A vehicle can capture data that can be converted into a synthetic scenario for use in a simulator. Objects can be identified in the data and attributes associated with the objects can be determined. The data can be used to generate a synthetic scenario of a simulated environment. The scenarios can include simulated objects that traverse the simulated environment and perform actions based on the attributes associated with the objects, the captured data, and/or interactions within the simulated environment. In some instances, the simulated objects can be filtered from the scenario based on attributes associated with the simulated objects and can be instantiated and/or destroyed based on triggers within the simulated environment. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within the simulated environment.

A vehicle can capture data that can be converted into a synthetic scenario for use in a simulator. Objects can be identified in the data and attributes associated with the objects can be determined. The data can be used to generate a synthetic scenario of a simulated environment. The scenarios can include simulated objects that traverse the simulated environment and perform actions based on the attributes associated with the objects, the captured data, and/or interactions within the simulated environment. In some instances, the simulated objects can be filtered from the scenario based on attributes associated with the simulated objects and can be instantiated and/or destroyed based on triggers within the simulated environment. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within the simulated environment.

Described herein are techniques for optimizing charging and/or replacement of battery packs within a fleet of electric vehicles. In some embodiments, such techniques may include receiving information indicating a current status of one or more electric vehicles in a fleet of electric vehicles, identifying schedule data for the one or more electric vehicles, and determining, based on the schedule data and the current status of the one or more vehicles, a charging schedule for the fleet of electric vehicles. The techniques may further include correlating one or more charging plates to the one or more electric vehicles and directing power to the one or more charging plates in accordance with the determined charging schedule.

Described herein are techniques for optimizing charging and/or replacement of battery packs within a fleet of electric vehicles. In some embodiments, such techniques may include receiving information indicating a current status of one or more electric vehicles in a fleet of electric vehicles, identifying schedule data for the one or more electric vehicles, and determining, based on the schedule data and the current status of the one or more vehicles, a charging schedule for the fleet of electric vehicles. The techniques may further include correlating one or more charging plates to the one or more electric vehicles and directing power to the one or more charging plates in accordance with the determined charging schedule.

A method includes receiving first driving data associated with a first vehicle, receiving second driving data associated with one or more vehicles around the first vehicle, creating training data by labeling the first driving data as positive data and treating the second driving data as unlabeled, and using the training data to train a classifier to predict whether driving data input to the classifier is positive or unlabeled.

A method includes receiving first driving data associated with a first vehicle, receiving second driving data associated with one or more vehicles around the first vehicle, creating training data by labeling the first driving data as positive data and treating the second driving data as unlabeled, and using the training data to train a classifier to predict whether driving data input to the classifier is positive or unlabeled.

Disclosed herein are system, method, and computer program product embodiments for assigning a lane relationship between an autonomous vehicle and other actors near an intersection. For example, the method includes executing a simulation scenario that includes features of a scene through which a vehicle may travel, the simulation scenario including one or more actors. The method further includes identifying an intersection between a first road and a second road in the simulation scenario, wherein the intersection is in a planned path of the vehicle. In response to one of the actors occupying a lane of either the first road or the second road, the method includes classifying the interaction between the vehicle and the actor based on the intersection, the path of the vehicle, and the lane occupied by the actor.

Disclosed herein are system, method, and computer program product embodiments for assigning a lane relationship between an autonomous vehicle and other actors near an intersection. For example, the method includes executing a simulation scenario that includes features of a scene through which a vehicle may travel, the simulation scenario including one or more actors. The method further includes identifying an intersection between a first road and a second road in the simulation scenario, wherein the intersection is in a planned path of the vehicle. In response to one of the actors occupying a lane of either the first road or the second road, the method includes classifying the interaction between the vehicle and the actor based on the intersection, the path of the vehicle, and the lane occupied by the actor.

Systems and methods of unsafe driving detection are provided which share partial unsafe driving behavior analyses with others in order to ensure that unsafe driving behavior is detected as early as possible. For example, in response to an event which interrupts a first detecting vehicle from collecting additional driving behavior data associated with a subject vehicle, the first detecting vehicle may transfer driving behavior data it has already collected and processed, to another detecting entity (e.g., a second detecting vehicle) in observable range of the subject vehicle.

Systems and methods of unsafe driving detection are provided which share partial unsafe driving behavior analyses with others in order to ensure that unsafe driving behavior is detected as early as possible. For example, in response to an event which interrupts a first detecting vehicle from collecting additional driving behavior data associated with a subject vehicle, the first detecting vehicle may transfer driving behavior data it has already collected and processed, to another detecting entity (e.g., a second detecting vehicle) in observable range of the subject vehicle.