Abstract: A system for automatically calibrating sensors of a vehicle includes an electronic control unit, a projector communicatively coupled to the electronic control unit, a first sensor communicatively coupled to the electronic control unit, and a second sensor communicatively coupled to the electronic control unit. The electronic control unit is configured to project, with the projector, a calibration pattern onto a surface, capture, with the first sensor, a first portion of the calibration pattern, capture, with the second sensor, a second portion of the calibration pattern, and calibrate the first sensor and the second sensor based on at least one feature sensed within the first portion of the calibration pattern and the second portion of the calibration pattern.

Abstract: An embodiment takes the form of a locker includes a mobility device repository, an electrical system, a kiosk having a user interface, and a communication interface communicatively connected to a network. The mobility device repository is configured to secure one or more personal mobility devices at the mobility device repository. The electrical system is configured to receive electrical power and to charge respective batteries of the personal mobility devices secured at the mobility device repository using the electrical power. The locker receives a personal mobility device at the mobility device repository and secures the received personal mobility device at the mobility device repository, and receives a checked-in indication that the personal mobility device is checked in. The checked-in indication is received via the user interface or via the communication interface over the network. The locker charges a respective battery of the received personal mobility device using the electrical system.

Abstract: Acquiring labeled data can be a significant bottleneck in the development of machine learning models that are accurate and efficient enough to enable safety-critical applications, such as automated driving. The process of labeling of driving logs can be automated. Unlabeled real-world driving logs, which include data captured by one or more vehicle sensors, can be automatically labeled to generate one or more labeled real-world driving logs. The automatic labeling can include analysis-by-synthesis on the unlabeled real-world driving logs to generate simulated driving logs, which can include reconstructed driving scenes or portions thereof. The automatic labeling can further include simulation-to-real automatic labeling on the simulated driving logs and the unlabeled real-world driving logs to generate one or more labeled real-world driving logs. The automatically labeled real-world driving logs can be stored in one or more data stores for subsequent training, validation, evaluation, and/or model management.

Abstract: Autonomous driving includes evaluating information about an environment surrounding a vehicle, generating, based on the evaluation of the information about the environment surrounding the vehicle, a driving plan for performing a driving maneuver, and operating vehicle systems in the vehicle to perform the driving maneuver according to the driving plan. The autonomous driving further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles. Under the driving plan, a driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehiclesAutonomous driving includes identifying a driving behavior of a vehicle based on an evaluation of information about manual operation of the vehicle and information about an environment surrounding the vehicle, and operating vehicle systems in the vehicle to perform a driving maneuver according to a driving plan for performing the driving maneuver.

Abstract: Autonomous driving includes identifying a traffic behavior of an object in an environment surrounding a vehicle based on an evaluation of information about the environment surrounding the vehicle while the vehicle is in the midst of manual operation, and operating vehicle systems in the vehicle to perform a driving maneuver according to a driving plan for performing the driving maneuver. The autonomous driving further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects, and operating the vehicle systems to perform the driving maneuver according to the driving plan in response to identifying that the traffic behavior of the object does not match the predominating traffic behavior of the like population of reference objects. Under the driving plan, the traffic behavior of the object is addressed.

Abstract: Providing user assistance in a vehicle includes identifying a driving behavior of the vehicle based on an evaluation of information about manual operation of the vehicle and information about an environment surrounding the vehicle, and issuing an alert to a user prompting the user to implement corrective manual operation. The user assistance further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and issuing the alert to a user prompting the user to implement corrective manual operation in response to identifying that the driving behavior of the vehicle does not match the predominating driving behavior of the like population of reference vehicles. Under the corrective manual operation, the driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehicles.

Abstract: Providing user assistance in a vehicle includes identifying a traffic behavior of an object in an environment surrounding the vehicle based on an evaluation of information about the environment surrounding the vehicle while the vehicle is in the midst of manual operation, and issuing an alert to a user prompting the user to implement defensive manual operation. The user assistance further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects, and issuing the alert to a user prompting the user to implement defensive manual operation in response to identifying that the traffic behavior of the object does not match the predominating traffic behavior of the like population of reference objects. Under the defensive manual operation, the traffic behavior of the object is addressed.

Abstract: A method of deploying a landing pad, for a vertical takeoff and landing vehicle, relative to a surface of an environment in which the landing pad is disposed, includes stowing an adjustable body of the landing pad in a stowed state. The adjustable body includes a platform surface that in the stowed state defines a first planar surface area relative to the surface, the first planar surface area being inoperable to receive the vertical takeoff and landing vehicle thereon. The method includes deploying the adjustable body from the stowed state to a deployed state. In the deployed state the platform surface defines a second planar surface area greater than the first planar surface area, the second surface planar area of the platform surface is sized and shaped to receive the vertical takeoff and landing vehicle thereon in the deployed state.

Abstract: An embodiment takes the form of a locker includes a mobility device repository, an electrical system, a kiosk having a user interface, and a communication interface communicatively connected to a network. The mobility device repository is configured to secure one or more personal mobility devices at the mobility device repository. The electrical system is configured to receive electrical power and to charge respective batteries of the personal mobility devices secured at the mobility device repository using the electrical power. The locker receives a personal mobility device at the mobility device repository and secures the received personal mobility device at the mobility device repository, and receives a checked-in indication that the personal mobility device is checked in. The checked-in indication is received via the user interface or via the communication interface over the network. The locker charges a respective battery of the received personal mobility device using the electrical system.

Abstract: Systems and methods for automatically re-energizing a vehicle. A vehicle determines a current energy source level in the vehicle's energy storage system. When the current energy source level is less than a threshold energy source level, a forthcoming location of the vehicle where the vehicle is non-operational is identified. Additionally, a window of time where the vehicle is located at the forthcoming location is identified. A mobile energy delivery (MED) vehicle is dispatched to the forthcoming location within the window of time to re-energize the vehicle.

Abstract: A method of deploying a landing pad, for a vertical takeoff and landing vehicle, relative to a surface of an environment in which the landing pad is disposed, includes stowing an adjustable body of the landing pad in a stowed state. The adjustable body includes a platform surface that in the stowed state defines a first planar surface area relative to the surface, the first planar surface area being inoperable to receive the vertical takeoff and landing vehicle thereon. The method includes deploying the adjustable body from the stowed state to a deployed state. In the deployed state the platform surface defines a second planar surface area greater than the first planar surface area, the second surface planar area of the platform surface is sized and shaped to receive the vertical takeoff and landing vehicle thereon in the deployed state.

Abstract: A system for automatically calibrating sensors of a vehicle includes an electronic control unit, a projector communicatively coupled to the electronic control unit, a first sensor communicatively coupled to the electronic control unit, and a second sensor communicatively coupled to the electronic control unit. The electronic control unit is configured to project, with the projector, a calibration pattern onto a surface, capture, with the first sensor, a first portion of the calibration pattern, capture, with the second sensor, a second portion of the calibration pattern, and calibrate the first sensor and the second sensor based on at least one feature sensed within the first portion of the calibration pattern and the second portion of the calibration pattern.

Abstract: This disclosure describes various embodiments for resource optimization in a vehicle. In an embodiment, a system for resource optimization in a vehicle is described. The system may comprise a memory; a processor coupled to the memory; and a resource optimization module. The resource optimization module may be configured to: monitor usage of local computing resources of the vehicle, the local computing resources comprising the processor and available bandwidth of a transmission medium; determine an availability of the local computing resources; evaluate data captured by one or more sensors of the vehicle; and determine whether to process the data locally or remotely based, at least in part, on the availability of the local computing resources and the data captured by the one or more sensors.

Abstract: Acquiring labeled data can be a significant bottleneck in the development of machine learning models that are accurate and efficient enough to enable safety-critical applications, such as automated driving. The process of labeling of driving logs can be automated. Unlabeled real-world driving logs, which include data captured by one or more vehicle sensors, can be automatically labeled to generate one or more labeled real-world driving logs. The automatic labeling can include analysis-by-synthesis on the unlabeled real-world driving logs to generate simulated driving logs, which can include reconstructed driving scenes or portions thereof. The automatic labeling can further include simulation-to-real automatic labeling on the simulated driving logs and the unlabeled real-world driving logs to generate one or more labeled real-world driving logs. The automatically labeled real-world driving logs can be stored in one or more data stores for subsequent training, validation, evaluation, and/or model management.

Abstract: Systems, methods, and other embodiments described herein relate to acquiring embedded information from a roadway. In one embodiment, a method includes, in response to receiving a reflected signal resulting from a scanning signal interacting with the roadway, analyzing the reflected signal to detect a roadway signature embedded within the roadway. The method includes computing an identifier of the roadway signature as a function of features associated with the roadway signature that are embodied within the reflected signal. The method includes providing the embedded information about the roadway according to the identifier.

Abstract: System, methods, and other embodiments described herein relate to localizing a vehicle on a roadway. In one embodiment, a method includes, in response to detecting one or more indicators of a roadway signature within a reflected signal from the roadway, acquiring a fix on the roadway signature as a function of the one or more indicators that identify at least a segment of the roadway signature. The method includes localizing the vehicle on the roadway by correlating the roadway signature with a signature mapping that identifies a location for the segment of the roadway signature on the roadway.

Abstract: Methods and systems for proactively preventing hazardous or other situations in a robot-cloud interaction are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices. The task logs may include information associated with tasks performed by the plurality of robotic devices. The method may also include a computing system determining information associated with hazardous situations based on the information associated with the task logs. For example, the hazardous situations may comprise situations associated with failures of one or more components of the plurality of robotic devices. According to the method, information associated with a contextual situation of a first robotic device may be determined, and when the information associated with the contextual situation is consistent with information associated with the one or more hazardous situations, an alert indicating a potential failure of the first robotic device may be provided.

Abstract: Systems and methods for automatically re-energizing a vehicle. A vehicle determines a current energy source level in the vehicle's energy storage system. When the current energy source level is less than a threshold energy source level, a forthcoming location of the vehicle where the vehicle is non-operational is identified. Additionally, a window of time where the vehicle is located at the forthcoming location is identified. A mobile energy delivery (MED) vehicle is dispatched to the forthcoming location within the window of time to re-energize the vehicle.

Abstract: Methods and systems for allocating tasks to robotic devices are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices and in a computing system configured to access a processor and memory, determining information associated with a health level for the plurality of robotic devices based on the information associated with the task logs. A health level for a given robotic device may be proportional to a current level of ability to perform a function, which may change over a lifespan of the given robotic device. Information associated with a plurality of tasks to be performed by one or more or the robotic devices may also be determined. The computing system may optimize an allocation of the plurality of tasks such that a high precision task may be allocated to a robotic device having a greater current health level than another robotic device.

Abstract: Autonomous driving includes evaluating information about an environment surrounding a vehicle, generating, based on the evaluation of the information about the environment surrounding the vehicle, a driving plan for performing a driving maneuver, and operating vehicle systems in the vehicle to perform the driving maneuver according to the driving plan. The autonomous driving further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles. Under the driving plan, a driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehicles.