Abstract: A radar system for an automated vehicle includes a digital-map, a radar, and a controller. The digital-map indicates a characteristic of a roadway traveled by a host-vehicle. The radar detects objects proximate to the host-vehicle. The radar is equipped with a range-setting that is selectively variable. The controller is in communication with the digital-map and the radar. The controller is configured to select the range-setting of the radar based on the characteristic of the roadway. The characteristic may be based on speed-limit, road-shape (e.g. curve-radius), a horizon-distance, and/or an obstruction (e.g. hill, sign, or building). The radar may be equipped with a frame-rate-setting (i.e. pulse repetition frequency or PRF) that is selectively variable, and the controller may be further configured to select the frame-rate-setting based on the characteristic of the roadway.

Abstract: A safe-to-proceed system (10) for operating an automated vehicle proximate to an intersection (14) includes an intersection-detector (18), a vehicle-detector (20), and a controller (24). The intersection-detector (18) is suitable for use on a host-vehicle (12). The intersection-detector (18) is used to determine when a host-vehicle (12) is proximate to an intersection (14). The vehicle-detector (20) is also suitable for use on the host-vehicle (12). The vehicle-detector (20) is used to estimate a stopping-distance (22) of an other-vehicle (16) approaching the intersection (14). The controller (24) is in communication with the intersection-detector (18) and the vehicle-detector (20). The controller (24) is configured to prevent the host-vehicle (12) from entering the intersection (14) when the stopping-distance (22) indicates that the other-vehicle (16) will enter the intersection (14) before stopping.

Abstract: Provided are systems, methods, and computer-readable medium for identifying security risks in applications executing in a cloud environment. In various implementations, a security monitoring and management system can obtain application data from a service provider system. The application data can include a record of actions performed by an application during use of the application by users associated with a tenant. The application executes in a service platform provided for the tenant by the service provider system. In various implementations, the application data is analyzed to identify an event associated with a security risk, where the event is identified from one or more actions performed by the application. The system can determine an action to perform in response to identifying the event. In various examples, an agent executing on the service platform can add instrumentation codes used by the application, where the instrumentation provides the application data.

Abstract: Systems and methods are described for performing Layered Belief LDPC decoding on received Standard Belief LDPC encoded data bursts. In on implementation, a receiver: demodulates a signal, the demodulated signal including a noise corrupted signal derived from a codeword encoded using standard belief LDPC encoding; converts the noise corrupted signal derived from the standard belief LDPC encoded codeword to a noise corrupted signal derived from a layered belief LDPC encoded codeword; and decodes the noise corrupted signal derived from the layered belief LDPC encoded codeword using a layered belief LDPC decoder. In further implementations, systems are described for reducing collisions in Layered Belief LDPC decoders that occur when multiple parity checks need the same soft decision at the same time. In these implementations, elements in an original LBD decoder table are rearranged to increase the distance between elements specifying the same location in a RAM where soft decisions are stored.

Abstract: The subject matter described in this specification is directed to systems and methods for dissipating heat from electronic components supporting autonomous vehicle systems. In particular, the specification describes how the electronic components can be positioned in a duct through which conditioned cabin air can be drawn to convectively dissipate heat from the electronic components.

Abstract: Provided are systems, methods, and computer-readable medium for a simulation platform that can generate simulated activity data for testing a security monitoring and control system. In various examples, the simulation platform can parse the activity data from a cloud service to generate a template, where each entry in the template describes an action and the fields associated with the action. The simulation platform can further generate a configuration that describes a test scenario. The simulation platform can use the configuration and the template to generate the particular action, including randomizing some or all of the fields of the action. When input into the security monitoring and control system, the system can operate on the simulated activity data in the same way as when the system ingests live activity data.

Abstract: Provided are systems, methods, and computer-readable medium for a simulation platform that can generate simulated activity data for testing a security monitoring and control system. In various examples, the simulation platform can parse the activity data from a cloud service to determine the fields associated with each action in the activity data. The simulation platform can then generate a template, where each entry in the template describes an action and the fields associated with the action. The simulation platform can further generate a configuration that describes a test scenario. The simulation platform can use the configuration and the template to generate the particular action, including randomizing some or all of the fields of the action. When input into the security monitoring and control system, the system can operate on the simulated activity data in the same way as when the system ingests live activity data.

Abstract: An operation-security system for an automated vehicle includes an object-detector and a controller. The object-detector includes at least three sensors. Each sensor is one of a camera used to determine an image-location of an object proximate to a host-vehicle, a lidar-unit used to determine a lidar-location of the object proximate to the host-vehicle, and a radar-unit used to determine a radar-location of the object proximate to the host-vehicle. The controller is in communication with the at least three sensors. The controller is configured to determine a composite-location based on a comparison of locations indicated by the at least three sensors. Information from one sensor is ignored when a respective location indicated by the one sensor differs from the composite-location by greater than an error-threshold. If a remote sensor not on the host-vehicle is used, V2V or V2I communications may be used to communicate a location to the host-vehicle.

Abstract: Techniques are disclosed for transporting objects using autonomous vehicles. In an embodiment, a customer makes a reservation with a transportation service provider (e.g., via an online booking application). The customer provides a pick-up date/pick-up time window, a destination and a description of the object(s) (e.g., luggage/cargo) that the customer will be transporting with or without themselves in the autonomous vehicle. After the computer system identifies the physical characteristics of the object(s), it identifies an autonomous vehicle with the appropriate amount of storage/container space with respect to the identified physical characteristics, and then assigns the vehicle to the customer. On or around the pick-up day, the computer system configures the assigned autonomous vehicle with reservation details including a description of the objects.

Abstract: An infrastructure-device status-verification system suitable for use by an automated vehicle includes a transceiver, an object-detector, and a controller. The transceiver is suitable to install on a host-vehicle. The transceiver is used to receive an indicated-status of an infrastructure-device. The object-detector is suitable to install on the host-vehicle. The object-detector is used to determine a detected-status of the infrastructure-device. The controller is in communication with the transceiver and the object-detector. The controller determines a confirmed-status of the infrastructure-device based on the indicated-status and the detected-status. The system provides for increased confidence and security regarding information about the status of an infrastructure-device such as the traffic-signal (e.g.

Abstract: An operation-security system for an automated vehicle includes an object-detector and a controller. The object-detector includes at least three sensors. Each sensor is one of a camera used to determine an image-location of an object proximate to a host-vehicle, a lidar-unit used to determine a lidar-location of the object proximate to the host-vehicle, and a radar-unit used to determine a radar-location of the object proximate to the host-vehicle. The controller is in communication with the at least three sensors. The controller is configured to determine a composite-location based on a comparison of locations indicated by the at least three sensors. Information from one sensor is ignored when a respective location indicated by the one sensor differs from the composite-location by greater than an error-threshold. If a remote sensor not on the host-vehicle is used, V2V or V2I communications may be used to communicate a location to the host-vehicle.

Abstract: A learning system for an automated vehicle to learn local traffic customs includes a location-detector, and object-detector, and a controller. The location-detector indicates a location of a host-vehicle on a digital-map. The object-detector detects a lane-marking and other-vehicles proximate to the host-vehicle. The controller is in communication with the location-detector and the object-detector. The controller is configured to determine when an observed-behavior of the other-vehicles is not in accordance with the lane-marking present at the location, and operate the host-vehicle in accordance with the observed-behavior.

Abstract: Provided are systems, methods, and computer-readable medium for identifying security risks in applications executing in a cloud environment. In various implementations, a security monitoring and management system can obtain application data from a service provider system. The application data can include a record of actions performed by an application during use of the application by users associated with a tenant. The application executes in a service platform provided for the tenant by the service provider system. In various implementations, the application data is analyzed to identify an event associated with a security risk, where the event is identified from one or more actions performed by the application. The system can determine an action to perform in response to identifying the event. In various examples, an agent executing on the service platform can add instrumentation codes used by the application, where the instrumentation provides the application data.

Abstract: A safe-to-proceed system (10) for operating an automated vehicle proximate to an intersection (14) includes an intersection-detector (18), a vehicle-detector (20), and a controller (24). The intersection-detector (18) is suitable for use on a host-vehicle (12). The intersection-detector (18) is used to determine when a host-vehicle (12) is proximate to an intersection (14). The vehicle-detector (20) is also suitable for use on the host-vehicle (12). The vehicle-detector (20) is used to estimate a stopping-distance (22) of an other-vehicle (16) approaching the intersection (14). The controller (24) is in communication with the intersection-detector (18) and the vehicle-detector (20). The controller (24) is configured to prevent the host-vehicle (12) from entering the intersection (14) when the stopping-distance (22) indicates that the other-vehicle (16) will enter the intersection (14) before stopping.

Abstract: A vehicle control system includes an object-detector, a location-detector, a configuration-map, and a controller-circuit. The object-detector is configured to detect objects proximate to a host-vehicle. The location-detector is configured to indicate a location of the host-vehicle. The configuration-map is configured to indicate a configuration of the object-detector for the location of the host-vehicle when the host-vehicle is operated in an automated-mode. The controller-circuit is in communication with the location-detector, the configuration-map, and the object-detector. The controller-circuit is configured to operate the object-detector in accordance with the configuration for the location of the host-vehicle when the host-vehicle is operated in an automated-mode, detect a human-override of the automated-mode at the location, and update the configuration-map for the location in accordance with objects detected and in response to the human-override of the automated-mode.

Abstract: A detection system includes a first-sensor, a second-sensor, and a controller. The first-sensor is mounted on a host-vehicle. The first-sensor detects objects in a first-field-of-view. The second-sensor is positioned at a second-location different than the first-location. The second-sensor detects objects in a second-field-of-view that at least partially overlaps the first-field of view. The controller is in communication with the first-sensor and the second-sensor. The controller selects the second-sensor to detect an object-of-interest in accordance with a determination that an obstruction blocks a first-line-of-sight between the first-sensor and the object-of-interest.

Abstract: Systems and methods are described for performing Layered Belief LDPC decoding on received Standard Belief LDPC encoded data bursts. In on implementation, a receiver: demodulates a signal, the demodulated signal including a noise corrupted signal derived from a codeword encoded using standard belief LDPC encoding; converts the noise corrupted signal derived from the standard belief LDPC encoded codeword to a noise corrupted signal derived from a layered belief LDPC encoded codeword; and decodes the noise corrupted signal derived from the layered belief LDPC encoded codeword using a layered belief LDPC decoder. In further implementations, systems are described for reducing collisions in Layered Belief LDPC decoders that occur when multiple parity checks need the same soft decision at the same time. In these implementations, elements in an original LBD decoder table are rearranged to increase the distance between elements specifying the same location in a RAM where soft decisions are stored.

Abstract: Systems and methods are described for performing Layered Belief LDPC decoding on received Standard Belief LDPC encoded data bursts. In on implementation, a receiver: demodulates a signal, the demodulated signal including a noise corrupted signal derived from a codeword encoded using standard belief LDPC encoding; converts the noise corrupted signal derived from the standard belief LDPC encoded codeword to a noise corrupted signal derived from a layered belief LDPC encoded codeword; and decodes the noise corrupted signal derived from the layered belief LDPC encoded codeword using a layered belief LDPC decoder. In further implementations, systems are described for reducing collisions in Layered Belief LDPC decoders that occur when multiple parity checks need the same soft decision at the same time. In these implementations, elements in an original LBD decoder table are rearranged to increase the distance between elements specifying the same location in a RAM where soft decisions are stored.

Abstract: Provided are systems, methods, and computer-readable medium for a simulation platform that can generate simulated activity data for testing a security monitoring and control system. In various examples, the simulation platform can parse the activity data from a cloud service to determine the fields associated with each action in the activity data. The simulation platform can then generate a template, where each entry in the template describes an action and the fields associated with the action. The simulation platform can further generate a configuration that describes a test scenario. The simulation platform can use the configuration and the template to generate the particular action, including randomizing some or all of the fields of the action. When input into the security monitoring and control system, the system can operate on the simulated activity data in the same way as when the system ingests live activity data.

Abstract: A radar system for an automated vehicle includes a digital-map, a radar, and a controller. The digital-map indicates a characteristic of a roadway traveled by a host-vehicle. The radar detects objects proximate to the host-vehicle. The radar is equipped with a range-setting that is selectively variable. The controller is in communication with the digital-map and the radar. The controller is configured to select the range-setting of the radar based on the characteristic of the roadway. The characteristic may be based on speed-limit, road-shape (e.g. curve-radius), a horizon-distance, and/or an obstruction (e.g. hill, sign, or building). The radar may be equipped with a frame-rate-setting (i.e. pulse repetition frequency or PRF) that is selectively variable, and the controller may be further configured to select the frame-rate-setting based on the characteristic of the roadway.