Autonomous driving control device and autonomous driving control method

An autonomous driving control device comprising:

• a collection unit configured to collect from an autonomous driving vehicle having a driving trajectory planned in advance, driving data including own-state quantities representing a state of the autonomous driving vehicle;
• a generation unit configured to generate a driving trajectory model representing specific values of the own-state quantities in a time series based on the driving data collected by the collection unit;
• a determination unit configured to compare a driving trajectory under evaluation for the autonomous driving vehicle with a reference driving trajectory acquired from the driving trajectory model generated by the generation unit, and determine an abnormality level indicating a degree of deviation between the driving trajectory under evaluation and the reference driving trajectory;
• an inference unit configured to, upon the determination unit determining that the abnormality level indicates presence of an abnormality, use image data, based on which the abnormality level was determined to indicate the presence of an abnormality, and artificial intelligence learned through machine learning using, as data for learning, data sets each including a confirmation of presence or absence of an abnormality and its factor that were received as input from an operator, thereby inferring the presence or absence of an abnormality and its factor;
• and
• an output unit configured to output report data including the presence or absence of an abnormality and its factor inferred by the inference unit.

An autonomous driving control device comprising:

• a collection unit configured to collect from an autonomous driving vehicle having a driving trajectory planned in advance, driving data including own-state quantities representing a state of the autonomous driving vehicle;
• a generation unit configured to generate a driving trajectory model representing specific values of the own-state quantities in a time series based on the driving data collected by the collection unit;
• a determination unit configured to compare a driving trajectory under evaluation for the autonomous driving vehicle with a reference driving trajectory acquired from the driving trajectory model generated by the generation unit, and determine an abnormality level indicating a degree of deviation between the driving trajectory under evaluation and the reference driving trajectory;
• an inference unit configured to, upon the determination unit determining that the abnormality level indicates presence of an abnormality, use image data, based on which the abnormality level was determined to indicate the presence of an abnormality, and a rule base user-defined from data sets each including a confirmation of presence or absence of an abnormality and its factor that were received as input from an operator, thereby inferring the presence or absence of an abnormality and its factor;
• and
• an output unit configured to output report data including the presence or absence of an abnormality and its factor inferred by the inference unit.

An autonomous driving control device comprising:

• a collection unit configured to collect from an autonomous driving vehicle having a driving trajectory planned in advance, driving data including own-state quantities representing a state of the autonomous driving vehicle;
• a generation unit configured to generate a driving trajectory model representing specific values of the own-state quantities in a time series based on the driving data collected by the collection unit;
• a determination unit configured to compare a driving trajectory under evaluation for the autonomous driving vehicle with a reference driving trajectory acquired from the driving trajectory model generated by the generation unit, and determine an abnormality level indicating a degree of deviation between the driving trajectory under evaluation and the reference driving trajectory;
• an inference unit configured to, upon the determination unit determining that the abnormality level indicates presence of an abnormality, present to an operator image data, based on which the abnormality level was determined to indicate the presence of an abnormality, and receive as input from the operator a confirmation of presence or absence of an abnormality and its factor;
• and
• a detection unit configured to, with the factor received as input from the operator as a label, detect a significant abnormality indicated as the factor, using a learner generated by machine learning classification of the driving trajectory under evaluation.

An autonomous driving control device comprising:

• a collection unit configured to collect from an autonomous driving vehicle having a driving trajectory planned in advance, driving data including own-state quantities representing a state of the autonomous driving vehicle;
• a generation unit configured to generate a driving trajectory model representing specific values of the own-state quantities in a time series based on the driving data collected by the collection unit;
• a determination unit configured to compare a driving trajectory under evaluation for the autonomous driving vehicle with a reference driving trajectory acquired from the driving trajectory model generated by the generation unit, and determine an abnormality level indicating a degree of deviation between the driving trajectory under evaluation and the reference driving trajectory;
• an inference unit configured to, upon the determination unit determining that the abnormality level indicates presence of an abnormality, use image data, based on which the abnormality level was determined to indicate the presence of an abnormality, and artificial intelligence learned through machine learning using, as data for learning, data sets each including a confirmation of presence or absence of an abnormality and its factor that were received as input from an operator, thereby inferring the presence or absence of an abnormality and its factor;
• and
• a detection unit configured to, with the factor inferred by the inference unit as a label, detect a significant abnormality indicated as the factor, using a learner generated by machine learning classification of the driving trajectory under evaluation.

An autonomous driving control device comprising:

• a collection unit configured to collect from an autonomous driving vehicle having a driving trajectory planned in advance, driving data including own-state quantities representing a state of the autonomous driving vehicle;
• a generation unit configured to generate a driving trajectory model representing specific values of the own-state quantities in a time series based on the driving data collected by the collection unit;
• a determination unit configured to compare a driving trajectory under evaluation for the autonomous driving vehicle with a reference driving trajectory acquired from the driving trajectory model generated by the generation unit, and determine an abnormality level indicating a degree of deviation between the driving trajectory under evaluation and the reference driving trajectory;
• an inference unit configured to, upon the determination unit determining that the abnormality level indicates presence of an abnormality, use image data, based on which the abnormality level was determined to indicate the presence of an abnormality, and a rule base user-defined from data sets each including a confirmation of presence or absence of an abnormality and its factor that were received as input from an operator, thereby inferring the presence or absence of an abnormality and its factor;
• and
• a detection unit configured to, with the factor inferred by the inference unit as a label, detect a significant abnormality indicated as the factor, using a learner generated by machine learning classification of the driving trajectory under evaluation.

An autonomous driving control method comprising:

• collecting from an autonomous driving vehicle having a driving trajectory planned in advance, driving data including own-state quantities representing a state of the autonomous driving vehicle;
• generating a driving trajectory model representing specific values of the own-state quantities in a time series based on the collected driving data;
• comparing a driving trajectory under evaluation for the autonomous driving vehicle with a reference driving trajectory acquired from the generated driving trajectory model, and determining an abnormality level indicating a degree of deviation between the driving trajectory under evaluation and the reference driving trajectory;
• upon determining that the abnormality level indicates presence of an abnormality, presenting to an operator image data, based on which the abnormality level was determined to indicate the presence of an abnormality, and receiving a confirmation of presence or absence of an abnormality and its factor as input from the operator;
• and
• outputting report data including the confirmation of presence or absence of an abnormality and its factor that are received as input from the operator.

A non-transitory computer readable medium having stored thereon instructions executable by a computer to cause the computer to perform an autonomous driving control method, comprising:

• collecting from an autonomous driving vehicle having a driving trajectory planned in advance, driving data including own-state quantities representing a state of the autonomous driving vehicle;
• generating a driving trajectory model representing specific values of the own-state quantities in a time series based on the collected driving data;
• comparing a driving trajectory under evaluation for the autonomous driving vehicle with a reference driving trajectory acquired from the generated driving trajectory model, and determining an abnormality level indicating a degree of deviation between the driving trajectory under evaluation and the reference driving trajectory;
• upon determining that the abnormality level indicates presence of an abnormality, presenting to an operator image data, based on which the abnormality level was determined to indicate the presence of an abnormality, and receiving a confirmation of presence or absence of an abnormality and its factor as input from the operator;
• and
• outputting report data including the confirmation of presence or absence of an abnormality and its factor that are received as input from the operator.