Simulation system and methods for autonomous vehicles

Abstract

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. More specifically, systems, devices, and methods are configured to simulate navigation of autonomous vehicles in various simulated environments. In particular, a method may include receiving data representing characteristics of a dynamic object, calculating a classification of a dynamic object to identify a classified dynamic object, identifying data representing dynamic-related characteristics associated with the classified dynamic object, forming a data model of the classified dynamic object, simulating a predicted range of motion of the classified dynamic object in a simulated environment to form a simulated dynamic object, and simulating a predicted response of a data representation of a simulated autonomous vehicle.

1 . (canceled) 
     
     
         2 . A method, comprising:
 receiving sensor data from a sensor associated with a vehicle representing an object in an environment;   determining, based at least in part on the sensor data, a classification associated with the object;   determining at least one of a heading, velocity, or acceleration of the object;   generating a data model associated with the object based at least in part on at least one of the sensor data, the classification, or at least one of the heading, the velocity, or the acceleration;   generating, based at least in part on the data model and the at least one of the heading, velocity, or acceleration of the object, a simulation;   determining, based at least in part on the simulation, a probability associated with at least one of a velocity, acceleration, position, or action of the object in a simulated environment; and   controlling, based at least in part on the probability, the vehicle.   
     
     
         3 . The method of  claim 2 , wherein the data model comprises one or more of one or more of a dynamic object data modeler, an environment modeler, a sensor modeler, or a vehicle modeler. 
     
     
         4 . The method of  claim 2 , wherein determining the probability is further based at least in part on a second probability determined based at least in part on simulating an additional object, simulating the additional object being based at least in part on an additional data model associated with the additional object. 
     
     
         5 . The method of  claim 2 , wherein the probability indicates a likelihood that the object will transition from a static state to a dynamic state or interfere with a planned path of the vehicle. 
     
     
         6 . The method of  claim 2 , further comprising receiving an indication of an event in an environment surrounding the vehicle,
 wherein generating the simulation is further based on the event.   
     
     
         7 . The method of  claim 2 , further comprising simulating a maneuver of the vehicle based at least in part on the probability, and wherein generating the command is further based at least in part on simulating the maneuver. 
     
     
         8 . A system comprising:
 one or more processors;   memory storing processor-executable instructions that, when executed by the one or more processors cause the system to perform operations comprising:
 receiving, from a sensor associated with a vehicle, sensor data representing an object in an environment a vehicle; 
 determining, based at least in part on the sensor data, a classification associated with the object; 
 determining at least one of a heading, velocity, or acceleration of the object; 
 generating a data model associated with the object based at least in part on at least one of the sensor data, the classification, or at least one of the heading, the velocity, or the acceleration; 
 generating, based at least in part on the data model and the at least one of the heading, velocity, or acceleration of the object, a simulation; 
 determining, based at least in part on the simulation, a probability associated with at least one of a velocity, acceleration, position, or action of the object in a simulated environment; and 
 controlling, based at least in part on the probability, the vehicle. 
   
     
     
         9 . The system of  claim 8 , wherein the data model comprises one or more of one or more of a dynamic object data modeler, an environment modeler, a sensor modeler, or a vehicle modeler. 
     
     
         10 . The system of  claim 8 , wherein determining the probability is further based at least in part on a second probability determined based at least in part on simulating an additional object, simulating the additional object being based at least in part on an additional data model associated with the additional object. 
     
     
         11 . The system of  claim 8 , wherein the probability indicates a likelihood that the object will transition from a static state to a dynamic state. 
     
     
         12 . The system of  claim 8 , wherein the operations further comprise receiving an indication of an event in an environment surrounding the vehicle,
 wherein generating the simulation is further based on the event.   
     
     
         13 . The system of  claim 8 , wherein the operations further comprise simulating a maneuver of the vehicle based at least in part on the probability, and wherein generating the command is further based at least in part on simulating the maneuver. 
     
     
         14 . The system of  claim 13 , wherein simulating the maneuver is based at least in part on:
 generating one or more simulated candidate trajectories based at least in part on the probability; and
 instantiating the one or more simulated candidate trajectories in the simulation, and 
   wherein generating the command is based at least in part on determining that at least one of the simulated candidate trajectories is collision-free operation.   
     
     
         15 . One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors cause the one or more processors to perform operations comprising:
 receiving, from a sensor associated with a vehicle, sensor data representing an object in an environment surrounding a vehicle;   determining, by a vehicle controller and based at least in part on the sensor data, a classification associated with the object;   determining at least one of a heading, velocity, or acceleration of the object;   generating a data model associated with the object based at least in part on at least one of the sensor data, the classification, or at least one of the heading, the velocity, or the acceleration;   generating, based at least in part on the data model and the at least one of the heading, velocity, or acceleration of the object, a simulation;   determining, based at least in part on the simulation, a probability associated with at least one of a velocity, acceleration, position, or action of the object in a simulated environment; and   generating, based at least in part on the probability, a command for controlling the vehicle.   
     
     
         16 . The one or more non-transitory computer-readable media of  claim 15 , wherein the data model comprises one or more of one or more of a dynamic object data modeler, an environment modeler, a sensor modeler, or a vehicle modeler. 
     
     
         17 . The one or more non-transitory computer-readable media of  claim 15 , wherein determining the probability is further based at least in part on a second probability determined based at least in part on simulating an additional object, simulating the additional object being based at least in part on an additional data model associated with the additional object. 
     
     
         18 . The one or more non-transitory computer-readable media of  claim 15 , wherein the probability indicates a likelihood that the object will transition from a static state to a dynamic state. 
     
     
         19 . The one or more non-transitory computer-readable media of  claim 15 , wherein the operations further comprise receiving an indication of an event in an environment surrounding the vehicle,
 wherein generating the simulation is further based on the event.   
     
     
         20 . The one or more non-transitory computer-readable media of  claim 15 , wherein the operations further comprise simulating a maneuver of the vehicle based at least in part on the probability, and wherein generating the command is further based at least in part on simulating the maneuver. 
     
     
         21 . The one or more non-transitory computer-readable media of  claim 15 , wherein simulating the maneuver is based at least in part on:
 generating one or more simulated candidate trajectories based at least in part on the probability; and
 instantiating the one or more simulated candidate trajectories in the simulation, and 
   wherein generating the command is based at least in part on determining that at least one of the simulated candidate trajectories is collision-free operation.