# US 10,480,952 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 10,480,952 B2 - Document ID: US10480952B2 - Status: Granted - Kind: B2 - Application number: 15/886,196 - Filed: 2018-02-01 - Published: 2019-08-01 - Granted: 2019-11-19 - Earliest claimed: 2018-02-01 - Assignees: DiDi Research America, LLC - Applicants: DiDi Research America, LLC - Inventors: Zafar Takhirov - Web page: https://patentgrep.com/patent/US10480952B2 ## 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 probabilistic navigation method, comprising: obtaining a map of an environment comprising a plurality of first objects each associated with a probability model, the environment comprising a metropolitan area, and the plurality of first objects including a first static object of the metropolitan area and a first dynamic object of the metropolitan area, the probability model at least describing probability fields of a second object colliding with each of the plurality of first objects, the probability fields decaying away from one or more physical boundaries of each of the plurality of first objects, the density and shape of each the probability fields being based on one or more physical properties of a corresponding first object of the plurality of first objects, the second object comprising an autonomous vehicle; obtaining one or more global factors, the one or more global factors including a global weather condition; obtaining one or more local factors, the one or more local factors including a local road condition, the one or more local factors being captured by one or more sensors disposed on the second object; 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 relatively apply to all of the first objects, and the local factors apply to a portion of the first objects, wherein at least a first global factor has a first degree of influence for the first static object and the first global factor has a second degree of influence for the first dynamic object, the first degree of influence being different from the second degree of influence, the updating including adjusting a rate of decay for at least one of the probability fields of a corresponding probability of the plurality of probability models; and determining a navigation for the second object through at least a part of the environment based at least on minimizing a total collision probability of the second object with the plurality of first objects along the navigation according to the updated probability models; autonomously causing, in response to determining the navigation, the navigation of the second object through at least the part of the environment. ### Claim 2 (depends on 1) The method of claim 1, wherein: the static first objects are substantially stationary with respect to ground; and the dynamic first objects are movable with respect to ground. ### Claim 3 (depends on 1) The method of claim 1, wherein: the global factors further comprise at least one of global traffic condition, season, time, or geographic location of the environment; and the local factors further comprise at least one of: local event, local traffic condition, or one or more of the first objects along the navigation. ### Claim 4 (depends on 1) The method of claim 1, wherein obtaining the local factors further comprises using one or more sensors disposed 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 the first objects along the navigation comprises a summation of probabilities of colliding with the first objects along the navigation. ### Claim 6 (depends on 1) The method of claim 1, wherein determining the navigation for the second object through at least a part of the environment based at least on minimizing the total collision probability with the first objects along the navigation according to the updated probability models comprises: determining the navigation for the second object through at least a part of the environment based at least on (1) minimizing the total collision probability with the first objects along the navigation according to the updated probability models and (2) 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 second object 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 probabilistic navigation system, comprising a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the system to perform a probabilistic navigation method, the method comprising: obtaining a map of an environment comprising a plurality of first objects each associated with a probability model, the environment comprising a metropolitan area, and the plurality of first objects including a first static object of the metropolitan area and a first dynamic object of the metropolitan area, the probability model at least describing probability fields of a second object colliding with each of the plurality of first objects, the probability fields decaying away from one or more physical boundaries of each of the plurality of first objects, the density and shape of each the probability fields being based on one or more physical properties of a corresponding first object of the plurality of first objects, the second object comprising an autonomous vehicle; obtaining one or more global factors, the one or more global factors including a global weather condition; obtaining one or more local factors, the one or more local factors including a local road condition, the one or more local factors being captured by one or more sensors disposed on the second object; 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 relatively apply to all of the first objects, and the local factors apply to a portion of the first objects, wherein at least a first global factor has a first degree of influence for the first static object and the first global factor has a second degree of influence for the first dynamic object, the first degree of influence being different from the second degree of influence, the updating including adjusting a rate of decay for at least one of the probability fields of a corresponding probability of the plurality of probability models; and determining a navigation for the second object through at least a part of the environment based at least on minimizing a total collision probability of the second object with the plurality of first objects along the navigation according to the updated probability models; autonomously causing, in response to determining the navigation, the navigation of the second object through at least the part of the environment. ### Claim 9 (depends on 8) The system of claim 8, wherein: the static first objects are substantially stationary with respect to ground; and the dynamic first objects are movable with respect to ground. ### Claim 10 (depends on 8) The system of claim 8, wherein: the global factors further comprise at least one of global traffic condition, season, time, or geographic location of the environment; and the local factors further comprise at least one of: local event, local traffic condition, or one or more of the first objects along the navigation. ### Claim 11 (depends on 8) The system of claim 8, wherein obtaining the local factors further comprises using one or more sensors disposed 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 the first objects along the navigation comprises a summation of probabilities of colliding with the first objects along the navigation. ### Claim 13 (depends on 8) The system of claim 8, wherein determining the navigation for the second object through at least a part of the environment based at least on minimizing the total collision probability with the first objects along the navigation according to the updated probability models comprises: determining the navigation for the second object through at least a part of the environment based at least on (1) minimizing the total collision probability with the first objects along the navigation according to the updated probability models and (2) 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, wherein the method further comprises: controlling the second object to move according to the determined navigation, wherein the navigation comprises at least one of a route, a moving speed, or a moving direction.