Automated vehicle artificial intelligence training based on simulations
AssigneeMSC.Software Corporation
InventorEdward Schwalb
Life of this patent
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Abstract
Examples described herein relate to apparatuses and methods for or simulating and improving performance of an artificial intelligence (AI) driver, including but not limited to generating sensor data corresponding to a virtual environment, generating a pixelated image corresponding to the virtual environment based on the sensor data, determining actuator commands responsive to pixels in the pixelated image, wherein the decision module determines the actuator commands based on the AI driver, and simulating behaviors of the ego vehicle object using the actuator commands.
Claims30
A simulation system for training an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV), comprising:
- a memory;
- and
- a processing circuit, wherein the processing circuit is configured to implement a controlled system corresponding to an ego vehicle object associated with the AV, the controlled system comprises, a sensor module, a perception module, a decision module, and an actuator module, wherein:
- the sensor module generates sensor data corresponding to a virtual environment;
- the perception module generates a pixelated image corresponding to the virtual environment based on the sensor data;
- the decision module determines actuator commands responsive to pixels in the pixelated image, wherein the decision module determines the actuator commands based on the AI driver;
- and
- the actuator module simulates behaviors of the ego vehicle object using the actuator commands, wherein the pixelated image comprises a pixelated graphical depiction of metrics, the metrics comprise at least one of a safety quotient, a path tracking parameter, a comfort parameter, a fuel economy parameter, and a duration of drive parameter.
A simulation system for training an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV), comprising:
- a memory;
- and
- a processing circuit, wherein the processing circuit is configured to implement a controlled system corresponding to an ego vehicle object associated with the AV, the controlled system comprises, a sensor module, a perception module, a decision module, an actuator module, a dynamic simulation module, an environment simulation module, a measurement module, and a modification module, wherein:
- the sensor module generates sensor data corresponding to a virtual environment;
- the perception module generates a pixelated image corresponding to the virtual environment based on the sensor data;
- the decision module determines actuator commands responsive to pixels in the pixelated image, wherein the decision module determines the actuator commands based on the AI driver;
- the actuator module simulates behaviors of the ego vehicle object using the actuator commands
- the dynamic simulation module determines a first predicted consequence parameter indicative of consequences of the actuator commands on the ego vehicle object;
- the environment simulation module determines a second predicted consequence parameter indicative of consequences of the actuator commands on the virtual environment;
- the measurement module determines an overall predicted consequence parameter based on the first predicted consequence parameter and the second predicted consequence parameter;
- and
- the modification module modifies the AI driver to improve the overall predicted consequence parameter.
A method for training an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV), comprising:
- generating sensor data corresponding to a virtual environment;
- generating a pixelated image corresponding to the virtual environment based on the sensor data;
- determining actuator commands responsive to pixels in the pixelated image, wherein the actuator commands are determined based on the AI driver;
- and
- simulating behaviors of the ego vehicle object using the actuator commands, wherein the pixelated image comprises a pixelated graphical depiction of metrics, the metrics comprise at least one of a safety quotient, a path tracking parameter, a comfort parameter, a fuel economy parameter, and a duration of drive parameter.
A non-transitory computer-readable medium storing processor-readable instructions such that, when executed, causes a processor to train an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV) by:
- generating sensor data corresponding to a virtual environment;
- generating a pixelated image corresponding to the virtual environment based on the sensor data;
- determining actuator commands responsive to pixels in the pixelated image, wherein the actuator commands are determined based on the AI driver;
- and
- simulating behaviors of the ego vehicle object using the actuator commands, wherein the pixelated image comprises a pixelated graphical depiction of metrics, the metrics comprise at least one of a safety quotient, a path tracking parameter, a comfort parameter, a fuel economy parameter, and a duration of drive parameter.
A method for improving an AI driver in a simulated virtual environment, the method comprising:
- generating pixelated images using sensor data corresponding to a virtual environment;
- storing result parameters of simulations involving the AI driver managing an ego vehicle object, wherein the result parameters are generated based on the pixelated images;
- organizing the result parameters into inputs and outputs;
- deriving a simplified model that reproduces the outputs based on the inputs;
- and
- applying the simplified model to perform subsequent simulations.
A simulation system for training an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV), comprising:
- a memory;
- and
- a processing circuit, wherein the processing circuit is configured to:
- generate pixelated images using sensor data corresponding to a virtual environment;
- store result parameters of simulations involving the AI driver managing an ego vehicle object, wherein the result parameters are generated based on the pixelated images;
- organize the result parameters into inputs and outputs;
- derive a simplified model that reproduces the outputs based on the inputs;
- and
- apply the simplified model to perform subsequent simulations.
A non-transitory computer-readable medium storing processor-readable instructions such that, when executed, causes a processor to train an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV) by:
- generating pixelated images using sensor data corresponding to a virtual environment;
- storing result parameters of simulations involving the AI driver managing an ego vehicle object, wherein the result parameters are generated based on the pixelated images;
- organizing the result parameters into inputs and outputs;
- deriving a simplified model that reproduces the outputs based on the inputs;
- and
- applying the simplified model to perform subsequent simulations.
A method for training an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV), comprising:
- generating, by a sensor module, sensor data representing a virtual environment;
- generating, by a perception module, a pixelated image corresponding to the virtual environment based on the sensor data;
- determining, by a candidate actuator command sequence generator, a plurality of candidate actuator command sequences based on the pixelated image, wherein each of the plurality of candidate actuator command sequences controls an ego vehicle object;
- predicting consequences associated with each of the plurality of candidate actuator command sequences through virtual simulations;
- and
- selecting an actuator command sequence from the plurality of candidate actuator command sequences based on the consequences associated with each of the plurality of candidate actuator command sequences.
A simulation system for training an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV), comprising:
- a memory;
- and
- a processing circuit, wherein the processing circuit is configured to:
- generate sensor data representing a virtual environment;
- generate a pixelated image corresponding to the virtual environment based on the sensor data;
- determine a plurality of candidate actuator command sequences based on the pixelated image, wherein each of the plurality of candidate actuator command sequences controls an ego vehicle object;
- predict consequences associated with each of the plurality of candidate actuator command sequences through virtual simulations;
- and
- select an actuator command sequence from the plurality of candidate actuator command sequences based on the consequences associated with each of the plurality of candidate actuator command sequences.
A non-transitory computer-readable medium storing processor-readable instructions such that, when executed, causes a processor to train an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV) by:
- generating sensor data representing a virtual environment;
- generating a pixelated image corresponding to the virtual environment based on the sensor data;
- determining a plurality of candidate actuator command sequences based on the pixelated image, wherein each of the plurality of candidate actuator command sequences controls an ego vehicle object;
- predicting consequences associated with each of the plurality of candidate actuator command sequences through virtual simulations;
- and
- selecting an actuator command sequence from the plurality of candidate actuator command sequences based on the consequences associated with each of the plurality of candidate actuator command sequences.
A method for training an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV), comprising:
- generating sensor data corresponding to a virtual environment;
- generating a pixelated image corresponding to the virtual environment based on the sensor data;
- determining actuator commands responsive to pixels in the pixelated image, wherein the actuator commands are determined based on the AI driver;
- simulating behaviors of the ego vehicle object using the actuator commands determining a first predicted consequence parameter indicative of consequences of the actuator commands on the ego vehicle object;
- determining a second predicted consequence parameter indicative of consequences of the actuator commands on the virtual environment;
- determining an overall predicted consequence parameter based on the first predicted consequence parameter and the second predicted consequence parameter;
- and
- modifying the AI driver to improve the overall predicted consequence parameter.
A non-transitory computer-readable medium storing processor-readable instructions such that, when executed, causes a processor to train an Artificial Intelligence (AI) driver configured to operate an Automated Vehicle (AV) by:
- generating sensor data corresponding to a virtual environment;
- generating a pixelated image corresponding to the virtual environment based on the sensor data;
- determining actuator commands responsive to pixels in the pixelated image, wherein the actuator commands are determined based on the AI driver;
- simulating behaviors of the ego vehicle object using the actuator commands determining a first predicted consequence parameter indicative of consequences of the actuator commands on the ego vehicle object;
- determining a second predicted consequence parameter indicative of consequences of the actuator commands on the virtual environment;
- determining an overall predicted consequence parameter based on the first predicted consequence parameter and the second predicted consequence parameter;
- and
- modifying the AI driver to improve the overall predicted consequence parameter.
Dependencies are inferred from the claim text.