# US 12,110,017 B2 · Street lane-level matching method for automotive applications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,110,017 B2 - Document ID: US12110017B2 - Status: Granted - Kind: B2 - Application number: 17/684,241 - Filed: 2022-03-01 - Published: 2023-09-07 - Granted: 2024-10-08 - Earliest claimed: 2022-03-01 - Assignees: Ford Global Technologies, LLC - Applicants: Ford Global Technologies, LLC - Inventors: Rahi Hemmati; Hamed Asadi; Azin Neishaboori; Mohsen Bahrami - Web page: https://patentgrep.com/patent/US12110017B2 ## CPC - B60W 30/12: Lane keeping - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - B60W 40/105: Speed - B60W 2552/10: Number of lanes - B60W 2552/53: Road markings, e.g. lane marker or crosswalk - G08G 1/167: {Driving aids for lane monitoring, lane changing, e.g. blind spot detection} - 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 Lane-level matching is provided. For a time index of a time slot of a period of receiving consecutive position information, a lateral distance of the vehicle to each of a plurality of lanes of a roadway is calculated using data from sensors of a vehicle. For each lane of the plurality of lanes, a positional uncertainty of the vehicle in an orthogonal direction to a lane bearing of the lane is calculated, a lane confidence based on an average of confidence values of the lane over the period of receiving consecutive position information is calculated, and a lane probability based on the lane confidence is calculated. For the time index, it is identified which of the lanes has a highest probability as being the lane to which the vehicle is matched. ## Claims ### Claim 1 A system for lane-level matching, comprising: a computing device programmed to: calculate, for a time index of a time slot of a period of receiving consecutive vehicle position information, using data from sensors of a vehicle, a lateral distance of the vehicle to each of a plurality of lanes of a roadway; for each lane of the plurality of lanes: calculate a positional uncertainty of the vehicle in an orthogonal direction to a lane bearing of the lane; calculate a lane confidence based on an average of confidence values of the lane over the period of receiving the consecutive position information; and calculate a lane probability based on the lane confidence; and identify, for the time index, which of the lanes has a highest probability as being the lane to which the vehicle position, location, and/or direction is matched; and navigate the vehicle along the plurality of lanes, using the lane to which the vehicle is matched. ### Claim 2 (depends on 1) The system of claim 1, wherein the computing device is further programmed to: divide a time period between a current position report to a next report into one or more time slots; calculate the confidence values for predicted position of the vehicle over each time slot; and compute the lane confidence as an average of the confidence values of the time slots. ### Claim 3 (depends on 1) The system of claim 1, wherein the computing device is further programmed to estimate the positional uncertainty of the vehicle in view of estimated Gaussian noise in each orthogonal direction. ### Claim 4 (depends on 1) The system of claim 1, wherein the computing device is further programmed to utilize steering direction of the vehicle as an input to the lane confidence of the vehicle as a relative vehicle heading angle with respect to the lane bearing. ### Claim 5 (depends on 1) The system of claim 1, wherein the computing device is further programmed to define each lane as lines centered within the lanes connecting sample points of the middle of each lane. ### Claim 6 (depends on 1) The system of claim 1, wherein a sum of the lane probability for each of the lanes at the time index adds up to the entire sample space. ### Claim 7 (depends on 1) The system of claim 1, wherein the computing device is further programmed to set the lane confidence to zero for any of the lanes having greater than a predefined maximum threshold angle from a heading angle of the vehicle. ### Claim 8 (depends on 1) The system of claim 1, wherein the computing device is programmed to initialize the lane probability for each of the lanes as being equally likely. ### Claim 9 A method for lane-level matching, comprising: calculating, for a time index of a time slot of a period of receiving consecutive vehicle position information, using data from sensors of a vehicle, a lateral distance of the vehicle to each of a plurality of lanes of a roadway; for each lane of the plurality of lanes: calculating a positional uncertainty of the vehicle in an orthogonal direction to a lane bearing of the lane; calculating a lane confidence based on an average of confidence values of the lane over the period of receiving consecutive position information; and calculating a lane probability based on the lane confidence; and identifying, for the time index, which of the lanes has a highest probability as being the lane to which the vehicle position, location, and/or direction is matched; and navigating the vehicle along the plurality of lanes, using the lane to which the vehicle is matched. ### Claim 10 (depends on 9) The method of claim 9, further comprising: dividing a time period between a current position report to a next report into one or more time slots; calculating the confidence values for predicted position of the vehicle over each time slot; and computing the lane confidence as an average of the confidence values of the time slots. ### Claim 11 (depends on 9) The method of claim 9, further comprising estimating the positional uncertainty of the vehicle in view of estimated Gaussian noise in each orthogonal direction. ### Claim 12 (depends on 9) The method of claim 9, further comprising utilizing steering direction of the vehicle as an input to the lane confidence of the vehicle as a relative vehicle heading angle with respect to the lane bearing. ### Claim 13 (depends on 9) The method of claim 9, further comprising defining each lane as lines centered within the lanes connecting sample points of the middle of each lane. ### Claim 14 (depends on 9) The method of claim 9, wherein a sum of the lane probability for each of the lanes at the time index adds up to the entire sample space. ### Claim 15 (depends on 9) The method of claim 9, further comprising setting the lane confidence to zero for any of the lanes having greater than a predefined maximum threshold angle from a heading angle of the vehicle. ### Claim 16 (depends on 9) The method of claim 9, further comprising initializing the lane probability for each of the lanes as being equally likely. ### Claim 17 A non-transitory computer-readable medium comprising instructions for lane-level matching that, when executed by a computing device, cause the computing device to perform operations including to: calculate, for a time index of a time slot of a period of receiving consecutive vehicle position information, using data from sensors of a vehicle, a lateral distance of the vehicle to each of a plurality of lanes of a roadway; for each lane of the plurality of lanes: calculate a positional uncertainty of the vehicle in an orthogonal direction to a lane bearing of the lane; calculate a lane confidence based on an average of confidence values of the lane over the period of receiving consecutive position information; and calculate a lane probability based on the lane confidence; and identify, for the time index, which of the lanes has a highest probability as being the lane to which the vehicle position, location, and/or direction is matched; and navigate the vehicle along the plurality of lanes, using the lane to which the vehicle is matched. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the computing device, cause the computing device to perform operations including to: divide a time period between a current position report to a next report into one or more time slots; calculate the confidence values for predicted position of the vehicle over each time slot; and compute the lane confidence as an average of the confidence values of the time slots. ### Claim 19 (depends on 17) The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the computing device, cause the computing device to perform operations including to estimate the positional uncertainty of the vehicle in view of estimated Gaussian noise in each orthogonal direction. ### Claim 20 (depends on 17) The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the computing device, cause the computing device to perform operations including to utilize steering direction of the vehicle as an input to the lane confidence of the vehicle as a relative vehicle heading angle with respect to the lane bearing. ### Claim 21 (depends on 17) The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the computing device, cause the computing device to perform operations including to define each lane as lines centered within the lanes connecting sample points of the middle of each lane. ### Claim 22 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein a sum of the lane probability for each of the lanes at the time index adds up to the entire sample space, and further comprising instructions that, when executed by the computing device, cause the computing device to perform operations including to initialize the lane probability for each of the lanes as being equally likely. ### Claim 23 (depends on 17) The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the computing device, cause the computing device to perform operations including to set the lane confidence to zero for any of the lanes having greater than a predefined maximum threshold angle from a heading angle of the vehicle. ### Claim 24 (depends on 1) The system of claim 1, wherein the computing device is further programmed to use the lane to which the vehicle is matched for Traffic Light Classification (TLC) by matching the vehicle to traffic light signals and signal timing provided to the vehicle. ### Claim 25 (depends on 1) The system of claim 1, wherein the computing device is further programmed to use the lane to which the vehicle is matched for advanced driver assistant system (ADAS) and/or for autonomous driving. ### Claim 26 (depends on 1) The system of claim 1, wherein the computing device is further programmed to use the lane to which the vehicle is matched for lane-level matching the vehicle to high-resolution map data.