the driving session occurrence information. ### Claim 3 (depends on 1) The method according to claim 1, further comprising predicting an average speed and riding direction of children riding bicycles on their way to a bicycle parking of the school, wherein the bicycle parking and the children are some of the school zone objects. ### Claim 4 (depends on 1) The method according to claim 1, wherein the school zone occurrence information and the driving session occurrence information are normalized per time period. ### Claim 5 The method according to further comprising predicting a behavior of the one or more school zone objects by performing statistical inference. ### Claim 6 (depends on 1) The method according to claim 1, wherein the school zone objects comprise at least one of (i) school crossing guards, (ii) children carrying school bags, and (iii) children wearing school uniform. ### Claim 7 (depends on 1) The method according to claim 1, wherein the school zone objects comprise at least one of (i) a school bus, (ii) a school building, or (iii) a school playground. ### Claim 8 (depends on 1) The method according to claim 1, further comprising generating an ADAS response when determining that the vehicle is driving towards the school zone or is within the school zone. ### Claim 9 (depends on 1) The method according to claim 1, further comprising generating a recommendation aimed to a human driver. ### Claim 10 (depends on 1) The method according to claim 1, further comprising predicted, by performing statistical inference, a duration of blocking traffic by school crossing guards, wherein the school crossing guards are school zone objects wherein the ADAS response comprises controlling an aspect of driving a vehicle. ### Claim 11 (depends on 1) The method according to claim 1, further comprising predicting, by performing statistical inference, a time of an exit of at least ten children from a school building. ### Claim 12 (depends on 1) The method according to claim 1, further comprising obtaining school zone location information and verifying one or more school zone indicators based on the school zone location information. ### Claim 13 (depends on 1) The method according to claim 1, further comprising obtaining school zone location and activity time information and verifying one or more school zone indicators based on the school zone location and activity time information. ### Claim 14 A method for school zone alert, the method comprises: receiving by a vehicle computerized system, school zone indicators, wherein the school zone indicators are indicative of school zone objects; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more school zone indicators of the school zone indicators; wherein the school zone indicators are indicative of school zone objects that (a) appear or occur at a first probability in school zones and (b) appear or occur outside the school zones at a second probability, the second probability is lower by at least thirty percent than the first probability; wherein the school zone indicators are learnt by unsupervised learning; wherein the school zone objects comprise one or more school zone objects that differ from a school zone traffic sign, and differ from a school zone text written on a road; autonomously determining, when finding at least one of the one or more school zone identifiers, that the vehicle is within the school zone; and generating an alert when determining that the vehicle is within the school zone. ### Claim 15 (depends on 14) The method according to claim 14 further comprising controlling an aspect of driving the vehicle when determining that the vehicle is within the school zone. ### Claim 16 (depends on 14) The method according to claim 14, comprising generating the school zone indicators by: obtaining school zone occurrence information regarding occurrences of school zone objects within school zone sensed information that was obtained in the school zones; obtaining driving session occurrence information regarding occurrences of driving session objects within driving session school zone sensed information; wherein at least a majority of the driving session sensed information differs from school zone sensed information; wherein the driving session sensed information is obtained outside the school zones; and determining the school zone identifiers, based on the school zone occurrence information and the driving session occurrence information.