Abstract: Disclosed is a golf-shot-tracking-self-driving-path central controlling system, comprising a predetermined-paths-determining module, a golf-ball-next-shot-location determining module and a path driving controlling module, to centrally control each of a plurality of golf-shot-tracking fairway-self-driving golf carts to drive in one of a plurality of predetermined paths or shift among the plurality of predetermined paths.

Abstract: The present invention relates to a collision distance estimation device and a driver assistance system using the same. The collision distance estimation device includes an image acquisition unit configured to acquire images of surroundings of a vehicle to generate image information, an image reading unit configured to detect and identify an object present around the vehicle from the image information to generate object recognition information, a travel detection unit configured to generate movement distance information on the basis of wheel sensing information, steering information, and the image information, and a collision distance calculation unit configured to calculate collision distance information on the basis of the object recognition information and the movement distance information.

Abstract: Systems and methods are directed to an autonomy computing system of an autonomous vehicle. The autonomy computing system can include first functional circuitry configured to generate a first output associated with a first autonomous compute function of the autonomous vehicle based on sensor data using first neural networks. The autonomy computing system can include second functional circuitry configured to generate a second output associated with the first autonomous compute function of the autonomous vehicle based on the sensor data and neural networks. The autonomy computing system can include monitoring circuitry configured to determine a difference between the first output of the first functional circuitry and the second output of the second functional circuitry. The autonomy computing system can include a vehicle control system configured to generate vehicle control signals for the autonomous vehicle based on the outputs.

Abstract: Disclosed are systems and techniques for using blockchain technology to maintain and validate a vehicle ledger. The technique includes receiving, at a master node in the system, a request to update a vehicle ledger associated with a first vehicle node comprising the system. If first criteria are met, the system updates the vehicle ledger, including: generating an updated version of the vehicle ledger using vehicle data stored in a master ledger associated with the master node, and transmitting the updated version of the vehicle ledger to the first vehicle node. If the first criteria are not met, the system forgoes updating the vehicle ledger. The vehicle data corresponds to a first vehicle associated with the first vehicle node. The master ledger is implemented using a blockchain that contains vehicle records for vehicles associated with the system. The blockchain includes a first block including vehicle data corresponding to the first vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for using a drone to monitor a community. The drone may include a processor and a storage device storing instructions that, when executed by the processor, cause the one or more processors to perform operations. The operations may include receiving an instruction to deploy based on a determination, by a community monitoring system that an event was detected at a property of the community, navigating towards the property along an initial navigation path, obtaining local monitoring system data from a local monitoring system of a property of the community, generating based on the local monitoring system data a navigational model that identifies a location of each of one or more surveillance objectives, determining an adjusted navigation path to a location of a surveillance objective of the one or more surveillance objectives, and navigating along the adjusted navigation path.

Abstract: A system includes a locator device and one or more processors operably connected to the locator device. The locator device determines a trailing distance between a trailing vehicle system that travels along a route and a leading vehicle system that travels along the route ahead of the trailing vehicle system in a same direction of travel. The one or more processors compare the trailing distance to a first proximity distance relative to the leading vehicle system. In response to the trailing distance being less than the first proximity distance, the one or more processors set a permitted power output limit for the trailing vehicle system to be less than a maximum achievable power output for the trailing vehicle system, the permitted power output limit being set based on a power-to-weight ratio of the leading vehicle system.

Abstract: Described herein are systems, methods, and non-transitory computer readable media for generating fused sensor data through metadata association. First sensor data captured by a first vehicle sensor and second sensor data captured by a second vehicle sensor are associated with first metadata and second metadata, respectively, to obtain labeled first sensor data and labeled second sensor data. A frame synchronization is performed between the first sensor data and the second sensor data to obtain a set of synchronized frames, where each synchronized frame includes a portion of the first sensor data and a corresponding portion of the second sensor data. For each frame in the set of synchronized frames, a metadata association algorithm is executed on the labeled first sensor data and the labeled second sensor data to generate fused sensor data that identifies associations between the first metadata and the second metadata.

Abstract: A system and method for contextually monitoring vehicle states. The method includes enriching a plurality of messages based on at least one missing property value and a plurality of predictive properties in the plurality of messages, wherein at least one message of the plurality of messages is generated by a vehicle monitoring system, wherein each message of the plurality of messages is ingested at a vehicle state monitor, wherein each of the predictive properties is a property indicated in one of the plurality of messages of a predetermined type of property; and generating a contextual vehicle state for a vehicle based on the enriched plurality of messages, wherein the contextual vehicle state is a snapshot of a plurality of vehicle state properties at a given time.

Abstract: In one embodiment, a first trajectory is generated for a driving environment using control values allowed for a driving-by-wire system. If the trajectory includes a collision with an object, the ADV estimates the time of the collision and the relative speed between the ADV and the object at the time of the collision. A second trajectory is then generated for the driving environment using control values allowed for a human driver. The time of the collision and the relative speed between the ADV and the object at the time collision on the second trajectory are also estimated. The ADV then compares the two collision times and the two relative speeds, and based on the comparison, generates an alert message for the human driver to take over the control of the ADV.

Abstract: A vehicle agnostic removable pod can be mounted on a vehicle using one or more legs of a pod mount. The removable pod can collect and time stamp a variety of environmental data as well as vehicle data. For example, environmental data can be collected using a sensor suite which can include an IMU, 3D positioning sensor, one or more cameras, and/or a LIDAR unit. As another example, vehicle data can be collected via a CAN bus attached to the vehicle. Environmental data and/or vehicle data can be time stamped and transmitted to a remote server for further processing by a computing device.

Abstract: A method for checking a capacity of at least one supply line for an electrically operated assembly that is coupled electrically via the at least one supply line to an electrical system of an at least partially automated mobile platform. The method includes: ensuring a non-critical operating state of the mobile platform; determining an off-load terminal voltage at an input connection of the assembly, with zero-current supply line; applying a defined current to the supply line of the electrically operated assembly; determining an on-load terminal voltage at the input connection of the assembly, in doing so, the defined voltage being applied to the supply line of the electrically operated assembly; determining a differential voltage between the off-load terminal voltage and the on-load terminal voltage; comparing the differential voltage to a differential-voltage limit value to determine whether the capacity of the electric supply line is sufficient to operate the assembly.

Abstract: Disclosed are a robot and a method for delivering articles by the robot. A robot, according to an embodiment of the present disclosure, includes: one or more loading spaces configured to load articles; a travel driver configured to move the robot; an input interface configured to obtain identification information of the articles; a measurer configured to measure temperature of each of the articles; a temperature regulator configured to heat or cool the loading spaces; and a controller configured to determine a type of each of the articles from the identification information, determine a delivery route based on the determined type of article, and control the temperature regulator based on the measured temperature while controlling the travel driver such that the robot moves along the determined delivery route. Examples of the present disclosure are implemented by executing a machine learning algorithm in a 5G environment connected for Internet of things (IoT).

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to identify a virtual boundary between a host roadway lane of a host vehicle and a target roadway lane of a target vehicle, the virtual boundary based on a predicted path of the target vehicle, determine a first constraint value based on a boundary approach velocity of the target vehicle, determine a second constraint value based on (1) a boundary approach velocity of the host vehicle and (2) a boundary approach acceleration of the host vehicle and perform a threat assessment of a collision between the host vehicle and the target vehicle upon determining that the first constraint value violates a first threshold or the second constraint value violates a second threshold.

Abstract: In a vehicle system that can receive remote support from a remote support server (e.g., interfacing with a human or computer teleoperator), a local normalization engine locally normalizes operation of the vehicle based on locally available sensor data that may not be accessible to the remote support server. The local normalization engine applies transformations to control commands received from the remote support server to transform the command to compensate for conditions that are locally sensed and may be unknown to the remote support server. Alternatively, or in addition, the local normalization engine controls auxiliary functions of the vehicle (e.g., by activating one or more auxiliary actuators) that may not be under direct control of the remote support server.

Abstract: A control system for a work vehicle includes an actual topography acquisition device, a storage device, a soil amount acquisition device, and a controller. The actual topography acquisition device acquires actual topography information indicating an actual topography of a work target. The storage device stores design topography information indicating a final design topography. The soil amount acquisition device generates a soil amount signal indicating a held soil amount of the work implement. The controller acquires the actual topography information from the actual topography acquisition device and acquires the design topography information from the storage device. The controller generates a command signal for moving the work implement at position that is between the actual topography and the final design topography and is a predetermined distance above the actual topography. The controller acquires the soil amount signal and changes the predetermined distance based on the held soil amount.

Abstract: A method includes receiving a component history for a processor module, the component history including identities of host vehicles in a set of host vehicles in which the processor module was installed; receiving an operational history for the set of host vehicles for a time period the processor module was installed on the set of host vehicles; receiving indirect sensor measurements related to the set of host vehicles for the time period; receiving a part survival model that is based at least in part on a part status of a plurality of historical processor modules, the plurality of historical processor modules having a historical component history, a historical operational history, and historical indirect sensor measurements; and determining a survival probability of the processor module based at least in part on the component history, the operational history, the indirect sensor measurements, and the part survival model.

Abstract: Multi-agent reinforcement learning may be used for rider order-dispatching via matching the distribution of orders and vehicles. Information may be obtained. The information may include a plurality of vehicle locations of a plurality of vehicles, a plurality of ride orders, and a current time. The obtained information may be input into a trained model. The trained model may be based on Kullback-Leibler divergence optimization and independent agents under a guidance of a joint policy. A plurality of order-dispatching tasks may be generated for the plurality of vehicles to fulfill.

Abstract: A managing apparatus for positioning rental vehicles includes a memory storing instructions and a processor configured to execute the instructions to cause the managing apparatus to access model information and location information for a plurality of autonomous vehicles, receive a request including a delivery location and a chosen model for renting, select an autonomous vehicle of the chosen model from among the plurality of autonomous vehicles based on the model information, the location information, and the delivery location, instruct the selected autonomous vehicle to fully-autonomously or semi-autonomously travel to the delivery location, and instruct the selected autonomous vehicle to switch to manual operation mode at the delivery location for manual operation by a vehicle rental customer.

Abstract: A vehicle includes a plurality of controllers. During a key-off mode, a master controller may maintain operation of a predetermined set of controllers by broadcasting a power sustain request on a communication bus. Responsive to a loss of communication for a time exceeding a predetermined amount of time with one or more of the predetermined set of controllers, the master controller may be programmed to stop broadcasting the power sustain request to cause the predetermined set of controllers to power down.

Abstract: A vehicle control system is provided that includes a control unit that can be disposed onboard a vehicle to control movement of the vehicle; and one or more transceiver devices that can emit plural signals from the vehicle, with at least one of the plural signals containing a vehicle identifier, and, responsive to a receiver unit disposed off-board the vehicle receiving at least one of the plural signals, the control unit can determine a location of the vehicle and can communicate a signal to the one or more transceiver devices based on the location. The control unit can change the movement of the vehicle based on the vehicle location information.