Abstract: Aspects of the present invention relate to a control system and method for controlling a state of charge of energy storage means of a hybrid electric vehicle, the control system comprising one or more electronic controllers, the one or more electronic controllers configured to: receive destination data; determine a route to be travelled by the vehicle, the route comprising one or more characteristics, in dependence on the received destination data; and control the state of charge of the energy storage means in dependence on the route to allow the vehicle to travel an end portion of the route in an electric-only mode.

Abstract: A method of calibrating an advanced driver assistance system (ADAS) for a vehicle comprises: receiving first information by a mobile device that has a camera and a light detection and ranging (LiDAR) device, the first information comprising a three-dimensional (3D) specification of physical dimensions for the vehicle having the ADAS; scanning, by the mobile device, the vehicle and a physical target for the ADAS, the scanning performed with the camera and the LiDAR device after receiving the first information, the scanning generating frame data; tracking, by the mobile device, the vehicle in the frame data using the first information; generating, by the mobile device, an output indicating whether the physical target is positioned at a target location for the ADAS; and initiating, after the output is generated, a calibration process of the ADAS that uses the physical target.

Abstract: Given that an intersection point of a straight line, which connects a relatively moving body and an eyeball reference position of a driver of a vehicle, and a virtual surface, which includes a virtual display region and is wider than the virtual display region, is defined as a first intersection point, and an intersection point between the virtual display region and a straight line, which connects the eyeball reference position and an AR image, is defined as a second intersection point, a position of the second intersection point is controlled such that a second distance, which is a distance between the first intersection point and the second intersection point, is longer when a first distance, which is a distance between the relatively moving body and the vehicle, is longer than a predetermined value, than when the first distance is the predetermined value.

Abstract: Techniques for identifying road segments associated with an object trajectory are discussed herein. A computing device can implement a model that extracts points from an object trajectory and identifies road segments along a path between the extracted points. The model can determine a set of road segments for the path based on searching a graph. The graph can comprise nodes to represent different road segments, and a graph search algorithm can output road segments representing a shortest path between the nodes associated with the extracted points. The road segments can be used by a vehicle computing device for predicting vehicle actions to control a vehicle.

Abstract: In an aspect, a method for monitoring recurrent aircraft faults is disclosed. The method comprises (a) retrieving a plurality of data entries from an aircraft. The plurality of data entries may comprise semantic information about a plurality of recurrent aircraft faults. The method also comprises (b) using a trained natural language processing algorithm, establishing a plurality of pairwise relations of the plurality of data entries, a pairwise relation corresponding to a recurrent fault of the plurality of recurrent aircraft faults. The method also comprises (c) generating one or more alerts from the plurality of pairwise relations. Finally, the method comprises (d) presenting the one or more alerts in a user interface.

Abstract: A vehicle includes an autonomous driving system and a vehicle platform that controls the vehicle according to commands from the automated driving system. The autonomous driving system is configured to send to the vehicle platform commands for autonomous driving, which is autonomous driving of the vehicle alone, and commands for remote driving, which is automatic driving based on signals from outside the vehicle. The vehicle is configured to be operable in an automatic mode, in which the vehicle platform is under control of an autonomous driving system, and in a manual mode, in which the vehicle is under control of a driver. When the vehicle is not in remote operation, switching to remote operation is allowed in manual mode and prohibited in automatic mode.

Abstract: The present invention provides a vehicle control apparatus (10) including: an acquisition unit (11) that acquires internal situation information indicating a situation inside a vehicle; a detection unit (12) that detects a person who satisfies a predetermined condition inside the vehicle, based on the internal situation information; a decision unit (13) that decides a driving mode of the vehicle, based on whether the person who satisfies the predetermined condition is detected; a processing unit (14) that performs processing of the decided driving mode; and a notification unit (15) that notifies an outside of the vehicle of the decided driving mode.

Abstract: A safety system for an autonomous electronic vehicle having a body and a platform. The safety system includes at least one mechanical connection unit and a safety control module. The mechanical connection unit connects the body to the platform, and is transitionable between a first state, in which the body is attached to the platform at the mechanical connection unit, and a second state, in which the body is released from the platform at the mechanical connection unit. The safety control module is programmed to prompt the mechanical connection unit to transition from the first state to the second state under circumstances of an imminent collision event. In some embodiments, the safety control module is programmed to determine a safety path for passengers within the body and/or others in the collision zone based on conditions surrounding the vehicle.

Abstract: Managing a vehicle body including computing an efficiency value of a monitoring target, the monitoring target being the power train or a part or a subsystem of the power train, on the basis of information about the vehicle body, the information being sensed by a sensor provided to the vehicle body. An output terminal that outputs the efficiency value of the monitoring target. Determining whether a load parameter is larger than a load determination value set in advance, and computing the efficiency value of the monitoring target on the basis of input energy and output energy of the monitoring target on condition that the load parameter be larger than the load determination value, and recording the computed efficiency value of the monitoring target.

Abstract: Systems and techniques for whether to associate predicted trajectories of objects detected in an environment with candidate vehicle trajectories are described. A vehicle traversing an environment may have several candidate actions that may be used to control the vehicle. Predicted object trajectories may be excluded from further processing based on a relevancy score determination. Various factors may be evaluated to determine the relevancy score associated with a predicted object trajectory and candidate vehicle action pair that indicated the impact of the predicted object trajectory and candidate vehicle action. A resultant trajectory may be determined using only those relevant predicted object trajectories.

Abstract: Systems and methods for terminal procedure prediction for flight planning include obtaining, at a processor, input data corresponding to an upcoming flight of an aircraft; obtaining, at the processor, predicted data corresponding to the upcoming flight; and processing, at the processor, the input data and the predicted data to obtain a terminal procedure prediction associated with the upcoming flight, the terminal procedure prediction at least partially based on flight plans of historical flights having conditions similar to the input data and the predicted data.

Abstract: A method of adaptively tuning parameters in action planning for automated driving of a vehicle to a destination is provided. The method comprises receiving sensor data from a vehicle sensor, lane data of a road plan to the destination, and a plurality of first hyperparameters in a reinforcement learning agent at an initial state and an initial timestamp. The reinforcement learning agent has a planning policy. The method comprises adjusting the plurality of first hyperparameters via the planning policy having at least one first activation function to define an output defining a plurality of second hyperparameters. The method comprises determining a baseline trajectory action based on a trajectory reward value at a final state and a final timestamp. The method comprises modifying the baseline trajectory action between the initial state and the final state to define a refined trajectory action and controlling the vehicle based on the refined trajectory action.

Abstract: Systems and methods for detecting and tracking objects are provided. In one example, a computer-implemented method includes receiving sensor data from one or more sensors. The method includes inputting the sensor data to one or more machine-learned models including one or more first neural networks configured to detect one or more objects based at least in part on the sensor data and one or more second neural networks configured to track the one or more objects over a sequence of sensor data. The method includes generating, as an output of the one or more first neural networks, a 3D bounding box and detection score for a plurality of object detections. The method includes generating, as an output of the one or more second neural networks, a matching score associated with pairs of object detections. The method includes determining a trajectory for each object detection.

Abstract: The present disclosure provides for alternate destination selection and management onboard a vehicle. The alternate destination selection includes receiving, at a vehicle management system (VMS) an alternate destination request selecting and determining a plurality of navigation estimates based on the request and updating the VMS according to the alternate destination and the plurality of navigation estimates.

Abstract: A method of controlling a data center having a cold air cooling system, and at least one containment structure, comprising: determining a minimum performance constraint; determining optimum states of the cold air cooling system, a controlled leakage of air across the containment structure between a hot region and a cold air region, and information technology equipment for performing tasks to meet the minimum performance constraint, to minimize operating cost; and generating control signals to the cold air cooling system, a controlled leakage device, and the information technology equipment in accordance with the determined optimum states.

Abstract: Systems and methods for providing manual driving capabilities to an operator of a vehicle are provided. The systems include a screen within the vehicle configured to selectively transition between closed and open configurations by moving the screen and thereby cover or uncover, respectively, an opening to a compartment of the vehicle, a manual driving apparatus configured to selectively transition between concealed and exposed configurations by moving the manual driving apparatus to be disposed within the compartment or disposed at least partially outside of the compartment, respectively, and a controller configured to transition between autonomous and manual operating modes, wherein while in the autonomous operating mode an automated driving system is configured to perform driving tasks of the vehicle without human intervention, wherein while in the manual operating mode the vehicle is configured to perform the driving tasks based on human interaction with the manual driving apparatus.

Abstract: An interconnected network including a plurality of autonomous vehicles is described. Each autonomous vehicle can include an immersion cooling system. The network can include a central server for determining whether there has been any network disruption. In the event of the network disruption (or when a network disruption is predicted), the central server can deploy at least one vehicle to the area to maintain the network connectivity. Upon receiving the instructions, the vehicle can drive to the area. The central server can use an artificial intelligence algorithm to make the prediction.

Abstract: Maneuver Coordination Services (MCS) may be used in various implementations of vehicular networks. Emergency Group Maneuver Coordination (EGMC) may be used for detected unexpected safety-critical situations (USCS) on the road. EGMC provides cooperative maneuver coordination among a group of vehicles to ensure safer collective actions in the case of USCS. An MCS and EGMC may utilize a Maneuver Coordination Message (MCM) based on procedures for the Generation and Transmission of MCM with reduced communication overhead.

Abstract: A vehicle comprising a plurality of seats, a plurality of Bluetooth® modules to which Bluetooth® identification information, different from each other, is assigned, configured to perform pairing with a plurality of user terminals, wherein each of the plurality of Bluetooth® modules corresponds to each of the plurality of seats, a memory configured to store terminal identification information assigned to each of the plurality of user terminals and the Bluetooth® identification information assigned to each of the plurality of Bluetooth® modules, and a processor configured to be connected to the plurality of Bluetooth® modules and the memory.

Abstract: This disclosure is directed to techniques for collaborative driving during flagger interactions. For instance, a vehicle navigating along a path may detect a flagger using sensor data. Based on detecting the flagger, the vehicle may stop at a location that is at least a threshold distance from the flagger and also send the sensor data to one or more computing devices associated with a teleoperator. The vehicle may then receive a guidance from the one or more computing devices, where the guidance is to proceed past the location. In some examples, the vehicle receives the guidance when the teleoperator activates and holds an input device. Based on receiving the guidance, the vehicle may begin to navigate passed the location and by the flagger. In some examples, the vehicle continues to navigate as long as the teleoperator activates the input device.