There is provided an automatic correction method for an onboard camera and an onboard camera device. The automatic correction method includes the following steps: obtaining a lane image with the onboard camera and a current extrinsic parameter matrix, and identifying two lane lines in the lane image; converting the lane image into a top-view lane image, and obtaining two projected lane lines in the top-view lane image for the two lane lines; calculating a plurality of correction parameter matrices corresponding to the current extrinsic parameter matrix according to the two projected lane lines; and correcting the current extrinsic parameter matrix according to the plurality of correction parameter matrices. This can be applied in situations where the vehicle is stationary or travelling for automatic correction on the extrinsic parameter matrix of the onboard camera.

There is provided an automatic correction method for an onboard camera and an onboard camera device. The automatic correction method includes the following steps: obtaining a lane image with the onboard camera and a current extrinsic parameter matrix, and identifying two lane lines in the lane image; converting the lane image into a top-view lane image, and obtaining two projected lane lines in the top-view lane image for the two lane lines; calculating a plurality of correction parameter matrices corresponding to the current extrinsic parameter matrix according to the two projected lane lines; and correcting the current extrinsic parameter matrix according to the plurality of correction parameter matrices. This can be applied in situations where the vehicle is stationary or travelling for automatic correction on the extrinsic parameter matrix of the onboard camera.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

A method for determining, by reanalysis, a vibratory environment of a coupled vehicle/passenger system. A vehicle is subjected to external forces Fext and is coupled to a new passenger including multiple payloads (e.g., x=I, . . . N payload(s)). At the level of vehicle/passenger interfaces Ix, the method comprising a step DET1) for determining, based on reference interfacial acceleration γ.sub.x_ref of a reference passenger, the interfacial acceleration γ.sub.x′ relative to the new passenger.

A method for determining, by reanalysis, a vibratory environment of a coupled vehicle/passenger system. A vehicle is subjected to external forces Fext and is coupled to a new passenger including multiple payloads (e.g., x=I, . . . N payload(s)). At the level of vehicle/passenger interfaces Ix, the method comprising a step DET1) for determining, based on reference interfacial acceleration γ.sub.x_ref of a reference passenger, the interfacial acceleration γ.sub.x′ relative to the new passenger.

The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

A computation device includes: a list generation unit that generates a list indicating element values of first elements comprised in a plurality of computational matrices having equal numbers of rows and columns, the element values being indicated for the respective positions of the first elements in the computational matrices; and a computation execution unit that carries out computation based on the element values of the first elements indicated in the list and the element values of second elements comprised in a partial matrix belonging to a computation target matrix and having the same number of rows and columns as the computational matrices.

A computation device includes: a list generation unit that generates a list indicating element values of first elements comprised in a plurality of computational matrices having equal numbers of rows and columns, the element values being indicated for the respective positions of the first elements in the computational matrices; and a computation execution unit that carries out computation based on the element values of the first elements indicated in the list and the element values of second elements comprised in a partial matrix belonging to a computation target matrix and having the same number of rows and columns as the computational matrices.