Abstract: Aspects of the disclosure relate to enhanced telematics processing systems with improved third party source data integration features and enhanced customized driving output determinations. A computing platform may receive telematics data and third party source data. The computing platform may enrich the telematics data using the third party source data. After generating the enriched telematics data, the computing platform may use machine learning algorithms and datasets to validate the enriched telematics data. The computing platform may ingest, via a batch ingestion process, the enriched telematics data. For example, the computing platform may store the enriched telematics data and generate additional enriched telematics data until expiration of a predetermined period of time. The computing platform may ingest the enriched telematics data associated with each trip. Once the enriched telematics data has been ingested, the computing platform may generate a standardized common trip format output for each trip.

Abstract: According to one embodiment, a transport service method comprises organizing a vehicle group of a plurality of self-driving vehicles in which a parameter value inherent to vehicles falls within a predetermined range, and controlling cooperative travel of the self-driving vehicles in the vehicle group to perform transport service of baggage, persons, animals, and the like.

Abstract: An apparatus for remote support of autonomous operation of a vehicle includes a processor that is configured to perform a method including receiving a request for a trip through a transportation network, wherein the trip includes a first travel portion from a first location to a second location, and a second travel portion from the second location to a third location. Each of the first travel portion and the second travel portion is associated with a different service such that a first payload of the first travel portion is different from a second payload of the second travel portion. The method also includes determining, based on the request, a route for the trip from the first location to the second location, and from the second location to the third location, and performing a validation process that confirms a presence of cargo in the first payload and/or the second payload.

Abstract: A fallback safety system for a vehicle equipped with a platooning system is configured to detect an operating state and a functional failure of the platooning system of the vehicle and to detect a distance of the vehicle to a vehicle driving in front with a sensor system of the vehicle. Further, in the case of a detected functional failure of the platooning system during a convoy driving operation of the vehicle controlled by the platooning system, the fallback safety system is configured to initiate braking of the vehicle and to adjust a braking acceleration of the vehicle during the initiated braking depending on the detected distance of the vehicle to the vehicle driving in front.

Abstract: A vehicle control device includes a first inputter, a second inputter, a mode controller configured to, when the first inputter is operated by a user, determine a driving mode of a vehicle as a first mode, and when the second inputter is operated by the user in the first mode, switch the driving mode from the first mode to a second mode, and a driving controller configured to control at least one of a steering and speed of the vehicle, and the driving controller is configured to control a steering and speed of the vehicle and prohibit change of a path of the vehicle when the driving mode is the first mode, and control a steering and speed of the vehicle and change of the path of the vehicle is allowed when the driving mode is the second mode.

Abstract: Systems, devices, and methods for identifying a target aerial vehicle, deploying an interceptor aerial vehicle comprising at least one effector, maneuvering the interceptor aerial vehicle to a position to engage a target aerial vehicle, deploying the at least one effector to intercept the target aerial vehicle, and confirming that the target aerial vehicle has been intercepted.

Abstract: An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Abstract: An operation device including: an operation section configured to receive operation by a remote operator conferred with operation authority to operate a vehicle capable of autonomous driving; a communication section configured to transmit, to the vehicle, operation information for remote driving based on the operation received by the operation section; a memory; and a processor coupled to the memory, the processor being configured to: acquire biometric information regarding the remote operator, determine whether or not a compromised state, in which operation of the vehicle by the remote operator is compromised, has arisen based on the acquired biometric information, and transfer operation authority of the vehicle to another remote operator in a case in which the compromised state has been determined to have arisen.

Abstract: An impairment analysis (“IA”) computer system for alerting a first driver of a first vehicle to a driving hazard posed by a second vehicle operated by a second driver is provided. The IA computer system is associated with the first vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) receive second vehicle data including second driver data and second vehicle condition data, where the second vehicle data is collected by a plurality of sensors included on the first vehicle; (ii) analyze the second vehicle data by applying a baseline model to the second vehicle data; (iii) determine that the second vehicle poses a driving hazard to the first vehicle based upon the analysis; and/or (iv) generate an alert signal based upon the determination that the second vehicle poses a driving hazard to the first vehicle.

Abstract: An unsupervised real to virtual domain unification model for highway driving, or DU-drive, employs a conditional generative adversarial network to transform driving images in a real domain to their canonical representations in the virtual domain, from which vehicle control commands are predicted. In the case where there are multiple real datasets, a real-to-virtual generator may be independently trained for each real domain and a global predictor could be trained with data from multiple real domains. Qualitative experiment results show this model can effectively transform real images to the virtual domain while only keeping the minimal sufficient information, and quantitative results verify that such canonical representation can eliminate domain shift and boost the performance of control command prediction task.

Abstract: A vehicle control system installed on a vehicle includes: an automatic steering control device configured to execute automatic steering control that determines a steering angle command value and controls steering of the vehicle such that an actual steering angle follows the steering angle command value; a stop state detection device configured to detect a stop state where the vehicle is stopped; and an override detection device configured to detect an override operation by a driver of the vehicle. When the override operation is detected in the stop state, the automatic steering control device prohibits variation in the actual steering angle due to the automatic steering control, until the vehicle starts moving.

Abstract: There is provided a method and a system for authorizing a user device to send a request to a vehicle in order to prevent a physical layer relay attack. The system comprises a vehicle comprising an acoustic transducer and an RF transceiver and a user device comprising an acoustic transducer and an RF transceiver. The method relates to a signaling scheme using a combination of acoustic and RF signals for preventing a successful physical layer relay attack.

Abstract: The disclosed embodiments are directed to adjusting the operational characteristics of a black box installed in a vehicle. In one embodiment a method is disclosed comprising classifying a mental state of a driver of an automobile, loading at least one setting based on the mental state, the setting defining an operational characteristic of a black box installed in the automobile, configuring the black box using the at least one setting, and recording one or more events by the black box.

Abstract: A computer is programmed to allocate respective connectivity quality data of a geographic area to a first map or a second map. The computer is further programmed to assign one of a plurality of subsets of the first map and one of a plurality of subsets of the second map to a first vehicle, identify respective locations of the first and second vehicles and one of the first or second maps that includes the locations of the first and second vehicles. The computer is further programmed to send, to the first and second vehicles, a map dataset that is a result of applying an XOR function to (1) the subset of the identified map that includes the location of the first vehicle assigned to the first vehicle and (2) the subset of the identified map that includes the location of the second vehicle assigned to the second vehicle.

Abstract: According to one or more embodiments, a method includes computing, by a steering system, a model rack force value based on a vehicle speed, steering angle, and a road-friction coefficient value. The method further includes determining, by the steering system, a difference between the model rack force value and a load rack force value. The method further includes updating, by the steering system, the road-friction coefficient value using the difference that is determined.

Abstract: According to one or more embodiments, a method includes computing, by a steering system, a model rack force value based on a vehicle speed, steering angle, and a road-friction coefficient value. The method further includes determining, by the steering system, a difference between the model rack force value and a load rack force value. The method further includes updating, by the steering system, the road-friction coefficient value using the difference that is determined.

Abstract: An information presentation device, mounted on a vehicle for which automatic evacuation control functions when it is difficult for a driver to continue driving the vehicle, and presenting information to an occupant of the vehicle except the driver by a display in a display area that is visually recognizable by the occupant, includes: an operation information acquisition unit that acquires operation information of the automatic evacuation control; and a display generation unit that generates an occupant notification display that is displayed in the display area to notify information relating to the automatic evacuation control when the automatic evacuation control is in operation. The occupant notification display includes: an explanatory image that shows an explanation of a process executed currently; and a progress image that indicates a degree of progress in the automatic evacuation control.

Abstract: A vehicle control system for a semi-autonomous vehicle is provided. The vehicle control system includes a controller coupled to a plurality of sensors positioned within the vehicle and to a heads-up display (HUD). The controller includes a processor in communication with a memory device. The controller receives sensor data from the plurality of sensors, determines the vehicle is turning, identifies, based on the sensor data, a candidate turn path for the vehicle, and identifies an actual turn path for the vehicle. The controller also transmits, to one or more automation systems of the vehicle, a control signal that instructs the automation systems to perform a turn-assist function to reduce a determined deviation between the actual turn path and the candidate turn path and to transmit, to the HUD, a control signal that instructs the HUD to display a notification to a driver of the vehicle of the turn-assist function.

Abstract: Method and device for determining trajectory to optimal position of a follower aircraft with respect to vortices generated by a leader aircraft. The method includes controlling trajectory of a follower aircraft to an optimal position where the follower aircraft benefits from effects of at least one of the vortices of a leader aircraft. A first section control step controls flight of the follower aircraft using current measurements of flight parameters, from a safety position to a search position, along an approach section passing through an approach zone. A second section control step controls flight of the follower aircraft using current measurements of flight parameters, from the search position to a precision position, along a search section passing through a search zone, and a third section control step controls flight of the follower aircraft, from the precision position to the optimal position, along an optimization section passing through an optimization zone.

Abstract: The present disclosure provides a method and apparatus of monitoring a sensor of a driverless vehicle, a device and a storage medium, wherein the method comprises: monitoring a physical state of a to-be-monitored sensor; monitoring a data transmission state of the to-be-monitored sensor; monitoring output data of the to-be-monitored sensor, and using predetermined data to perform cross-validation for the output data; when any monitoring result gets abnormal, determining the to-be-monitored sensor as getting abnormal, and giving an alarm. The solution of the present disclosure may be applied to improve safety of the driverless vehicle.