Abstract: Disclosed are systems, apparatuses, and methods to enhance control of a drone-load system, including through drone thrusters or load thrusters.

Abstract: Provided are methods for location based parameters for an image sensor, which can include determining the geographic location of the vehicle, adjusting the parameters of the image sensor of the vehicle from a first setting to a second setting based on the geographic location of the vehicle, receiving sensor data associated with the image sensor based on the second setting, and processing the sensor data to generate an image. Systems and computer program products are also provided.

Abstract: Provided is a multi-objective path planning method for an intelligent public transport system, includes: generating a shortest path network topology for connecting bus stops by analyzing a traffic network topology and bus drivable paths; dynamically and evenly allocate bus routes according to passenger waiting periods, road lengths, and the shortest path network topology, to thereby allocate different routes for different buses; and determining an optimal bus route configuration according to average passenger travel distances, passenger experience, and the bus routes. The method can minimize a passenger waiting period while ensuring a high passenger load rate, thereby improving the overall efficiency of the public transport system and the travel experience of passengers.

Abstract: A system for controlling a subject vehicle includes a front detection unit to detect a driving situation of a target vehicle located in front of the subject vehicle; a determination unit to detect reversing of the target vehicle or predict a collision between the target vehicle and the subject vehicle through the front detection unit; and a control unit to, when the reversing of the target vehicle is detected or the collision between target vehicle and the subject vehicle is predicted through the determination unit, generate a warning signal of the subject vehicle or control driving of the subject vehicle so that the subject vehicle avoids the collision with the target vehicle.

Abstract: A autonomous robotic golf caddy which is capable of following a portable receiver at a pre-determined distance, and which is capable of sensing a potential impending collision with an object in its path and stop prior to said potential impending collision.

Abstract: A method for authorizing the flight of an aircraft provided with a hybrid power plant having at least one heat engine and at least one electric motor electrically connected to an electrical energy source comprising several electrical accumulators. The method comprises extracting (STP1), while on the ground, an electrical energy or an electrical power to be extracted from the electrical energy source during a predetermined time period and determining (STP2) for the electrical accumulators, respective initial values of an operating parameter, calculating (STP3) an average value from the initial values, determining (STP4) a minimum value from the measured initial values, issuing an authorization when a difference between the average value and the minimum value is less than a threshold and a prohibition when said difference between the average value and the minimum value is greater than or equal to the threshold.

Abstract: The on-vehicle security device 10 includes log collection means 11 for collecting communication logs, log analysis means 12 for analyzing the collected communication logs, and control means 13 for having alerting means issue an alert when it is determined that an error occurrence frequency exceeds a predetermined threshold value, based on an analysis result of the communication logs by the log analysis means 12, and blocks a communication path where an error has occurred when a state where the error occurrence frequency exceeds the predetermined threshold value continues after having the alerting means issue the alert.

Abstract: The present invention belongs to the technical field of electric automobiles and particularly relates to a fault tolerance decision-making method and system for sensor failure of a vehicular wheel hub driving system. The method comprises a current sensor failure diagnostic process, a position/velocity sensor failure diagnostic process and a selection process for a wheel hub motor fault tolerance control method. The system comprises a current sensor failure diagnostic module, a position/velocity sensor failure diagnostic module and a selection module for a wheel hub motor fault tolerance control method. The position/velocity sensor failure diagnostic module further comprises a fault tolerance control switching module.

Abstract: A device and method for preventing blind spot collision based on vehicle-to-vehicle communication. The method includes detecting a forward vehicle, requesting vehicle-to-vehicle communication with the forward vehicle, receiving image data and ultrasound data of the forward vehicle, analyzing information about a moving object in a blind spot formed by the forward vehicle, based on the image data and the ultrasound data, calculating a possibility of collision with the moving object based on the information about the moving object, and performing one or both of warning notification and collision avoidance control based on the possibility of collision.

Abstract: A drone loaded with a package takes off from a takeoff device and uses a GPS system to fly to a user house that is a delivery destination of the package as the destination. Further, when the drone approaches the user house that is the destination, the flight of the drones is switched from autonomous navigation using the GPS system to remote control performed by a landing device and an in-house control device installed in the user house. The drone lands on the landing device by remote control from the landing device and the in-house control device, separates the package, and then returns to the warehouse using the GPS system and lands on the takeoff device.

Abstract: An Unmanned Aerial System configured to receive a request from a user and fulfill that request using an Unmanned Aerial Vehicle. The Unmanned Aerial System selects a distribution center that is within range of the user, and deploys a suitable Unmanned Aerial Vehicle to fulfill the request from that distribution center. The Unmanned Aerial System is configured to provide real-time information about the flight route to the Unmanned Aerial Vehicle during its flight, and the Unmanned Aerial Vehicle is configured to dynamically update its mission based on information received from the Unmanned Aerial System.

Abstract: Systems, methods, and other embodiments described herein relate to identifying a user of a vehicle. In one embodiment, a method includes determining an activity of the user using one or more mobile devices. The method includes detecting an event in the vehicle using one or more vehicle sensors. The method includes identifying the user based on a relationship between the activity and the event.

Abstract: A first vehicle in traffic can use machine learning and artificial intelligence to detect an imminent collision with a second vehicle or other object. A well-trained AI algorithm can select a sequence of actions (braking, swerving, or accelerating—depending on the specific kinetics) to avoid the collision if possible, and to reduce or minimize the harm if unavoidable. With proper training, the AI model may also infer the intent and future actions of the second vehicle, as well as potential interference of other traffic agents. A good algorithm can also infer the intent of the driver of the first vehicle, for example based on prior driving habits. The AI algorithm may be implemented in a processor on the subject vehicle, potentially in communication with another processor at a fixed site such as a local access point or a central supercomputer.

Abstract: A trailer assist system includes a controller configured to determine whether a trailer is in a jackknife condition based on an angle of the trailer relative to a vehicle. Determination is made whether the jackknife condition can be corrected by utilizing vehicle brakes, trailer brakes, or vehicle steering.

Abstract: Vehicles can be operated according to an artificial intelligence model contained in an on-board processor. The AI model can analyze sensor data, such as visible or infrared images of traffic, and determine when a collision is possible, whether it has become imminent, and whether the collision is avoidable or unavoidable using sequences of accelerations, braking, and steering. The AI model can also select the most appropriate sequence of actions from a large plurality of calculated sequences to avoid the collision if avoidable, and to minimize the harm if unavoidable. The AI model can also cause a processor to actuate linkages connected to the throttle (or electric power control), brakes (or regenerative braking), and steering to implement the selected sequence of actions. Thus the collision can be avoided or mitigated by an ADAS system or a fully autonomous vehicle.

Abstract: A mobile object control device includes a recognizer that recognizes a surroundings situation of a mobile object, and a controller that controls an acceleration and deceleration of the mobile object based on the surroundings situation recognized by recognize, and the controller sets a risk area at a first reference position based on an end of an obstacle present around the mobile object in a traveling direction of the mobile object when a predicted trajectory in which another mobile object is estimated to move and a future trajectory of the mobile object interfere with each other, and the length of a target area corresponding to the predicted trajectory in a predicted trajectory direction is equal to or larger than a predetermined length with respect to the target area difficult for the recognizer to recognize due to the obstacle, and controls at least the speed of the mobile object based on the set risk area.

Abstract: Computer implemented methods and systems for deploying response vehicles based on a virtual environment. A server may obtain a virtual model of an overall region wherein the virtual model was generated based upon a plurality of images captured by a remote imaging vehicle. The server may then provide the virtual model to a user electronic device for rendering in a virtual environment. The server may then determine a target location within the overall region at which the response vehicle should be deployed and generate a route for the response vehicle to follow. The route may be based on damage indicated by the virtual model of the overall region. The server may then provide the route to the user electronic device and/or the response vehicle.

Abstract: A vehicle control device includes a processor including hardware, the processor being configured to: stop a smart key function of a vehicle when a first state in which an intensity of a radio wave received from a predetermined wireless communication device is a threshold or more has continued for a predetermined time.

Abstract: An apparatus for assisting driving of a vehicle includes: a radar mounted on the vehicle to have a front field of view of the vehicle and configured to acquire detection data; and a controller including a processor, configured to process the detection data, and configured to identify an estimated collision time between the vehicle and a first preceding vehicle, located in front of the vehicle, based on processing of the detection data, and to control a braking system of the vehicle to brake the vehicle, in response to the estimated collision time being less than a reference time, wherein the controller is configured to increase a reference time for braking the vehicle, based on an acceleration and a travelling speed of the first preceding vehicle and an acceleration and a travelling speed of a second preceding vehicle, located in front of the first preceding vehicle.

Abstract: An autonomous vehicle including a vehicle propulsion system, a braking system, a steering system, and a computing system. The computing system includes a processor and memory that stores computer-executable instructions that cause the processor to select a route for a passenger of the autonomous vehicle for a trip to a destination. The route for the passenger is selected from a plurality of potential routes. The route for the passenger is selected based on a preference of the passenger and aggregate ride quality feedback from passengers of autonomous vehicles in a fleet at locations along the potential routes to the destination. The processor is further configured to control at least one of the vehicle propulsion system, the braking system, or the steering system to move the autonomous vehicle along the route as selected for the trip to the destination.