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: 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 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 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: 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 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: 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: 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: 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 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.

Abstract: Disclosed are methods, systems, and non-transitory computer readable memory for dynamic control of suspension systems of vehicles. For instance, a method may include: obtaining a suspension control feature map; determining an initial location of a vehicle; determining a region of the suspension control feature map that corresponds to the initial location of the vehicle; determining a set of suspension control trigger conditions based on the region of the suspension control feature map; determining a current location of the vehicle; determining whether a first suspension control trigger condition of the set of suspension control trigger conditions is satisfied based on the current location; and in response to determining the first suspension control trigger condition is satisfied, instructing an adjustable suspension of the vehicle to transition from a first adjustable suspension setting to a second adjustable suspension setting.

Abstract: An autonomous or semi-autonomous vehicle can detect an imminent collision according to sensor data and responsively select an action or a sequence of actions to avoid the collision if avoidable, and to reduce or minimize the harm of the collision if unavoidable. An artificial intelligence model may be used to process the sensor data, detect the imminent collision, and select the collision avoidance action or actions, or the harm-reduction or harm-minimization action or actions. For example, when the collision is considered unavoidable, the vehicle may apply the brakes to reduce the vehicle's speed and therefore, the severity of the impact. When the collision is considered avoidable, the vehicle may automatically apply steering to avoid the potential collision, such as when the vehicle is departing a lane and may collide with a vehicle traveling in the same or opposite direction.

Abstract: A computer-implemented method comprises: detecting, by an assisted-driving system that is currently controlling motion of a first vehicle, a traffic event external to the first vehicle; and providing, by the assisted-driving system and in response to detecting the traffic event, confirmation to a passenger in the first vehicle that the assisted-driving system is handling the traffic event, the confirmation including a physical feedback perceptible to the passenger.

Abstract: An agricultural navigation system and method for an autonomous vehicle (AV) is described. The agricultural navigation system includes a system controller, a localization module associated with the system controller, and an environmental sensor. The system controller determines an AV positional pose that identifies the location of the AV. The system controller determines a relative body frame of reference (RBF) that is associated with the AV positional pose. The environmental sensor detects an asset feature in the AV environment. The asset feature includes an agricultural asset feature having a crop row. The system controller identifies at least one asset feature frame (AFF) that includes a coordinate system originating at the asset feature. The localization module determines the AV positional pose in the coordinate system of the AFF. The system controller transforms the AV positional pose from the RBF coordinate system to the coordinate system of the AFF.

Abstract: A method and system for adjusting vehicle operations. Wheel sensors are disposed to detect parameters associated with treads of wheels and parameters associated with road conditions. An electronic control unit receives information from the wheel sensors. The electronic control unit determines a performance metric of the wheels based on the parameters associated with treads. The electronic control unit receives determine adjustments for operation of the vehicle based on the performance metric and the parameters associated with road conditions.

Abstract: A lift device includes multiple electrical components and a battery monitoring system. The battery monitoring system includes multiple batteries and a controller. The multiple batteries are configured to power the multiple electrical components. The controller is configured to obtain sensor data from the batteries. The controller is also configured to determine a state of charge of the batteries for open circuit conditions based on the sensor data. The controller is configured to determine a state of charge of the batteries for load conditions based on the sensor data. The controller is configured to determine a state of charge of the batteries for charging conditions based on the sensor data. The controller determines an overall state of charge of the batteries based on the state of charge for open circuit conditions, the state of charge of the batteries for load conditions, and the state of charge for charging conditions.

Abstract: A robot generates a cell grid (e.g., occupancy grid) representation of a geographic area. The occupancy grid may include a plurality of evenly sized cells, and each cell may be assigned an occupancy status, which can be used to indicate the location of obstacles present in the geographic area. Footprints for the robot corresponding to a plurality of robot orientations and joint states may be generated and stored in a look-up table. The robot may generate a planned path for the robot to navigate within the geographic area by generating a plurality of candidate paths, each candidate path comprising a plurality of candidate robot poses. For each candidate robot pose, the robot may query the look-up table for a corresponding robot footprint to determine if a collision will occur.