# US 11,029,167 B2 · Probabilistic navigation system and method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,029,167 B2 - Document ID: US11029167B2 - Status: Granted - Kind: B2 - Application number: 16/548,779 - Filed: 2019-08-22 - Published: 2019-12-12 - Granted: 2021-06-08 - Earliest claimed: 2018-02-01 - Assignees: Beijing Voyager Technology Co., Ltd. - Applicants: Beijing Voyager Technology Co., Ltd. - Inventors: Zafar Takhirov - Web page: https://patentgrep.com/patent/US11029167B2 ## CPC - G01C 21/3461: {Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries} - G01C 21/3626: {Details of the output of route guidance instructions (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968)} - G01C 21/3697: {Output of additional, non-guidance related information, e.g. low fuel level (G01C21/3679 takes precedence)} - G08G 1/164: {Centralised systems, e.g. external to vehicles} - G08G 1/165: {for passive traffic, e.g. including static obstacles, trees} - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - 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) - 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 Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models. ## Claims ### Claim 1 A method, comprising: obtaining a map of an environment comprising a plurality of objects each associated with a probability model, each of the probability models at least describing one or more probability fields of a vehicle colliding with at least one of the plurality of objects; obtaining, using the vehicle, one or more global factors from one or more data stores; obtaining, using one or more sensors, one or more local factors; updating the probability fields of the probability models based on the one or more global factors and the one or more local factors, wherein the global factors apply to all of the objects, and the local factors apply to a portion of the objects, wherein at least a first global factor has a first degree of influence for a particular object of the plurality of objects, and wherein the first global factor has a second degree of influence for another object of the plurality of objects, the updating including adjusting a rate of decay for at least one of the probability fields of a corresponding probability model of the plurality of probability models; determining a navigation for the vehicle through at least a part of the environment based at least on a total collision probability of the vehicle with at least one of the plurality of objects along the navigation according to the updated probability models; and causing, in response to determining the navigation, the navigation of the vehicle through at least the part of the environment. ### Claim 2 (depends on 1) The method of claim 1, wherein: the objects comprise static and dynamic objects, the particular object comprises a particular static object, and the other object comprises a particular dynamic object; the static objects are substantially stationary with respect to ground; and the dynamic objects are movable with respect to ground. ### Claim 3 (depends on 1) The method of claim 1, wherein: the one or more global factors comprise at least one of weather, a global traffic condition, a season, a time, or a geographic location of the environment; and the one or more local factors comprise at least one of a local event, a local traffic condition, a local road condition, or one or more of the objects along the navigation. ### Claim 4 (depends on 1) The method of claim 1, wherein the one or more sensors are disposed at least on the vehicle or along a path of the navigation to monitor and dynamically update the local factors. ### Claim 5 (depends on 1) The method of claim 1, wherein the total collision probability with at least one of the objects along the navigation comprises a summation of probabilities of colliding with the objects along the navigation. ### Claim 6 (depends on 1) The method of claim 1, wherein determining the navigation for the vehicle through at least the part of the environment based at least on the total collision probability with at least one of the objects along the navigation according to the updated probability models comprises: determining the navigation for the vehicle through at least the part of the environment based at least on: minimizing the total collision probability with the objects along the navigation according to the updated probability models, and satisfying one or more constraints comprising at least one of minimizing a trip time, minimizing a trip distance, minimizing a trip cost, limiting energy consumption, or fulfilling a user request. ### Claim 7 (depends on 1) The method of claim 1, further comprising controlling the vehicle to move according to the determined navigation, wherein the navigation comprises at least one of a route, a moving speed, or a moving direction. ### Claim 8 A computing system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the computing system to perform operations comprising: obtaining a map of an environment comprising a plurality of objects each associated with a probability model, each of the probability models at least describing one or more probability fields of a vehicle colliding with at least one of the plurality of objects; obtaining, using the vehicle, one or more global factors from one or more data stores; obtaining, using one or more sensors, one or more local factors; updating the probability fields of the probability models based on the one or more global factors and the one or more local factors, wherein the global factors apply to all of the objects, and the local factors apply to a portion of the objects, wherein at least a first global factor has a first degree of influence for a particular object of the plurality of objects, and wherein the first global factor has a second degree of influence for another object of the plurality of objects, the updating including adjusting a rate of decay for at least one of the probability fields of a corresponding probability model of the plurality of probability models; determining a navigation for the vehicle through at least a part of the environment based at least on a total collision probability of the vehicle with at least one of the plurality of objects along the navigation according to the updated probability models; and causing, in response to determining the navigation, the navigation of the vehicle through at least the part of the environment. ### Claim 9 (depends on 8) The system of claim 8, wherein: the objects comprise static and dynamic objects, the particular object comprises a particular static object, and the other object comprises a particular dynamic object; the static objects are substantially stationary with respect to ground; and the dynamic objects are movable with respect to ground. ### Claim 10 (depends on 8) The system of claim 8, wherein: the one or more global factors comprise at least one of weather, a global traffic condition, a season, a time, or a geographic location of the environment; and the one or more local factors comprise at least one of a local event, a local traffic condition, a local road condition, or one or more of the objects along the navigation. ### Claim 11 (depends on 8) The system of claim 8, wherein the one or more sensors are disposed at least on the vehicle or along a path of the navigation to monitor and dynamically update the local factors. ### Claim 12 (depends on 8) The system of claim 8, wherein the total collision probability with at least one of the objects along the navigation comprises a summation of probabilities of colliding with the objects along the navigation. ### Claim 13 (depends on 8) The system of claim 8, wherein determining the navigation for the vehicle through at least the part of the environment based at least on the total collision probability with at least one of the objects along the navigation according to the updated probability models comprises: determining the navigation for the vehicle through at least the part of the environment based at least on: minimizing the total collision probability with the objects along the navigation according to the updated probability models, and satisfying one or more constraints comprising at least one of minimizing a trip time, minimizing a trip distance, minimizing a trip cost, limiting energy consumption, or fulfilling a user request. ### Claim 14 (depends on 8) The system of claim 8, further comprising controlling the vehicle to move according to the determined navigation, wherein the navigation comprises at least one of a route, a moving speed, or a moving direction. ### Claim 15 A non-transitory computer readable medium comprising instructions that, when executed, cause one or more processors to perform operations comprising: obtaining a map of an environment comprising a plurality of objects each associated with a probability model, each of the probability models at least describing one or more probability fields of a vehicle colliding with at least one of the plurality of objects; obtaining, using the vehicle, one or more global factors from one or more data stores; obtaining, using one or more sensors, one or more local factors; updating the probability fields of the probability models based on the one or more global factors and the one or more local factors, wherein the global factors apply to all of the objects, and the local factors apply to a portion of the objects, wherein at least a first global factor has a first degree of influence for a particular object of the plurality of objects, and wherein the first global factor has a second degree of influence for another object of the plurality of objects, the updating including adjusting a rate of decay for at least one of the probability fields of a corresponding probability model of the plurality of probability models; determining a navigation for the vehicle through at least a part of the environment based at least on a total collision probability of the vehicle with at least one of the plurality of objects along the navigation according to the updated probability models; and causing, in response to determining the navigation, the navigation of the vehicle through at least the part of the environment. ### Claim 16 (depends on 15) The non-transitory computer readable medium of claim 15, wherein: the objects comprise static and dynamic objects, the particular object comprises a particular static object, and the other object comprises a particular dynamic object; the static objects are substantially stationary with respect to ground; and the dynamic objects are movable with respect to ground. ### Claim 17 (depends on 15) The non-transitory computer readable medium of claim 15, wherein: the one or more global factors comprise at least one of weather, a global traffic condition, a season, a time, or a geographic location of the environment; and the one or more local factors comprise at least one of a local event, a local traffic a condition, a local road condition, or one or more of the objects along the navigation. ### Claim 18 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the one or more sensors are disposed at least on the vehicle or along a path of the navigation to monitor and dynamically update the local factors. ### Claim 19 (depends on 15) The non-transitory computer readable medium of claim 15, wherein the total collision probability with at least one of the objects along the navigation comprises a summation of probabilities of colliding with the objects along the navigation. ### Claim 20 (depends on 15) The non-transitory computer readable medium of claim 15, wherein determining the navigation for the vehicle through at least the part of the environment based at least on the total collision probability with at least one of the objects along the navigation according to the updated probability models comprises: determining the navigation for the vehicle through at least the part of the environment based at least on: minimizing the total collision probability with the objects along the navigation according to the updated probability models, and satisfying one or more constraints comprising at least one of minimizing a trip time, minimizing a trip distance, minimizing a trip cost, limiting energy consumption, or fulfilling a user request.