# US 9,751,506 B2 · Algorithms for avoiding automotive crashes at left and right turn intersections > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,751,506 B2 - Document ID: US9751506B2 - Status: Granted - Kind: B2 - Application number: 14/924,585 - Filed: 2015-10-27 - Published: 2017-04-27 - Granted: 2017-09-05 - Earliest claimed: 2015-10-27 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Upali Priyantha Mudalige; Thanura Ranmal Elvitigala - Web page: https://patentgrep.com/patent/US9751506B2 ## CPC - B60T 7/22: initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle {, or by means of contactless obstacle detectors mounted on the vehicle} - G01S 13/931: of land vehicles - G01S 17/931: of land vehicles - G08G 1/163: {involving continuous checking} - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - B60T 2201/022: Collision avoidance systems - G01S 2013/9316: {combined with communication equipment with other vehicles or with base stations} - G01S 2013/93185: {Controlling the brakes} - G01S 2013/9322: {using additional data, e.g. driver condition, road state or weather data} - G08G 1/07: Controlling traffic signals - G08G 1/09623: {Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle (G08G1/0967 takes precedence)} - G08G 1/096783: {where the origin of the information is a roadside individual element} - B60T: VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL (electrodynamic brake systems for vehicle, in general B60L; brakes per se, i.e. devices where braking effect occurs, including ultimate brake actuators, F16D); ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - 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 system and method for warning a driver of a host vehicle of a potential collision with other vehicles when turning at an intersection. The method includes determining if the host vehicle is likely to turn at the intersection, and if so, segmenting the intersection into a plurality of different regions, where each region has a different level of collision threat risk. The method obtains the speed, velocity and position of the host vehicle and any relevant remote vehicles in the intersection. The method determines a predicted path of the host vehicle and the predicted path of the remote vehicles. The method then determines whether the host vehicle and the remote vehicles will simultaneously occupy a collision zone in the intersection based on the predicted paths, and if so, issues a warning to the driver of the collision risk or apply vehicle controls to avoid or mitigate the collision risk. ## Claims ### Claim 1 A method for collision threat assessment when a host vehicle is turning left or right at an intersection, said method comprising: determining if the host vehicle is likely to turn left or right at the intersection with a predetermined level of confidence; segmenting the intersection and surrounding area into a plurality of different regions where each region has a different level of collision threat risk if the host vehicle is likely to turn left or right at the intersection; obtaining speed, velocity and position data of the host vehicle using sensors on the host vehicle; obtaining speed, velocity and position data of one or more remote vehicles and/or other objects in or near the intersection, including using information and data from one or more of a map database, a long range radar sensor on the vehicle, a short range radar sensor on the vehicle, cameras on the vehicle, a lidar sensor on the vehicle, V2X communications, Internet communications and satellite communications; determining, by a controller including a processor, a predicted path of the host vehicle based on the speed, velocity and position data as the host vehicle travels through the intersection for each of the different segmented regions; determining, by the controller, a predicted path of one or more of the remote vehicles based on the speed, velocity and position data as the one or more vehicles travel through the intersection; determining, by the controller, whether the host vehicle and the one or more remote vehicles will simultaneously occupy a collision zone in the intersection based on the predicted path of the host vehicle and the predicted path of the one or more remote vehicles; issuing, by the controller, a warning to a driver of the host vehicle if the host vehicle and the one or more remote vehicles will occupy the collision zone during the same time, including determining a time at current vehicle speed and predicted path of when the host vehicle will enter the collision zone, and issuing the warning if the time to the collision zone is below a first predetermined threshold; and providing automatic braking of the host vehicle, by the controller, if the time to the collision zone is less than a second predetermined threshold that is shorter than the first predetermined threshold. ### Claim 2 (depends on 1) The method according to claim 1 wherein segmenting the intersection into different regions includes identifying an approach region before the intersection that the host vehicle will travel through, identifying a waiting region in the intersection that the host vehicle will travel through when making the turn, and identifying a no-waiting region in the intersection that the host vehicle will travel through when making the turn. ### Claim 3 (depends on 1) The method according to claim 1 wherein determining the predicted path of the host vehicle also includes considering geometry of the intersection that is used to refine the predicted path. ### Claim 4 (depends on 1) The method according to claim 1 wherein determining whether the host vehicle and the one or more remote vehicles will simultaneously occupy a collision zone in the intersection includes determining a duration that the host vehicle will occupy the collision zone based on the speed and the predicted path of the host vehicle, determining a duration that the one or more remote vehicles will occupy the collision zone based on the speed and predicted path of the one or more remote vehicles, and determining if the durations overlap. ### Claim 5 (depends on 1) The method according to claim 1 wherein determining whether the host vehicle and the one or more remote vehicles will simultaneously occupy a collision zone in the intersection includes determining whether the host vehicle and the one or more remote vehicles will simultaneously occupy the collision zone before the host vehicle enters the intersection. ### Claim 6 (depends on 1) The method according to claim 1 wherein determining whether the host vehicle and the one or more remote vehicles will simultaneously occupy a collision zone in the intersection includes determining whether the host vehicle and the one or more remote vehicles will simultaneously occupy the collision zone when the host vehicle is in the intersection when the one or more remote vehicles is a opposite direction traveling remote vehicle. ### Claim 7 (depends on 1) The method according to claim 1 further comprising determining that the host vehicle will not stop in the intersection when making the turn. ### Claim 8 (depends on 1) The method according to claim 1 wherein the first threshold is determined experimentally considering driver reaction time and system delays. ### Claim 9 (depends on 1) The method according to claim 1 wherein the second threshold for providing automatic braking is determined based on system delays. ### Claim 10 (depends on 1) The method according to claim 1 further comprising providing information of a potential collision to the host vehicle if the time to the collision zone is greater than the first predetermined threshold, but less than a third predetermined threshold. ### Claim 11 (depends on 1) The method according to claim 1 further comprising determining that the host vehicle is coming to or has come to a stop in the intersection in anticipation of making a turn after opposing traffic has cleared, wherein issuing a warning includes determining a likely path the host vehicle will make when making the turn, calculating a closeness coefficient between the likely path of the host vehicle and the predicted path of the host vehicle, and issuing the warning if the closeness coefficient is greater than a predetermined closeness threshold, a time that the one or more remote vehicles will enter the collision zone is less than a predetermined time threshold and brakes of the host vehicle have been released. ### Claim 12 (depends on 11) The method according to claim 11 wherein determining the closeness coefficient between the predicted path and the likely path includes using the equation: CPL=CX*CY, where: C X = N * Σ ⁢ ⁢ X Pi * X Li - Σ ⁢ ⁢ X Pi * Σ ⁢ ⁢ X Li N * Σ ⁡ ( X Pi ) 2 - ( Σ ⁢ ⁢ X Pi ) 2 * ⁢ N * Σ ⁢ ⁢ ( X Li ) 2 - ( Σ ⁢ ⁢ X Li ) 2 , ⁢ C Y = N * Σ ⁢ ⁢ Y Pi * Y Li - Σ ⁢ ⁢ Y Pi * Σ ⁢ ⁢ Y N * Σ ⁡ ( Y Pi ) 2 - ( Σ ⁢ ⁢ Y Pi ) 2 * ⁢ N * Σ ⁢ ⁢ ( Y Li ) 2 - ( Σ ⁢ ⁢ Y Li ) 2 , and where CPL , is the closeness coefficient, CX and Cy represent a correlation between the predicted path and the likely path in the X and Y directions, and are calculated using the closeness between individual nodes N, where (XPi, YPi) and (XLi, YLi) correspond to (X,Y) coordinates of the nodes N on the predicted path and the likely path, respectively. ### Claim 13 (depends on 1) The method according to claim 1 wherein determining a predicted path of the host vehicle includes determining the predicted path of the host vehicle based on a yaw rate and/or road wheel angle of the host vehicle. ### Claim 14 (depends on 1) The method according to claim 1 wherein determining if the host vehicle is likely to turn left or right at the intersection includes using information and data from one or more of turn signal activity, a map database, a long range radar sensor on the vehicle, a short range radar sensor on the vehicle, cameras on the vehicle, a lidar sensor on the vehicle, V2X communications, Internet communications and satellite communications. ### Claim 15 (depends on 1) The method according to claim 1 wherein segmenting the intersection and surrounding area into regions includes segmenting the intersection surrounding regions based on intersection geometry and vehicle state. ### Claim 16 (depends on 1) The method according to claim 1 further comprising determining whether the host vehicle should enter the intersection including determining whether a traffic signal is red and a distance to an intersection stop bar. ### Claim 17 A method for collision threat assessment when a host vehicle is turning left or right at an intersection, said method comprising: determining if the host vehicle is likely to turn left or right at the intersection with a predetermined level of confidence; segmenting the intersection and surrounding area into a plurality of different regions where each region has a different level of collision threat risk if the host vehicle is likely to turn left or right at the intersection; obtaining speed, velocity and position data of the host vehicle using sensors on the host vehicle; obtaining speed, velocity and position data of one or more remote vehicles and/or other objects in or near the intersection, including using information and data from one or more of a map database, a long range radar sensor on the vehicle, a short range radar sensor on the vehicle, cameras on the vehicle, a lidar sensor on the vehicle, V2X communications, Internet communications and satellite communications; determining, by a controller including a processor, a predicted path of the host vehicle based on the speed, velocity and position data as the host vehicle travels through the intersection for each of the different segmented regions; determining, by the controller, a predicted path of one or more of the remote vehicles based on the speed, velocity and position data as the one or more vehicles travel through the intersection; determining, by the controller, a duration that the host vehicle will occupy a collision zone based on the speed and the predicted path of the host vehicle; determining, by the controller, a duration that the one or more remote vehicles will occupy the collision zone based on the speed and predicted path of the one or more remote vehicles; determining, by the controller, if the durations overlap; determining, by the controller, that the host vehicle will not stop in the intersection when making the turn; issuing, by the controller, a warning to a driver of the host vehicle if the durations overlap and if a time to enter the collision zone by the host vehicle at a current speed and predicted path of the host vehicle is less than a first predetermined threshold; providing automatic braking of the host vehicle, by the controller, if the time to the collision zone is less than a second predetermined threshold that is shorter than the first predetermined threshold; and providing, by the controller, information of a potential collision to the host vehicle if the time to the collision zone is greater than the first predetermined threshold, but less than a third predetermined threshold. ### Claim 18 (depends on 17) The method according to claim 17 wherein segmenting the intersection into different regions includes identifying an approach region before the intersection that the host vehicle will travel through, identifying a waiting region in the intersection that the host vehicle will travel through when making the turn, and identifying a no-waiting region in the intersection that the host vehicle will travel through when making the turn. ### Claim 19 (depends on 17) The method according to claim 17 wherein determining the predicted path of the host vehicle also includes considering geometry of the intersection that is used to refine the predicted path. ### Claim 20 (depends on 17) The method according to claim 17 wherein the first threshold is determined experimentally considering driver reaction time and system delays and the second threshold is determined based on system delays. ### Claim 21 (depends on 17) The method according to claim 17 wherein segmenting the intersection and surrounding area into regions includes segmenting the intersection surrounding regions based on intersection geometry and vehicle state. ### Claim 22 A method for collision threat assessment when a host vehicle is turning left or right at an intersection, said method comprising: determining if the host vehicle is likely to turn left or right at the intersection with a predetermined level of confidence; segmenting the intersection and surrounding area into a plurality of different regions where each region has a different level of collision threat risk if the host vehicle is likely to turn left or right at the intersection; obtaining speed, velocity and position data of the host vehicle using sensors on the host vehicle; obtaining speed, velocity and position data of one or more remote vehicles and/or other objects in or near the intersection, including using information and data from one or more of a map database, a long range radar sensor on the vehicle, a short range radar sensor on the vehicle, cameras on the vehicle, a lidar sensor on the vehicle, V2X communications, Internet communications and satellite communications; determining, by a controller including a processor, a predicted path of the host vehicle based on the speed, velocity and position data as the host vehicle travels through the intersection for each of the different segmented regions; determining, by the controller, a predicted path of one or more of the remote vehicles based on the speed, velocity and position data as the one or more vehicles travel through the intersection; determining, by the controller, a duration that the host vehicle will occupy a collision zone based on the speed and the predicted path of the host vehicle; determining, by the controller, a duration that the one or more remote vehicles will occupy the collision zone based on the speed and predicted path of the one or more remote vehicles; determining, by the controller, if the durations overlap; determining, by the controller, that the host vehicle is coming to or has come to a stop in the intersection in anticipation of making a turn after opposing traffic has cleared; determining, by the controller, a likely path the host vehicle will take when making the turn; calculating, by the controller, a closeness coefficient between the likely path of the host vehicle and the predicted path of the host vehicle; issuing, by the controller, a warning if the closeness coefficient is greater than the predetermined closeness threshold, the time that the one or more remote vehicles will enter the collision zone is less than a predetermined time threshold, and brakes of the host vehicle have been released; providing automatic braking of the host vehicle, by the controller, if the time to the collision zone is less than a second predetermined threshold that is shorter than the first predetermined threshold; and providing, by the controller, information of a potential collision to the host vehicle if the time to the collision zone is greater than the first predetermined threshold, but less than a third predetermined threshold. ### Claim 23 (depends on 22) The method according to claim 22 wherein determining the closeness coefficient between the predicted path and the likely path includes using the equation: CPL=CX*CY, where: C X = N * Σ ⁢ ⁢ X Pi * X Li - Σ ⁢ ⁢ X Pi * Σ ⁢ ⁢ X Li N * Σ ⁡ ( X Pi ) 2 - ( Σ ⁢ ⁢ X Pi ) 2 * ⁢ N * Σ ⁢ ⁢ ( X Li ) 2 - ( Σ ⁢ ⁢ X Li ) 2 , ⁢ C Y = N * Σ ⁢ ⁢ Y Pi * Y Li - Σ ⁢ ⁢ Y Pi * Σ ⁢ ⁢ Y N * Σ ⁡ ( Y Pi ) 2 - ( Σ ⁢ ⁢ Y Pi ) 2 * ⁢ N * Σ ⁢ ⁢ ( Y Li ) 2 - ( Σ ⁢ ⁢ Y Li ) 2 , and where CPLis the closeness coefficient, CX and Cy represent a correlation between the predicted path and the likely path in the X and Y directions, and are calculated using the closeness between individual nodes N, where (XPi,YPi)and (XLi, YLi) correspond to (X,Y) coordinates of the nodes N on the predicted path and the likely path, respectively. ### Claim 24 (depends on 22) The method according to claim 22 wherein segmenting the intersection into different regions includes identifying an approach region before the intersection that the host vehicle will travel through, identifying a waiting region in the intersection that the host vehicle will travel through when making the turn, and identifying a no-waiting region in the intersection that the host vehicle will travel through when making the turn. ### Claim 25 (depends on 22) The method according to claim 22 wherein determining the predicted path of the host vehicle also includes considering geometry of the intersection that is used to refine the predicted path. ### Claim 26 (depends on 22) The method according to claim 22 wherein the first threshold is determined experimentally considering driver reaction time and system delays and the second threshold is determined based on system delays. ### Claim 27 (depends on 22) The method according to claim 22 wherein segmenting the intersection and surrounding area into regions includes segmenting the intersection surrounding regions based on intersection geometry and vehicle state.