# US 10,769,456 B2 · Systems and methods for near-crash determination > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,769,456 B2 - Document ID: US10769456B2 - Status: Granted - Kind: B2 - Application number: 16/010,295 - Filed: 2018-06-15 - Published: 2018-10-11 - Granted: 2020-09-08 - Earliest claimed: 2016-09-14 - Assignees: Nauto, Inc. - Applicants: Nauto, Inc. - Inventors: Suchitra Sathyanarayana; Ravi Kumar Satzoda; Stefan Heck - Web page: https://patentgrep.com/patent/US10769456B2 ## CPC - G05D 1/0055: {with safety arrangements} - B60W 30/095: Predicting travel path or likelihood of collision - B60W 40/09: Driving style or behaviour - B60W 60/0015: {specially adapted for safety} - G06F 18/23: Clustering techniques - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - 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 - G06V 40/20: Movements or behaviour, e.g. gesture recognition (recognition of facial expressions G06V40/16) - G07C 5/0866: {the electronic data carrier being a digital video recorder in combination with video camera} - H04N 23/90: Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums - B60W 2540/229: Attention level, e.g. attentive to driving, reading or sleeping - B60W 2556/10: Historical data - G06N 20/00: Machine learning - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - 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 - 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 - G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A method for near-collision detection, including determining a risk map for a vehicle and automatically detecting a near-collision event with an object based on vehicle behavior relative to the risk map. ## Claims ### Claim 1 A method comprising: capturing an external video with an external-facing camera of an on-board system mounted on a vehicle; determining an obstacle parameter for an obstacle detected from the external video; capturing an internal video with a driver-facing camera of the on-board system; generating a risk score for a spatial position relative to the vehicle, the risk score being determined based on a parametric model, the obstacle parameter, and a driver parameter based on the internal video; and detecting a near-collision event when the risk score exceeds a risk threshold. ### Claim 2 (depends on 1) The method of claim 1 further comprising: identifying an independent parameter of the parametric model with a highest weighted value as a cause of the near-collision event. ### Claim 3 (depends on 1) The method of claim 1 further comprising: transmitting information indicative of the near-collision event to a computing system that is remote from the vehicle. ### Claim 4 (depends on 1) The method of claim 1 further comprising: determining multiple obstacle parameters for multiple obstacles detected from the external video, wherein a risk score is calculated for each obstacle. ### Claim 5 (depends on 1) The method of claim 1, wherein the cause of the near-collision event is a driver cause as determined based on the driver parameter. ### Claim 6 (depends on 1) The method of claim 1 further comprising: transmitting a portion of the external video and a portion of the internal video to a computing system at a location remote from the vehicle. ### Claim 7 (depends on 1) The method of claim 1 further comprising: determining a driver response to the near-collision event based on the internal video. ### Claim 8 (depends on 1) The method of claim 1, wherein the parametric model includes a Gaussian model. ### Claim 9 A method comprising: obtaining an indication of a sampled video captured by an external-facing camera of a vehicle, the indication of the sampled video including an obstacle parameter for an obstacle detected based on the sampled video; obtaining an indication of a near-collision event labeled with a driver parameter, the near-collision event being detected based on a spatial risk map generated based on a parametric model, the obstacle parameter, and the driver parameter; and determining a cause of the near-collision event by identifying an independent parameter of the parametric model with a highest weighted value. ### Claim 10 (depends on 9) The method of claim 9, wherein the cause of the near-collision event is determined at a computing system remote from the vehicle. ### Claim 11 (depends on 9) The method of claim 9, wherein a processing system of the vehicle detects and labels the near-collision event. ### Claim 12 (depends on 9) The method of claim 9 further comprising: obtaining an indication of a secondary obstacle parameter for the obstacle detected based on the sampled video, the spatial risk map being further generated based on the secondary obstacle parameter. ### Claim 13 (depends on 9) The method of claim 9, wherein the driver parameter includes a driver response determined based on a sensor signal obtained during the near-collision event. ### Claim 14 (depends on 13) The method of claim 13, wherein the sensor signal includes an internal image sampled by an internal-facing camera of the vehicle. ### Claim 15 (depends on 9) The method of claim 9 further comprising: aggregating driver responses to near-collision events for multiple drivers; identifying good drivers associated with driver scores that exceed a threshold; identifying the driver responses of the good drivers; and training a vehicle control system based on the driver responses of the good drivers. ### Claim 16 (depends on 15) The method of claim 15 further comprising: determining a respective driver score based on a history of near-collision events associated with each driver. ### Claim 17 (depends on 9) The method of claim 9 further comprising: providing feedback to the driver based on a history of near-collision events associated with the driver. ### Claim 18 (depends on 9) The method of claim 9, wherein a risk score is calculated using the parametric model, the driver parameter, and the obstacle parameter. ### Claim 19 A method comprising: capturing a first image with a first camera facing away from a driver of a vehicle; determining an obstacle parameter based on the first image; determining a driver parameter associated with the driver, the driver parameter being based on a second image captured with a second camera facing the driver of the vehicle; determining a risk score based on a parametric model, the obstacle parameter, and the driver parameter; detecting a near-collision event based on the risk score; and determining a driver response to the near-collision event based on a third image captured with the second camera facing the driver of the vehicle. ### Claim 20 (depends on 19) The method of claim 19 further comprising: determining a driver score based on a history of near-collision events associated with the driver of the vehicle.