# US 10,372,136 B2 · Local trajectory planning method and apparatus for smart vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,372,136 B2 - Document ID: US10372136B2 - Status: Granted - Kind: B2 - Application number: 15/276,751 - Filed: 2016-09-26 - Published: 2017-12-21 - Granted: 2019-08-06 - Earliest claimed: 2016-06-21 - Assignees: Baidu Online Network Technology (Beijing) Co., Ltd. - Applicants: Baidu Online Network Technology (Beijing) Co., Ltd. - Inventors: Wenli Yang; Tianlei Zhang; Lintao Li - Web page: https://patentgrep.com/patent/US10372136B2 ## CPC - B60W 60/0011: {involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles} - B60W 30/143: {Speed control (B60W30/16 takes precedence)} - B60W 30/18163: {Lane change; Overtaking manoeuvres} - B62D 15/0255: {Automatic changing of lane, e.g. for passing another vehicle} - G01C 21/3407: {specially adapted for specific applications} - G01C 21/3691: {Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions (arrangements for giving variable traffic instructions G08G1/09)} - G05D 1/00: Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots (drive control systems specially adapted for autonomous road vehicles B60W60/00) - G08G 1/167: {Driving aids for lane monitoring, lane changing, e.g. blind spot detection} - B60W 2050/0088: {Adaptive recalibration} - B60W 2552/30: Road curve radius - B60W 2554/00: Input parameters relating to objects - B60W 2554/402: Type - B60W 2554/4041: Position - B60W 2554/802: Longitudinal distance - B60W 2720/10: Longitudinal speed - B60W 2720/14: Yaw - 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 - B62D: MOTOR VEHICLES; TRAILERS (steering, or guiding on a desired track, of agricultural machines or implements A01B69/00; wheels, castors, axles, increasing wheel adhesion B60B; vehicle tyres, tyre inflation or tyre changing B60C; connections between vehicles of a train or the like B60D; vehicles for use on rail and road, amphibious or convertible vehicles B60F; suspension arrangements B60G; heating, cooling, ventilating or other air treating devices B60H; windows, windscreens, non-fixed roofs, doors or similar devices, protective coverings for vehicles not in use B60J; propulsion plant arrangements, auxiliary drives, transmissions, controls, instrumentation or dashboards B60K; electric equipment or propulsion of electrically-propelled vehicles B60L; power supply for electrically-propelled vehicles B60M; passenger accommodation not otherwise provided for B60N; adaptations for load transportation or to carry special loads or objects B60P; arrangement of signalling or lighting devices, the mounting or supporting thereof or circuits therefor, for vehicles in general B60Q; vehicles, vehicle fittings or vehicle parts not otherwise provided for B60R; servicing, cleaning, repairing, supporting, lifting, or manoeuvring, not otherwise provided for, B60S; brake arrangements, brake control systems or parts thereof B60T; air-cushion vehicles B60V; motor-cycles, accessories therefor B62J, B62K; testing of vehicles G01M) - 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) - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - 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 The present invention provides a local trajectory planning method and apparatus for a smart vehicle, pre-acquiring path planning information from a starting location to a destination; the method comprising: determining a target lane; sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information; performing speed planning for the sampled alternative curves according to a current travel environment; selecting one of the alternative curves after the speed planning is performed as a target trajectory. Local trajectory planning of the smart vehicle is achieved through the present invention. ## Claims ### Claim 1 A local trajectory planning method for a smart vehicle, wherein path planning information from a starting location to a destination is pre-acquired; the method comprising: determining a target lane; sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information; performing speed planning for the sampled alternative curves according to a current travel environment; selecting one of the alternative curves after the speed planning is performed as a target trajectory, wherein a starting point direction of the alternative curves accords with the current travel direction of the smart vehicle, and a starting point curvature of the alternative curves is determined by a current steering wheel turn angle and an axle distance of the smart vehicle. ### Claim 2 (depends on 1) The method according to claim 1, wherein the method is executed cyclically or triggered by a specific event. ### Claim 3 (depends on 1) The method according to claim 1, wherein the determining the target lane comprises: acquiring information of the target lane from the path planning information; or performing behavior decision-making based on the current travel environment, a behavior decision-making result comprising information of the target lane. ### Claim 4 (depends on 3) The method according to claim 3, wherein when the smart vehicle travels to a path having a plurality of lanes, executing the performing behavior decision-making based on the current travel environment. ### Claim 5 (depends on 3) The method according to claim 3, wherein the current travel environment includes road factor and surrounding traffic factor; the road factor comprises one or any combinations of the following factors: whether a lane where the smart vehicle lies currently may reach the destination, a remaining travel distance of the lane where the smart vehicle lies currently, whether there is an available lane on the left side of the lane where the smart vehicle lies currently, whether there is an available lane on the right side of the lane where the smart vehicle lies currently, information about lanes accessible to the destination on the road segment where the smart vehicle lies currently, and information about the lane having a maximum remaining travel distance on the road segment where the smart vehicle lies currently; the surrounding traffic factor comprises one or any combinations of the following factors: a location, speed, category, and size of an obstacle within a preset range surrounding the location where the smart vehicle lies currently. ### Claim 6 (depends on 3) The method according to claim 3, wherein the performing behavior decision-making comprises: matching the current travel environment with a preset decision-making rule, and determining a matching behavior decision-making result; or inputting the current travel environment into a decision-making model and acquiring a decision-making result output by the decision-making model, the decision-making model being pre-built in a manner of employing machine learning. ### Claim 7 (depends on 1) The method according to claim 1, wherein the sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information comprises: acquiring information of the target lane from the path planning information; determining an ending point range of the alternative curves in the target lane according to a current speed of the smart vehicle; sampling ending points of the alternative curves in the ending point range, considering the current location of the smart vehicle as a starting point of the alternative curves, sampling respectively according to the starting point of the alternative curves and the ending points of the alternative curves, and forming the alternative curves. ### Claim 8 (depends on 7) The method according to claim 7, wherein upon sampling based on the starting point and ending points of the alternative curves, sampling based on a Euler spiral and forming alternative curves. ### Claim 9 (depends on 7) The method according to claim 7, wherein the sampling ending points of the alternative curves in the ending point range comprises: performing equidistant sampling on a lane centerline within the ending point range to obtain ending points of respective alternative curves, the ending point directions and curvature of the alternative curves all being consistent with the target lane. ### Claim 10 (depends on 1) The method according to claim 1, wherein the performing speed planning for the sampled alternative curves according to a current travel environment comprises: when a distance between the smart vehicle and a vehicle ahead is smaller than a target following distance, performing uniform deceleration at a preset first acceleration on the alternative curve; when the distance between the smart vehicle and the vehicle ahead is larger than the target following distance, performing uniform acceleration at a preset second acceleration on the alternative curve; when the current speed of the smart vehicle is larger than a lane speed limit, performing uniform deceleration at a preset third acceleration on the alternative curve; when the current speed of the smart vehicle is larger than a bend safe speed, performing uniform deceleration at a preset fourth acceleration on the alternative curve. ### Claim 11 (depends on 1) The method according to claim 1, wherein the selecting one of the alternative curves after the speed planning is performed as a target trajectory comprises: performing evaluation for alternative curves after the speed planning and selecting an optimal alternative curve as a target trajectory, based on one or any combinations of the following factors: distances between the alternative curves and an obstacle, curvature of the alternative curves, acceleration of the alternative curves and time consumed for completion of the alternative curves. ### Claim 12 (depends on 1) The method according to claim 1, wherein when the smart vehicle runs out of the planned path, triggering to perform path planning again and acquiring new path planning information, and performing subsequent local trajectory planning based on the new path planning information. ### Claim 13 (depends on 1) The method according to claim 1, wherein the method further comprises: sending information of the target trajectory to a control system of the smart vehicle so that the control system controls the smart vehicle to travel along the target trajectory. ### Claim 14 A local trajectory planning apparatus for use in a smart vehicle, the apparatus comprising: at least one processor; and a memory storing instructions, which when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising: determining a target lane; sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information; performing speed planning for the sampled alternative curves according to a current travel environment; selecting one of the alternative curves after the speed planning is performed as a target trajectory, wherein a starting point direction of the alternative curves accords with the current travel direction of the smart vehicle, and a starting point curvature of the alternative curves is determined by a current steering wheel turn angle and an axle distance of the smart vehicle. ### Claim 15 (depends on 14) The apparatus according to claim 14, wherein the operations are executed cyclically or triggered by a specific event. ### Claim 16 (depends on 14) The apparatus according to claim 14, wherein the operation of determining the target lane comprises: acquiring information of the target lane from the path planning information; or performing behavior decision-making based on the current travel environment, a behavior decision-making result comprising information of the target lane. ### Claim 17 (depends on 16) The apparatus according to claim 16, wherein when the smart vehicle travels to a path having a plurality of lanes, the operation or performing behavior decision-making based on the current travel environment is executed. ### Claim 18 (depends on 16) The apparatus according to claim 16, wherein the current travel environment includes road factor and surrounding traffic factor; the road factor comprises one or any combinations of the following factors: whether a lane where the smart vehicle lies currently may reach the destination, a remaining travel distance of the lane where the smart vehicle lies currently, whether there is an available lane on the left side of the lane where the smart vehicle lies currently, whether there is an available lane on the right side of the lane where the smart vehicle lies currently, information about lanes accessible to the destination on the road segment where the smart vehicle lies currently, and information about the lane having a maximum remaining travel distance on the road segment where the smart vehicle lies currently; the surrounding traffic factor comprises one or any combinations of the following factors: a location, speed, category, and size of an obstacle within a preset range surrounding the location where the smart vehicle lies currently. ### Claim 19 (depends on 16) The apparatus according to claim 16, wherein the operation of performing behavior decision-making comprises: matching the current travel environment with a preset decision-making rule, and determining a matching behavior decision-making result; or inputting the current travel environment into a decision-making model and acquiring a decision-making result output by the decision-making model, the decision-making model being pre-built in a manner of employing machine learning. ### Claim 20 (depends on 14) The apparatus according to claim 14, wherein the operation of sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information comprises: acquiring information of the target lane from the path planning information; determining an ending point range of the alternative curves in the target lane according to a current speed of the smart vehicle; sampling ending points of the alternative curves in the ending point range, considering the current location of the smart vehicle as a starting point of the alternative curves, sampling respectively according to the starting point of the alternative curves and the ending points of the alternative curves, and forming the alternative curves. ### Claim 21 (depends on 20) The apparatus according to claim 20, wherein upon sampling based on the starting point and ending points of the alternative curves, sampling based on a Euler spiral and forming alternative curves. ### Claim 22 (depends on 20) The apparatus according to claim 20, wherein the operation of sampling ending points of the alternative curves in the ending point range comprises: performing equidistant sampling on a lane centerline within the ending point range to obtain ending points of respective alternative curves, the ending point directions and curvature of the alternative curves all being consistent with the target lane. ### Claim 23 (depends on 14) The apparatus according to claim 14, wherein the operation of performing speed planning for the sampled alternative curves according to a current travel environment comprises: when a distance between the smart vehicle and a vehicle ahead is smaller than a target following distance, performing uniform deceleration at a preset first acceleration on the alternative curve; when the distance between the smart vehicle and the vehicle ahead is larger than the target following distance, performing uniform acceleration at a preset second acceleration on the alternative curve; when the current speed of the smart vehicle is larger than a lane speed limit, performing uniform deceleration at a preset third acceleration on the alternative curve; when the current speed of the smart vehicle is larger than a bend safe speed, performing uniform deceleration at a preset fourth acceleration on the alternative curve. ### Claim 24 (depends on 14) The apparatus according to claim 14, wherein the operation of selecting one of the alternative curves after the speed planning is performed as a target trajectory comprises: performing evaluation for alternative curves after the speed planning and selecting an optimal alternative curve as a target trajectory, based on one or any combinations of the following factors: distances between the alternative curves and an obstacle, curvature of the alternative curves, acceleration of the alternative curves and time consumed for completion of the alternative curves. ### Claim 25 (depends on 14) The apparatus according to claim 14, wherein when the smart vehicle runs out of the planned path, triggering to perform path planning again and acquiring new path planning information, and performing subsequent local trajectory planning based on the new path planning information. ### Claim 26 (depends on 14) The apparatus according to claim 14, the operation further comprises: sending information of the target trajectory to a control system of the smart vehicle so that the control system controls the smart vehicle to travel along the target trajectory. ### Claim 27 A computer storage medium, wherein the computer storage medium is coded with a computer program, and when the program is executed by one or more computers, the one or more computers perform the following operations: determining a target lane; sampling alternative curves from a current location of the smart vehicle to a target lane according to the path planning information; performing speed planning for the sampled alternative curves according to a current travel environment; selecting one of the alternative curves after the speed planning is performed as a target trajectory, wherein a starting point direction of the alternative curves accords with the current travel direction of the smart vehicle, and a starting point curvature of the alternative curves is determined by a current steering wheel turn angle and an axle distance of the smart vehicle.