# US 12,001,215 B2 · Systems and methods for generating basis paths for autonomous vehicle motion control > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,001,215 B2 - Document ID: US12001215B2 - Status: Granted - Kind: B2 - Application number: 18/158,251 - Filed: 2023-01-23 - Published: 2023-05-25 - Granted: 2024-06-04 - Earliest claimed: 2020-09-11 - Assignees: Uatc, LLC - Applicants: Uatc, LLC - Inventors: Chenggang Liu; David McAllister Bradley; Daoyuan Jia - Web page: https://patentgrep.com/patent/US12001215B2 ## CPC - G05D 1/0217: {in accordance with energy consumption, time reduction or distance reduction criteria} - B60W 60/001: {Planning or execution of driving tasks} - B60W 60/0011: {involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles} - G01C 21/26: specially adapted for navigation in a road network - G01C 21/3407: {specially adapted for specific applications} - G01C 21/3658: {Lane guidance} - G05D 1/644: Optimisation of travel parameters, e.g. of energy consumption, journey time or distance - B60W 30/18163: {Lane change; Overtaking manoeuvres} - B60W 2520/06: Direction of travel - B60W 2520/10: Longitudinal speed - B60W 2520/105: Longitudinal acceleration - B60W 2520/125: Lateral acceleration - B60W 2552/10: Number of lanes - B60W 2552/53: Road markings, e.g. lane marker or crosswalk - B60W 2555/60: Traffic rules, e.g. speed limits or right of way - 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 - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) ## Abstract Systems and methods for basis path generation are provided. In particular, a computing system can obtain a target nominal path. The computing system can determine a current pose for an autonomous vehicle. The computing system can determine, based at least in part on the current pose of the autonomous vehicle and the target nominal path, a lane change region. The computing system can determine one or more merge points on the target nominal path. The computing system can, for each respective merge point in the one or more merge points, generate a candidate basis path from the current pose of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate basis path. The computing system can select one or more candidate basis paths based on the suitability classification for each respective candidate basis path in the plurality of candidate basis paths. ## Claims ### Claim 1 A computer-implemented method for controlling an autonomous vehicle, comprising: determining a target lane for the autonomous vehicle that is different from a current lane in which the autonomous vehicle is operating; identifying a merge point in the target lane, the merge point describing a location between two vehicles in the target lane; determining that the autonomous vehicle should enter the target lane at the merge point, wherein determining that the autonomous vehicle should enter the target lane comprises: processing a plurality of features with a learned lane change model to determine that a lane change criterium is satisfied, wherein the lane change criterium comprises a limit on one or more of the plurality of features, and wherein the lane change criterium is indicative of a cost to enter the target lane at the merge point, and determining that entering the target lane at the merge point does not violate a plurality of constraints; determining a motion plan for entering the target lane at the merge point; and controlling the autonomous vehicle to navigate in accordance with the motion plan. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein identifying the merge point in the target lane comprises: identifying a lane change region in the target lane; identifying one or more potential merge points in the target lane based on the lane change region; and selecting the merge point from the one or more potential merge points. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of features comprises at least one of a respective acceleration, a respective speed, or a respective turning rate associated with entering the target lane at the merge point. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the respective acceleration comprises a maximum acceleration, wherein the respective speed comprises a maximum speed, and wherein the respective turning rate comprises a maximum turning rate. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein determining whether the lane change criterium is satisfied comprises at least one of determining whether the maximum acceleration exceeds a predetermined acceleration threshold; determining whether the maximum speed exceeds a predetermined speed threshold; or determining whether the maximum turning rate exceeds a predetermined turning rate threshold. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the target lane is adjacent to the current lane. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the learned lane change model is configured to evaluate the cost associated with one or more vehicle actions associated with entering the target lane at the merge point. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of constraints comprises one or more of a stopping location, a velocity target, or a safe buffer distance. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein controlling the autonomous vehicle to navigate in accordance with the motion plan comprises converting the motion plan into one or more vehicle controls for implementation by the autonomous vehicle. ### Claim 10 An autonomous vehicle control system, comprising: one or more processors; and one or more non-transitory, computer-readable media storing instructions that cause the one or more processors to perform operations comprising: determining a target lane for an autonomous vehicle that is different from a current lane in which the autonomous vehicle is operating; identifying a merge point in the target lane, the merge point describing a location between two vehicles in the target lane; determining that the autonomous vehicle should enter the target lane at the merge point, wherein determining that the autonomous vehicle should enter the target lane comprises: processing a plurality of features with a learned lane change model to determine that a lane change criterium is satisfied, wherein the lane change criterium comprises a limit on one or more of the plurality of features, and wherein the lane change criterium is indicative of a cost to enter the target lane at the merge point, and determining that entering the target lane at the merge point does not violate a plurality of constraints; determining a motion plan for entering the target lane at the merge point; and controlling the autonomous vehicle to navigate in accordance with the motion plan. ### Claim 11 (depends on 10) The autonomous vehicle control system of claim 10, wherein identifying the merge point in the target lane comprises: identifying a lane change region in the target lane; identifying one or more potential merge points in the target lane based on the lane change region; and selecting the merge point from the one or more potential merge points. ### Claim 12 (depends on 10) The autonomous vehicle control system of claim 10, wherein the plurality of features comprises at least one of a respective acceleration, a respective speed, or a respective turning rate associated with entering the target lane at the merge point. ### Claim 13 (depends on 12) The autonomous vehicle control system of claim 12, wherein the respective acceleration comprises a maximum acceleration, wherein the respective speed comprises a maximum speed, and wherein the respective turning rate comprises a maximum turning rate. ### Claim 14 (depends on 13) The autonomous vehicle control system of claim 13, wherein determining whether the lane change criterium is satisfied comprises at least one of determining whether the maximum acceleration exceeds a predetermined acceleration threshold; determining whether the maximum speed exceeds a predetermined speed threshold; or determining whether the maximum turning rate exceeds a predetermined turning rate threshold. ### Claim 15 (depends on 10) The autonomous vehicle control system of claim 10, wherein the learned lane change model is configured to evaluate the cost associated with one or more vehicle actions associated with entering the target lane at the merge point. ### Claim 16 (depends on 10) The autonomous vehicle control system of claim 10, wherein the plurality of constraints comprises one or more of a stopping location, a velocity target, or a safe buffer distance. ### Claim 17 (depends on 10) The autonomous vehicle control system of claim 10, wherein controlling the autonomous vehicle to navigate in accordance with the motion plan comprises converting the motion plan into one or more vehicle controls for implementation by the autonomous vehicle. ### Claim 18 One or more non-transitory, computer-readable media storing instructions for implementation to cause one or more processors to perform operations comprising: determining a target lane for an autonomous vehicle that is different from a current lane in which the autonomous vehicle is operating; identifying a merge point in the target lane, the merge point describing a location between two vehicles in the target lane; determining that the autonomous vehicle should enter the target lane at the merge point, wherein determining that the autonomous vehicle should enter the target lane comprises: processing a plurality of features with a learned lane change model to determine that a lane change criterium is satisfied, wherein the lane change criterium comprises a limit on one or more of the plurality of features, and wherein the lane change criterium is indicative of a cost to enter the target lane at the merge point, and determining that entering the target lane at the merge point does not violate a plurality of constraints; determining a motion plan for entering the target lane at the merge point; and controlling the autonomous vehicle to navigate in accordance with the motion plan.