| CPC B60W 60/0011 (2020.02) [B60W 30/09 (2013.01); B60W 30/0956 (2013.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/588 (2022.01); G06V 20/597 (2022.01); G06V 40/19 (2022.01); B60W 2554/4049 (2020.02)] | 9 Claims |
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1. A vehicle control method comprising:
recognizing an object which is located near the vehicle;
calculating an area of risk which is distributed around the recognized object;
generating a target trajectory for the vehicle to travel along on the basis of the area of risk;
controlling at least one of a speed or steering of the vehicle on the basis of the generated target trajectory;
selecting one or a plurality of models from a plurality of models that output the target trajectory in a case where the area of risk is input, input the area of risk to the selected model, and generate the target trajectory on the basis of an output result of the model to which the area of risk is input; and
changing a size of a range of the area of risk to a different size in accordance with a type of the selected model among the plurality of models,
wherein the plurality of models include at least one first model which is rule-based or model-based and at least one second model which is machine-learning-based, and
wherein the range of the area of risk is set to a first range in a case where the model selected is the first model, the range of the area of risk is set to a second range smaller than the first range in a case where the model selected is the second model, and calculating a potential of the risk within the set range.
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2. A vehicle control device comprising:
a processor configured to:
recognize an object which is located near a vehicle;
calculate an area of risk which is distributed around the object;
generate a target trajectory for the vehicle to travel along on the basis of the area of risk;
automatically control at least one of a speed or steering of the vehicle on the basis of the target trajectory;
select one or a plurality of models from a plurality of models that output the target trajectory in a case where the area of risk is input, input the area of risk to the selected model, and generate the target trajectory on the basis of an output result of the model to which the area of risk is input; and
change a size of a range of the area of risk to a different size in accordance with a type of model among the plurality of models,
wherein the plurality of models include at least one first model which is rule-based or model-based and at least one second model which is machine-learning-based, and
wherein, the processor is further configured to set the range of the area of risk to a first range in a case where the model selected is the first model, set the range of the area of risk to a second range smaller than the first range in a case where the model selected is the second model, and calculate a potential of the risk within the set range.
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9. A computer readable non-transitory storage medium having a program stored therein, the program causing a computer mounted in a vehicle to execute:
recognizing an object which is located near the vehicle;
calculating an area of risk which is distributed around the recognized object;
generating a target trajectory for the vehicle to travel along on the basis of the area of risk;
controlling at least one of a speed or steering of the vehicle on the basis of the generated target trajectory;
selecting one or a plurality of models from a plurality of models that output the target trajectory in a case where the area of risk is input, inputting the area of risk to the selected model, and generating the target trajectory on the basis of an output result of the model to which the area of risk is input; and
changing a size of a range of the area of risk to a different size in accordance with a type of the selected model among the plurality of models,
wherein the plurality of models include at least one first model which is rule-based or model-based and at least one second model which is machine-learning-based, and
wherein the range of the area of risk is set to a first range in a case where the model selected is the first model, the range of the area of risk is set to a second range smaller than the first range in a case where the model selected is the second model, and calculating a potential of the risk within the set range.
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