Abstract: A driving assistance apparatus includes an estimator and a controller. The estimator estimates a first estimated vehicle speed and a second estimated vehicle speed when there are a first deceleration target and a second deceleration target in a course ahead of a vehicle, and when a driver of the vehicle releases an accelerator pedal. Wherein the first estimated vehicle speed is an estimated vehicle speed of the vehicle at a first target position corresponding to the first deceleration target, and the second estimated vehicle speed is an estimated vehicle speed of the vehicle at a second target position corresponding to the second deceleration target. The controller controls the vehicle to automatically decelerate toward the second target position when the first estimated vehicle speed is greater than a first predetermined value, and when the second estimated vehicle speed is less than a second predetermined value.

Abstract: An aircraft includes a fuselage, a main wing, and a flight controller. The main wing is attached to the fuselage and configured to generate lift that acts on the aircraft. The flight controller includes an electric field direction estimator, an attitude calculator, and an attitude controller. The electric field direction estimator is configured to estimate a direction of an electric field around the aircraft. The attitude calculator is configured to calculate a target airframe attitude that reduces a possibility of occurrence of lightning strike on the aircraft, on the basis of the direction of the electric field estimated by the electric field direction estimator. The attitude controller is configured to control an attitude of an airframe of the aircraft to cause the attitude of the airframe to be the target airframe attitude calculated by the attitude calculator.

Abstract: A parking lot management device configured to manage traveling of vehicles by setting a scheduled passage time for each node indicating a travel route in a parking lot includes: an acquisition unit configured to acquire an actual passage time at which a first vehicle has actually passed a first node; a determination unit configured to determine a collision risk between a second vehicle and the first vehicle based on the actual passage time, the second vehicle being scheduled to pass, following the first vehicle, a second node that is located forward of the first node in a traveling direction of the first vehicle; and a setting unit configured to delay a first scheduled passage time at which the second vehicle passes the second node to cause the second vehicle to pass the second node following the first vehicle when it is determined that there is the collision risk.

Abstract: A method for controlling a subsea vehicle. The method includes receiving sensor data representing a subsea environment from one or more sensors of the subsea vehicle. The method identifies one or more objects present in the subsea environment based on the sensor data using an artificial intelligence machine. The method transmits at least a portion of the sensor data, including an identification of the one or more objects, to a user interface. The method includes receiving a requested vehicle task from the user interface. The requested vehicle task being selected by a user via the user interface. The method performs the requested vehicle task without vehicle position control from the user.

Abstract: A control unit performs a lane departure suppression control when a host vehicle is about to depart from a traveling lane. The control unit determines whether a low impact collision has occurred. The control unit performs a secondary collision damage mitigation control when the low impact collision is determined to have occurred while the lane departure suppression control is being performed.

Abstract: The present disclosure relates to a method to control a vehicle system. The method includes detecting a deviating vehicle having at least one of a deviating behaviour and a deviating vehicle property by means of a perception system of a first vehicle, wherein the perception system includes at least one sensor device configured to monitor a surrounding environment of the first vehicle; assigning a deviating vehicle classification to the deviating vehicle based on the deviating behaviour and/or deviating vehicle property; determining at least one second vehicle to receive information relating to the deviating vehicle; and transmitting to each determined second vehicle a set of information relating to the deviating vehicle, said set of information including at least one of the deviating vehicle classification and a predetermined instruction to be performed by the determined second vehicle, wherein the predetermined instruction is dependent on the deviating vehicle classification.

Abstract: A vehicle controller is used for a first vehicle and includes processing circuitry. The processing circuitry is configured to execute an index deriving process that derives a traveling performance index of the first vehicle, the traveling performance index being an index related to a traveling performance, an index receiving process that receives the traveling performance index of a second vehicle from the second vehicle through vehicle-to-vehicle communication, and a performance determination process that compares the traveling performance index of the second vehicle with the traveling performance index of the first vehicle to determine whether a traveling performance of the first vehicle is lower than a traveling performance of the second vehicle.

Abstract: A cooperative driving system includes a processor and a memory having one or more modules. The modules cause the processor to determine a route that includes a road segment to a destination for an ego vehicle based on (1) an ego vehicle operating parameter that has information regarding a preferred operation of an ego vehicle by an occupant, and (2) a location of a like vehicle with respect to the ego vehicle when the ego vehicle is traveling on at least a portion of the route. The one or more modules also cause the processor to determine a road-segment-level decision for the ego vehicle for the road segment based on the ego vehicle operating parameter, communicate with the like vehicle near the road segment to negotiate the road-segment-level decision to generate a negotiated road-segment-level decision, and maneuver the ego vehicle inside the road segment using the negotiated road-segment-level decision.

Abstract: An information processing device includes a controller that is configured to: acquire driving behavior information as an indication of prudence of a driver in driving a vehicle, acquire a vehicle avatar corresponding to the vehicle, the vehicle avatar having a basic personality set thereto according to information about the vehicle, create a message according to the basic personality of the vehicle avatar based on the driving behavior information, and output the message, that is created, as an utterance of the vehicle avatar.

Abstract: A trip energy estimation system includes a memory and a control module. The memory stores average traffic speed, average traffic acceleration, driver speed, and driver acceleration data. The control module executes an algorithm to estimate an amount of energy for an electric vehicle to travel between two locations. The algorithm includes: determining, based on the average traffic speed data and the average traffic acceleration data, a baseline amount of energy for the electric vehicle to travel between the two locations; determining, based on the driver speed data and the driver acceleration data, a dynamic amount of energy corresponding to at least one of stop and go events, over speeding, or under speeding; and determining a total amount of energy based on the baseline amount of energy and the dynamic amount of energy. The control module, based on the total amount of energy, performs an operation including indicating a trip estimate.

Abstract: A system receives sensor data from computing devices of passengers riding in an autonomous vehicle (AV). Based on the sensor data, the system can determine a position of each of the passengers within the AV. The system determines a next passenger to be picked up by the AV. Based at least in part on the position of each of the passengers within the AV, the system can (i) select a pickup location for the next passenger, and (ii) determine a route for the AV based on the pickup location such that an open seat within the AV is adjacent to the next passenger when the AV arrives at the pickup location for the next passenger. The system can transmit data corresponding to the route to enable the AV to update a current route in order to facilitate a rendezvous with the passenger at the pickup location.

Abstract: A vehicle includes an electric machine that generates torque to move wheels of the vehicle, and a controller. The controller operates the electric machine to limit a maximum speed at which the vehicle is driven in reverse such that the maximum speed depends on a number of detected objects behind the vehicle.

Abstract: Provided is a control device of a working vehicle including a prime mover and a driving mechanism that drives an implement based on power of the prime mover, wherein the control device is configured to obtain a steering angle of the working vehicle, and detect switching between tasks performed by the working vehicle in a farm field, when the steering angle is equal to or greater than a predetermined angle indicating the switching between tasks.

Abstract: A virtual lane estimation system includes a memory device, a sensor and a computer. The memory device is configured to store a road map that corresponds to a portion of a road ahead of a vehicle. The sensor is configured to observe a plurality of trajectories of a plurality of neighboring vehicles that traverse the portion of the road. The computer is configured to initialize a recursive self-organizing map as a plurality of points arranged as a two-dimensional grid aligned with the road map, train the points in the recursive self-organizing map in response to the trajectories, generate a directed graph that contains one or more virtual lanes through the road map in response to the points trained to the trajectories, and generate a control signal that controls navigation of the vehicle through the portion of the road in response to the virtual lanes in the directed graph.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. A processing device may be configured to determine a planned navigational action for the host vehicle; identify a target vehicle in the environment of the host vehicle; predict a resulting distance between the host and target vehicles if the planned action were taken; determine a host vehicle stopping distance based on a braking rate, maximum acceleration capability, and current speed of the host vehicle; determine a target vehicle stopping distance based on a braking rate and current speed of the target vehicle; and continue with the planned navigational action while the predicted distance is greater than a minimum safe longitudinal distance calculated based on the host vehicle stopping distance and the target vehicle stopping distance.

Abstract: A vehicle control method includes recognizing surrounding conditions of a vehicle and controlling a speed and a steering of the vehicle based on the surrounding conditions of the vehicle. The method includes controlling the vehicle to stop before a stop line such that the vicinity of a center or the center in a width direction of the vehicle is aligned with the vicinity of a center or the center in a length direction of the stop line in a width direction of a road.

Abstract: A compaction mitigation system for a work area and a method of mitigating compaction in a work area may include determining a compaction map of the work area having compaction data associated with a plurality of reference points, passing a work tool through the work area at the plurality of reference points, and adjusting the work tool at the plurality of reference points based on the compaction data.

Abstract: Provided is a technology that allows, inter alia, two different patrol-vehicles to be used to enforce parking regulations. Unique systems and methods, inter alia, allow a patrol vehicle or the like to collect parking data pertaining to parked vehicles, and to wirelessly transmit such parking data to a server. A later patrol vehicle or the like can wirelessly receive parking data related to parking events related to its parking enforcement situation and determine whether parking violations, such as overtime or permit sharing violations, are occurring using a combination of parking data generated locally and extraneous parking data collected by the previous patrol vehicle.

Abstract: An autonomous vehicle and a system and method for operating the autonomous vehicle. The system includes a control system and a cognitive system. The control system performs a driving action at the autonomous vehicle. The cognitive system generates the driving action using an evaluation model. The evaluation model is generated by operating the cognitive system in response to a training set of data to generate a planned action for operating the autonomous vehicle by the cognitive system, evaluating the planned action to obtain a system performance grade, and updating the cognitive system based on a comparison of the system performance grade to a human-based performance grade.

Abstract: Methods and systems for training a trajectory prediction model and performing a vehicle maneuver include encoding a set of training data to generate encoded training vectors, where the training data includes trajectory information for agents over time. Trajectory scenarios are simulated based on the encoded training vectors, with each simulated trajectory scenario representing one or more agents with respective agent trajectories, to generate simulated training data. A predictive neural network model is trained using the simulated training data to generate predicted trajectory scenarios based on a detected scene.