Abstract: In techniques disclosed herein, machine learning models can be utilized in the control of autonomous vehicle(s), where the machine learning models are trained using automatically generated training instances. In some such implementations, a label corresponding to an object in a labeled instance of training data can be mapped to the corresponding instance of unlabeled training data. For example, an instance of sensor data can be captured using one or more sensors of a first sensor suite of a first vehicle can be labeled. The label(s) can be mapped to an instance of data captured using one or more sensors of a second sensor suite of a second vehicle.

Abstract: A system, method and processor readable medium for identifying an operational design domain (ODD) for operation of an autonomous driving system (ADS). In one aspect, a proposed map is used to generate a geographic dataset. Performance of the ADS is evaluated against the geographic dataset under a range of environment& conditions, thereby identifying a bounded-risk portion of a proposed condition space defined by the proposed map and the range of environmental conditions. The operational design domain is identified based on the bounded-risk portion of the proposed condition space. The ODD can be updated as additional data is received. When a vehicle using the ADS is likely to leave the ODD, an operator can be alerted.

Abstract: A vehicle control device is equipped with a travel control unit which, in the case that a user-requested driving force exceeds a system-required driving force in a state in which a user is not in contact with an operating element, performs a travel control on the basis of a limited driving force obtained by applying a limit to the user-requested driving force, whereas in the case that the user-requested driving force exceeds the system-required driving force in a state in which the user is in contact with the operating element, performs the travel control on the basis of the user-requested driving force without applying the limit to the user-requested driving force.

Abstract: A method or system for controlling safety of both an ego vehicle and social objects in an environment of the ego vehicle, comprising: receiving data representative of at least one social object and determining a current state of the ego vehicle based on sensor data; predicting an ego safety value corresponding to the ego vehicle, for each possible behavior action in a set of possible behavior actions, based on the current state; predicting a social safety value corresponding to the at least one social object in the environment of the ego vehicle, based on the current state, for each possible behavior action; and selecting a next behavior action for the ego vehicle, based on the ego safety values, the social safety values, and one or more target objectives for the ego vehicle.

Abstract: An example method of performing maintenance on an aircraft includes receiving a vehicle data-signature-map of an interior of an aircraft for at least one parameter of the aircraft, and the vehicle data-signature-map is based on sensor outputs received from sensors of mobile devices positioned at locations in the interior of the aircraft.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for predetermining adjustment objects and modifying vehicle lighting unit(s) accordingly. A vehicle lighting system can comprise an imaging device configured to image the surroundings of a vehicle and a data communications device configured to obtain vehicle route data. A controller element can be configured to predetermine presence of an adjustment object based on route data and imaging data. The system is configured to set the beam mode of at least one vehicle lighting unit based on the predetermination. A method can comprise the steps of (a) obtaining vehicle route data, (b) obtaining vehicle surroundings image data, (c) predetermining the occurrence of an adjustment object based on the route data and imaging data and (d) setting the vehicle beam mode based on the predetermination.

Abstract: A control device for a vehicle that has a sensor that acquires automated driving information required for automated driving and a display that provides a driver with information. The control device includes an electronic control unit including a processor configured to: perform automated driving for automatically performing operations for driving the vehicle based on the automated driving information; and make a driving intervention degree of the driver change during automated driving, including: judge a distance or a required time until an anticipated switching point where switching to manual driving is anticipated; judge a difficulty of take-over of a driver when switching to manual driving; set a demanded driving intervention degree of a driver during automated driving based on the distance or required time up to the anticipated switching point and the difficulty of take-over; and provide information relating to the demanded driving intervention degree to the driver.

Abstract: Provided is a vehicle control system including a plurality of vehicles, a storage that stores information relating to a user who is on a vehicle for each of the plurality of vehicles, at least one processor configured to receive a matching request for requesting meeting with a user who is on one of the plurality of vehicles, specify a first vehicle which a first user matching the matching request is on, on the basis of the information stored in the storage and perform control on the first vehicle so as to let the first vehicle and a requesting source of the matching request meet each other.

Abstract: A method, a device and an apparatus for generating a defensive driving strategy and a storage medium are provided. The method includes: detecting a type of each obstacle of a plurality of obstacles in a sensible range of an autonomous vehicle; determining whether there is a collision risk of the autonomous vehicle with the obstacle by using a collision risk detection method corresponding to the type; and determining the defensive driving strategy for the autonomous vehicle when the obstacle has a collision risk with the autonomous vehicle in the sensible range. According to the embodiments, an occurrence probability of a dangerous condition on a road may be reduced.

Abstract: A method, apparatus, and system for integrating a non-autonomous vehicle into a transit environment populated with autonomous vehicles. An autonomous vehicle network integration apparatus is disclosed which collects data over a wireless network regarding a vehicle's route and surroundings, including nearby fully and partially autonomous vehicles. The apparatus is configured to analyze data and dynamically determine a range of influence, within which it communicates with vehicles to suggest driver actions and inform self-driving vehicle behavior.

Abstract: Sensor data collected via an autonomous vehicle can be labeled using sensor data collected via an additional vehicle, such as a non-autonomous vehicle mounted with a vehicle agnostic removable hardware pod. A training instance can include an instance of data collected by an autonomous vehicle sensor suite and one or more corresponding labels.

Abstract: The present disclosure generally relates to methods and systems for determining usage of a vehicle. A vehicle monitoring system may include a memory, at least one processor coupled to the memory, and a sensor system that generates vehicle trip data. The system may periodically determine a location of the vehicle during a trip while an ignition of the vehicle remains on. The system may determine that a duration of the trip is at least a threshold duration. The system may determine a direction of travel for segments between consecutive locations during the trip. The system may determine a ratio of a most common direction of travel for the trip to at least a second most common direction of travel for the trip. The system may determine that the vehicle has been used for commercial purposes in response to the ratio being less than a threshold ratio.

Abstract: A steering control system for an autonomous or semi-autonomous vehicle including at least one gaze sensor determining a driver gaze angle. The system also includes a controller comparing the driver gaze angle to predetermined gaze angles required for future vehicle locations associated with vehicle maneuvers.

Abstract: In one embodiment, a vehicle includes a steering wheel, one or more vehicle drive systems, one or more driving mode paddler shifters coupled to the steering wheel, an output device, and an electronic control unit. The electronic control unit is communicatively coupled to the one or more driving mode paddle shifters, the one or more vehicle drive systems, and the output device. The electronic control unit is configured to receive a drive mode shifting signal from the one or more driving mode paddle shifter indicative of a desire to change a driving mode of the vehicle, change one or more parameters of the one or more vehicle drive systems in order to change the driving mode in response to the driving mode shifting signal, and provide a notification related to the change in the driving mode with the output device.

Abstract: A vehicle driving assist apparatus includes a searching unit and a guidance unit. The searching unit is configured to search for a travel route based on a search instruction. The travel route is a route along which a vehicle travels to a destination. The guidance unit is configured as follows: when the searching unit finds a plurality of travel routes and the plurality of travel routes includes two or more travel routes each including a merging point where two or more roads meet, the guidance unit provides a driver with information about a travel route that is included in the plurality of travel routes and that includes the merging point before which there is a road that is included in the two or more roads and that allows vehicles to easily accelerate, in preference to the rest of the travel routes.

Abstract: A vehicle system includes a processor and a memory. The memory stores instructions executable by the processor to identify an area of interest from a plurality of areas on a map, to determine that a detected sound is received in a vehicle audio sensor upon determining that a source of the sound is within the area of interest and not another area in the plurality of areas, and to operate the vehicle based at least in part on the detected sound.

Abstract: A travel control device includes an operation acquiring unit for acquiring an operation by a driver of a host moving body; an outside-world information acquiring unit for acquiring outside-world information of the periphery of the host moving body; a moving-body information acquiring unit for acquiring moving-body information relating to a travel state of the host moving body; a travelable-range management unit for managing the range travelable by the moving body; and a control unit for controlling travel by the moving body on the basis of the operation acquired by the operation acquiring unit, the outside-world information acquired by the outside-world information acquiring unit, the moving-body information acquired by the moving-body information acquiring unit, and the travelable range managed by the travelable-range management unit, the travelable-range management unit including a travelable-range enlargement unit for enlarging the travelable range, and a travelable-range evaluation unit for evaluating the

Abstract: A method for displaying obstacle detection for an unmanned aerial vehicle (UAV) includes receiving obstacle information associated with at least one direction of the UAV. The obstacle information is obtained by analyzing a sensing signal of at least one sensor of the UAV. The method further includes displaying the obstacle information.

Abstract: An unmanned, autonomous, battery powered and rechargeable vehicle having multiple sensors for collecting, analyzing, storing and transmitting data related to environmental and plant growth data in greenhouse environment. The mobile vehicle navigates between plant rows tracking a designated and repeatable path several times daily, collecting data from the plant root ball up to and including the plant canopy, without touching, sampling, or impacting plant growth. A telescoping arm ensures the sensor platform height is optimized relative to the plant canopy height.