# US 2020/0401892 A1 · Decision Making 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 2020/0401892 A1 - Document ID: US20200401892A1 - Status: Published application - Kind: A1 - Application number: 17/013,422 - Filed: 2020-09-04 - Published: 2020-12-24 - Earliest claimed: 2016-09-23 - Assignees: Apple Inc. - Applicants: Apple Inc. - Inventors: Joshua D. Redding; Luke B. Johnson; Martin Levihn; Nicolas F. Meuleau; Sebastian Brechtel - Web page: https://patentgrep.com/patent/US20200401892A1 ## CPC - G06N 3/09: Supervised learning - B60W 30/00: Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units - B60W 60/001: {Planning or execution of driving tasks} - G05B 13/027: {using neural networks only} - G06F 16/9027: {Trees} - G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] - G06N 3/02: Neural networks - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/092: Reinforcement learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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 - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A behavior planner for a vehicle generates a plurality of conditional action sequences of the vehicle using a tree search algorithm and heuristics obtained from one or more machine learning models. Each sequence corresponds to a sequence of anticipated states of the vehicle. At least some of the action sequences are provided to a motion selector of the vehicle. The motion selector generates motion-control directives based on the received conditional action sequences and on data received from one or more sensors of the vehicle, and transmits the directives to control subsystems of the vehicle. ## Claims ### Claim at position 1 -20. (canceled) ### Claim 21 A method, comprising: obtaining, at a first rate at a motion selector of a vehicle, one or more conditional action sequences generated with respect to the vehicle, wherein individual ones of the conditional action sequences correspond to a respective set of anticipated states of the vehicle; identifying one or more motion-control directives at the motion selector, based at least in part on (a) the one or more conditional action sequences and (b) a set of sensor data obtained at a second rate that exceeds the first rate; and transmitting, by the motion selector, the one or more motion-control directives to one or more motion control subsystems of the vehicle. ### Claim 22 (depends on 21) The method as recited in claim 21, further comprising: generating at least one conditional action sequence of the one or more conditional action sequences using at least a Monte Carlo Tree Search algorithm. ### Claim 23 (depends on 21) The method as recited in claim 21, further comprising: generating at least one conditional action sequence of the one or more conditional action sequences using at least one machine learning model. ### Claim 24 (depends on 21) The method as recited in claim 21, further comprising: generating at least one conditional action sequence of the one or more conditional action sequences using at least an analysis of recorded driver behavior. ### Claim 25 (depends on 21) The method as recited in claim 21, further comprising: generating at least one conditional action sequence of the one or more conditional action sequences based at least in part on an anticipated social interaction. ### Claim 26 (depends on 21) The method as recited in claim 21, further comprising: generating at least one conditional action sequence of the one or more conditional action sequences based at least in part on statistical analysis of a set of actions taken under a particular set of driving conditions. ### Claim 27 (depends on 21) The method as recited in claim 21, wherein identifying the one or more action control directives comprises identifying at least one action control directive based at least in part on an occupant comfort consideration. ### Claim 28 A system, comprising: a motion selector of a vehicle implemented at one or more computing devices; wherein the motion selector is configured to: obtain, at a first rate, one or more conditional action sequences generated with respect to the vehicle, wherein individual ones of the conditional action sequences correspond to a respective set of anticipated states of the vehicle; identify one or more motion-control directives, based at least in part on (a) the one or more conditional action sequences and (b) a set of sensor data obtained at a second rate that exceeds the first rate; and transmit the one or more motion-control directives to one or more motion control subsystems of the vehicle. ### Claim 29 (depends on 28) The system as recited in claim 28, wherein the motion selector is configured to transmit the one or more motion control directives to the one or more motion control subsystems at a rate which differs from the first rate. ### Claim 30 (depends on 28) The system as recited in claim 28, wherein at least one motion-control directive of the one or more motion-control directives is identified based at least in part on an emergency response rule. ### Claim 31 (depends on 28) The system as recited in claim 28, wherein the one or more motion control subsystems comprise one or more of: (a) a braking system, (b) an acceleration system or (c) a turn controller. ### Claim 32 (depends on 28) The system as recited in claim 28, wherein the motion selector is further configured to: prior to identifying the one or more motion control directives, determine one or more non-drivable regions with respect to the vehicle; and generate a trajectory for the vehicle based at least in part on analysis of the one or more non-drivable regions. ### Claim 33 (depends on 28) The system as recited in claim 28, wherein at least conditional action sequence of the one or more conditional action sequence is obtained at the motion selector from a data center. ### Claim 34 (depends on 28) The system as recited in claim 28, wherein at least one sensor of the one or more sensors comprises one or more of: (a) an externally-oriented camera, (b) an occupant-oriented sensor, (c) a physiological signal detector, (d) a Global Positioning System (GPS) device, (e) a radar devices or (f) a LIDAR device. ### Claim 35 One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors implements a motion selector of a vehicle, wherein the motion selector is configured to: obtain, at a first rate, one or more conditional action sequences generated with respect to the vehicle, wherein individual ones of the conditional action sequences correspond to a respective set of anticipated states of the vehicle; identify one or more motion-control directives, based at least in part on (a) the one or more conditional action sequences and (b) a set of sensor data obtained at a second rate that exceeds the first rate; and transmit the one or more motion-control directives to one or more motion control subsystems of the vehicle. ### Claim 36 (depends on 35) The one or more non-transitory computer-accessible storage media as recited in claim 35, wherein the motion selector is configured to transmit the one or more motion control directives to the one or more motion control subsystems at a rate which differs from the first rate. ### Claim 37 (depends on 35) The one or more non-transitory computer-accessible storage media as recited in claim 35, wherein at least one motion-control directive of the one or more motion-control directives is identified based at least in part on an emergency response rule. ### Claim 38 (depends on 35) The one or more non-transitory computer-accessible storage media as recited in claim 35, wherein the one or more motion control subsystems comprise one or more of: (a) a braking system, (b) an acceleration system or (c) a turn controller. ### Claim 39 (depends on 35) The one or more non-transitory computer-accessible storage media as recited in claim 35, wherein the motion selector is further configured to: generate a modified version of a first trajectory based on one or more occupant comfort considerations, wherein the one or more motion-control directives are identified based at least in part on the modified version of the first trajectory. ### Claim 40 (depends on 35) The one or more non-transitory computer-accessible storage media as recited in claim 35, wherein at least conditional action sequence of the one or more conditional action sequence is obtained at the motion selector from a data center.