Abstract: A system includes at least one electronic control unit, which performs a method including consecutively or simultaneously, determining a future turning maneuver of the ego vehicle, detecting information relating to the lane markings and the number of available lanes in the environment in front of and next to the ego vehicle, and determining whether at least one further road user is in a relevant lane next to or behind the lane of the ego vehicle. If so, a future intended movement of the road user is determined from the information relating to the lane markings and the number of available lanes. And if it is determined that the ego-vehicle and the road user are on an at least two-lane turning lane, or that the road user stops before the turning maneuver of the ego-vehicle or leaves its lane, it is determined that there is no probability of a collision.

Abstract: A control system for a vehicle includes a time sensitive network switch and one or more processors. The time sensitive network switch is configured to receive sensor data from light detection and ranging (LIDAR) sensors, radio detection and ranging (RADAR) sensors, or image sensors. The sensor data is representative of estimated objects that are in an external environment of the autonomous vehicle. The one or more processors are configured to receive the sensor data and determine instructions to control movement of the autonomous vehicle at least partially based on the sensor data from the time sensitive network switch and at least partially based on map data.

Abstract: Examples are directed toward a system and method relating to mobile screening. For example, a mobile screening vehicle includes a passenger scanner that performs security scanning of a passenger on the mobile screening vehicle. The mobile screening vehicle also includes a verification system that verifies, consistent with the security scanning of passengers, that passengers on the mobile screening vehicle are approved to proceed to a secure area of a travel venue.

Abstract: In various embodiments, while a self-driving model operates a vehicle, a user monitoring subsystem acquires sensor data associated with a user of the vehicle and a vehicle observation subsystem acquires sensor data associated with the vehicle. The user monitoring subsystem computes values for a psychological metric based on the sensor data associated with the user. Based on the values for the psychological metric, a feedback application determines a description of the user over a first time period. The feedback application generates a dataset based on the description and the sensor data associated with the vehicle. Subsequently, a training application performs machine learning operation(s) on the self-driving model based on the dataset to generate a modified self-driving model. Advantageously, the dataset enables the training application to automatically modify the self-driving model to account for the impact different driving actions have on the user.

Abstract: A system for trajectory prediction using a predicted endpoint conditioned network includes one or more processors and a memory that includes a sensor input module, an endpoint distribution module, and a future trajectory module. The modules cause the one or more processors to the one or more processors to obtain sensor data of a scene having a plurality of pedestrians, determine endpoint distributions of the plurality of pedestrians within the scene, the endpoint distributions representing desired end destinations of the plurality of pedestrians from the scene, and determine future trajectory points for at least one of the plurality of pedestrians based on prior trajectory points of the plurality of pedestrians and the endpoint distributions of the plurality of pedestrians. The future trajectory points may be conditioned not only on the pedestrian and their immediate neighbors' histories (observed trajectories) but also on all the other pedestrian's estimated endpoints.

Abstract: An electric propulsion system includes a rotary electric machine having an output member, a rechargeable energy storage system (“RESS”) connected to the electric machine, a user interface device, and a controller. The RESS includes multiple battery modules and a switching circuit, the latter being configured, in response to electronic switching control signals, to connect the battery modules in a parallel-connected configuration or a series-connected configuration, as a selected battery configuration. The user interface device receives an operator-requested drive mode request as an electrical signal indicative of a desired drive mode of the electric propulsion system.

Abstract: Examples are directed toward a system and method relating to mobile screening. For example, a mobile screening vehicle includes a passenger scanner that performs security scanning of a passenger on the mobile screening vehicle. The mobile screening vehicle also includes a verification system that verifies, consistent with the security scanning of passengers, that passengers on the mobile screening vehicle are approved to proceed to a secure area of a travel venue.

Abstract: Based on a model and an identity of an operator of a vehicle, a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle can be determined. The model can be based on historical distances maintained by the operator. The model can also be based on information about an environment in which the vehicle is operating or information about a state of the operator. The identity can be received from an interface configured to receive information associated with the identity, a cloud computing platform, or a biometric system configured to determine the identity. The cruise control can be caused to operate according to the setting. A rate of energy consumption by the vehicle when the vehicle is controlled by the cruise control can be less than the rate of energy consumption when the vehicle lacks being controlled by the cruise control.

Abstract: Autoland systems and processes for landing an aircraft without pilot intervention are described. In implementations, the autoland system includes a memory operable to store one or more modules and at least one processor coupled to the memory. The processor is operable to execute the one or more modules to identify a plurality of potential destinations for an aircraft; calculate a merit for each potential destination identified; select a destination based upon the merit; and create a route from a current position of the aircraft to an approach fix associated with the destination that accounts for the terrain characteristic(s) and/or obstacle characteristic(s).

Abstract: Described herein is a LIDAR device that may include a transmitter, first and second receivers, and a rotating platform. The transmitter may be configured to emit light having a vertical beam width. The first receiver may be configured to detect light at a first resolution while scanning the environment with a first FOV and the second receiver may be configured to detect light at a second resolution while scanning the environment with a second FOV. In this arrangement, the first resolution may be higher than the second resolution, the first FOV may be at least partially different from the second FOV, and the vertical beam width may encompass at least a vertical extent of the first and second FOVs. Further, the rotating platform may be configured to rotate about an axis such that the transmitter and first and second receivers each move based on the rotation.

Abstract: A vehicle control system includes a control unit for vehicle control and a server which distributes map information to the control unit. The map information includes information on a first type of road whereby full or partial driving assistance of the vehicle is possible, and information on a second type whereby full or partial driving assistance of the vehicle are not possible. The server includes an output unit which transmits information on a second type of road existing in a predetermined range from a first type of road on which the vehicle is traveling, and such that the first and second types of road form a predetermined angle or less. The control unit includes a map management unit including information on the first and second types of road, and a vehicle control unit controlling the vehicle by using the information on the first and second types of road.

Abstract: A map in a cloud service stores physical objects previously detected by other vehicles that have previously traveled over the same road that a current vehicle is presently traveling on. New data received by the cloud service from the current vehicle regarding new objects that are being encountered by the current vehicle can be compared to the previous object data stored in the map. Based on this comparison, an operating status of the current vehicle is determined. In response to determining the status, an action such as terminating an autonomous navigation mode of the current vehicle is performed.

Abstract: An unmanned aerial vehicle (UAV) cluster includes a plurality of mission UAVs and a plurality of core UAVs arranged in a cluster. One or more of the mission UAVs is configured for controlled independent flight. The plurality of core UAVs are distributed throughout the cluster according to a selected distribution pattern that distributes the core UAVs according to a predefined mission characteristic of the UAV cluster.

Abstract: The disclosure relates to a method and an associated device for detecting uneven surfaces in vehicle environments. The method comprises emitting a first ultrasonic pulse or first ultrasonic burst and emitting a second ultrasonic pulse or second ultrasonic burst, and receiving a first reflection signal of the first ultrasonic pulse or a first reflection signal of the first ultrasonic burst and receiving a second reflection signal of the second ultrasonic pulse or a second reflection signal of the second ultrasonic burst. In the further course of the method, a comparison is made of the first reflection signal with the second reflection signal, and the presence of a surface unevenness in the vehicle's environment, or the presence of a surface curvature in the vehicle's environment, is determined.

Abstract: A method for instructing users of car-sharing services to operate a shared vehicle includes identifying a user of a selected vehicle, determining a customary vehicle of the user based on the identification, determining differences between the selected vehicle and the customary vehicle, and informing the user of the determined differences.

Abstract: An apparatus for assisting driving of a host vehicle includes: a camera mounted on the host vehicle and having a field of view outside of the host vehicle, the camera configured to acquire image data; and a controller configured to: process the image data, identify a speed limit of a road on which the host vehicle is driving and whether there is a speed enforcement for the host vehicle based on the image data; and provide information about the speed limit of the road to a driver of the host vehicle based on the speed limit of the road and whether there is the speed enforcement for the host vehicle. Thereby, the vehicle can be prevented from driving in excess of the speed limit.

Abstract: An autonomous drive instruction device shortens the time required for vehicles to arrive at a designated location from a parking location in a parking lot without a navigation map. The autonomous drive instruction device includes a travel-direction determiner and an autonomous drive instructor. The travel-direction determiner determines one path as the travel direction of the vehicle on the basis of the designated location and information on paths in a branch area. The one path corresponds to an extension direction that forms a smaller one of angles formed by extension directions of the paths from the branch area and a designated-location direction from the branch area to the designated location. The autonomous drive instructor outputs instruction information including information on the travel direction to the autonomous drive control device such that the autonomous dive control device controls the vehicle to move in the travel direction by autonomous driving.

Abstract: Systems and methods for controlling an autonomous vehicle in response to vehicle instructions from a remote computing system are provided. In one example embodiment, a computer-implemented method includes controlling a first autonomous vehicle to provide a vehicle service, the first autonomous vehicle being associated with a first convoy that includes one or more second autonomous vehicles. The method includes receiving one or more communications from a remote computing system associated with a third-party entity, the one or more communications including one or more vehicle instructions. The method includes coordinating with the one or more second autonomous vehicles to determine one or more vehicle actions to perform in response to receiving the one or more vehicle instructions from the third-party entity. The method includes controlling the first autonomous vehicle to implement the one or more vehicle actions.

Abstract: Systems and methods for determining a swing angle of a swing boom of a vehicle are provided. Sensor data is received from sensors disposed on a swing boom and a body of a vehicle. It is determined whether the swing boom is static or moving relative to the body based on the sensor data. In response to determining that the swing boom is static, the received sensor data is corrected based on an observed swing angle and an estimated swing angle is calculated based on the corrected sensor data. In response to determining that the swing boom is moving, the estimated swing angle is calculated based on the received sensor data. The estimated swing angle is output.

Abstract: Systems and methods for predicting object motion and controlling autonomous vehicles are provided. In one example embodiment, a computer implemented method includes obtaining state data indicative of at least a current or a past state of an object that is within a surrounding environment of an autonomous vehicle. The method includes obtaining data associated with a geographic area in which the object is located. The method includes generating a combined data set associated with the object based at least in part on a fusion of the state data and the data associated with the geographic area in which the object is located. The method includes obtaining data indicative of a machine-learned model. The method includes inputting the combined data set into the machine-learned model. The method includes receiving an output from the machine-learned model. The output can be indicative of a plurality of predicted trajectories of the object.