6996 results about "Autopilot" patented technology

A navigation and control system including one or more position sensors configured to generate position signals indicative of the location and heading of a vehicle. The system includes one or more operation control mechanisms having inputs and producing outputs which control an operation of the vehicle and includes a self-contained autonomous controller disposed remote from the operation control mechanisms. The autonomous controller includes a processor configured to receive the position signals from the position sensors and to generate operation control signals defining an updated travel path for the vehicle, and a programmable interface providing communication among the position sensors, the operation control mechanisms, and the processor. The programmable interface is configured to normalize inputs to the processor from the position sensors and to generate compatible operation control signals applied as the inputs to the operation control mechanisms, whereby the self-contained autonomous controller is configurable for operation with a variety of different sensors and different operation control mechanisms.

A navigation and control system including a sensor configured to locate objects in a predetermined field of view from a vehicle. The sensor has an emitter configured to repeatedly scan a beam into a two-dimensional sector of a plane defined with respect to a first predetermined axis of the vehicle, and a detector configured to detect a reflection of the emitted beam from one of the objects. The sensor includes a panning mechanism configured to pan the plane in which the beam is scanned about a second predetermined axis to produce a three dimensional field of view. The navigation and control system includes a processor configured to determine the existence and location of the objects in the three dimensional field of view based on a position of the vehicle and a time between an emittance of the beam and a reception of the reflection of the emitted beam from one of the objects.

The present invention relates to a helicopter having a modular airframe, with multiple layers which can be connected easily, the layers which house the electronics (autopilot and navigation systems), batteries, and payload (including camera system) of the helicopter. The helicopter has four, six, and eight rotors, which can be easily changed via removing one module of the airframe. In one embodiment, the airframe has a vertical stacked appearance, and in another embodiment, a domed shape (where several of the layers are stacked internally). In one embodiment, there is a combination landing gear and camera mount. The helicopter allows for simple flight and usage by remote control, and non-remote control, users.

Methods for personalized driving of autonomously driven vehicles include: (a) downloading at least a portion of a driving profile to a vehicle, wherein the vehicle is configured for autonomous driving and wherein the driving profile is associated with a specific driver; and (b) executing one or a plurality of operating instructions prescribed by the driving profile, such that the vehicle is operable in a driving style imitative of the specific driver. Apparatuses for personalized driving of autonomously driven vehicles are described.

Architecture for a multimodal, multiplatform switching, unmanned vehicle (UV) swarm system which can execute missions in diverse environments. The architecture includes onboard and ground processors to handle and integrate multiple sensor inputs generating a unique UV pilot experience for a remote drone pilot (RDP) via a virtual augmented reality cockpit (VARC). The RDP is monitored by an operational control system and an experienced control pilot. A ground processor handles real-time localization, forwarding of commands, generation and delivery of augmented content to users, along with safety features and overrides. The UVs onboard processors and autopilot execute the commands and provide a redundant source of safety features and override in the case of loss of signal. The UVs perform customizable missions, with adjustable rules for differing skill levels. RDPs experience real-time virtual piloting of the UV with augmented interactive and actionable visual and audio content delivered to them via VARC systems.

A method for automated lane centering and/or lane changing purposes for a vehicle traveling on a roadway that employs roadway points from a map database to determine a reference vehicle path and sensors on the vehicle for detecting static and moving objects to adjust the reference path. The method includes reducing the curvature of the reference path to generate a reduced curvature reference path that reduces the turning requirements of the vehicle and setting the speed of the vehicle from posted roadway speeds from the map database. The method also includes providing multiple candidate vehicle paths and vehicle speeds to avoid the static and moving objects in front of the vehicle.

A roadgraph may include a graph network of information such as roads, lanes, intersections, and the connections between these features. The roadgraph may also include one or more zones associated with particular rules. The zones may include locations where driving is typically challenging such as merges, construction zones, or other obstacles. In one example, the rules may require an autonomous vehicle to alert a driver that the vehicle is approaching a zone. The vehicle may thus require a driver to take control of steering, acceleration, deceleration, etc. In another example, the zones may be designated by a driver and may be broadcast to other nearby vehicles, for example using a radio link or other network such that other vehicles may be able to observer the same rule at the same location or at least notify the other vehicle's drivers that another driver felt the location was unsafe for autonomous driving.

Aspects of the present disclosure relate generally to modeling a vehicle's view of its environment. This view need not include what objects or features the vehicle is actually seeing, but rather those areas that the vehicle is able to observe using its sensors if the sensors were completely un-occluded. For example, for each of a plurality of sensors of the object detection component, a computer may an individual 3D model of that sensor's field of view. Weather information is received and used to adjust one or more of the models. After this adjusting, the models may be aggregated into a comprehensive 3D model. The comprehensive model may be combined with detailed map information indicating the probability of detecting objects at different locations. A model of the vehicle's environment may be computed based on the combined comprehensive 3D model and detailed map information and may be used to maneuver the vehicle.

The present invention relates to an apparatus and method for performing cooperative autonomous driving between a vehicle and a driver. For this, a cooperative autonomous driving apparatus according to the present invention includes a driver state determination unit for determining a state of a driver and calculating the state of the driver as a risk index. An autonomous driving control unit classifies section characteristics of respective sections included in a path to a destination corresponding to the driver based on section data stored in a database (DB), and controls autonomous driving of a vehicle in which the driver is riding, based on a driving environment recognized for the path to the destination corresponding to the driver. A driving control determination unit determines driving modes of the respective sections included in the path based on the state of the driver and the section characteristics.

The invention discloses a trajectory tracking control method used for an automatic driving robot of a vehicle, which relates to electronic control technologies of vehicles. The desired trajectory of a vehicle is described in a data point mode to obtain the current position information of the vehicle; humanoid driving is carried out according to the current driving direction at the current vehicle position to preview a distance ahead; the position obtained by previewing is compared with the desired trajectory to determine the lateral position deviation, the angle deviation and the vehicle speed deviation of the coordinate position obtained by previewing relative to the desired trajectory; then, the vehicle direction is jointly controlled by a trajectory tracking and speed controller according to position deviation and direction deviation; and according to vehicle speed deviation, the control quantities of an accelerator pedal and a brake pedal are determined with a fuzzy control method. The trajectory tracking control method is suitable for controlling the trajectory tracking of any trajectory, various vehicle types and various working conditions, and an automatic driving robot performs the control. The trajectory tracking control method has high precision and good repeatability and has an important meaning for guaranteeing the quality of vehicle reliability experiments and improving experiment safety.

The present invention relates to a reduced scale industrial helicopter, with an integrated automatic flight control system, that includes core autopilot functions, GPS management, and full-function navigation systems. The autopilot technology includes rapid launch capability, real-time in-flight switching between one or more of a) remote control, b) autopilot-directed, c) ground station controlled, and d) home modes, and is upgradeable. The helicopter is used for high or low altitude surveillance, and can handle various payloads, including photographic missions. The helicopter may include onboard batteries and/or a unique battery unit disposed beneath the helicopter, and includes autonomous features such as automatic takeoff, automatic landing, safety return to home base, and predetermined mission plans.

Provided is a technology for helping safe driving and realizing automatic driving of vehicles, or for counting the number of passing vehicles on the road or monitoring those passing vehicles for their driving. Using a plurality of cameras mounted in a vehicle or provided above a road, even if the relationship between the road plane and the respective cameras constantly changes in relative position or posture due to camera vibration or a change in road tilt, any obstacles located on the road such as other vehicles ahead, parked vehicles, and pedestrians on the road are detected without confusing those with textures including white lines, road signs, paint, road stains, and shadows of roadside objects, all of which do not disturb vehicle driving. An obstacle detection device 10 is structured by an image input section 11 for receiving images from a plurality of image pick-up devices 101, a correspondence detection section 12 for finding a plurality of pairs of corresponding points from the received right and left images, the slope degree calculation section 13 for calculating a slope degree of a plane including the corresponding points, and a result determination section 14 for determining as there being an obstacle when the calculated slope degree is larger than a predetermined value.

Systems and methods for efficiently addressing technical and privacy/authorization obstacles associated with tracking of individuals in a vehicle, and enabling route-based analysis to determine driving behavior, socio-demographics, future profitability, and interests of individuals or self-driving systems. Driving information is collected using a device associated with a driver and a vehicle or using data collected by systems of self-driving vehicles. The frequency and methods used for the collection of driving information can be modified based on location and movement of the device and based on previous classification of the driver or self-driving system, thereby enabling efficient use of bandwidth and battery and increasing accuracy of the classification. The driving information is encoded and transmitted to a server, where future typical route segments that the driver is likely to travel are predicted, and the driver, or the self-driving system, is classified into one or more groups based on the encoded driving information.

The invention relates to a system and method for generating a lane-level navigation map of an unmanned vehicle based on multi-source data. The lane-level navigation map comprises an offline global map part and an online local map part. According to an offline module, within a target region where the unmanned vehicle runs, original road data is acquired through satellite photos (or aerial photos), a vehicle sensor (laser radar and a camera) and a high-precision integrated positioning system (a global positioning system and an inertial navigation system), then the original road data is subjected to offline processing, multiple kinds of road information are extracted, and finally the road information extracting results are fused to generate the offline global map. The offline global map is stored through a layered structure. According to an online module, when the unmanned vehicle automatically drives in the target region, the road data in the offline global map is extracted according to real-time positioning information, and the online local map with the vehicle as the center within the fixed distance range is drawn. The system and method can be applied to fusion sensing, high-precision positioning and intelligent decisions of the unmanned vehicle.

A method and apparatus are provided for determining whether a driving environment has changed relative to previously stored information about the driving environment. The apparatus may include an autonomous driving computer system configured to detect one or more vehicles in the driving environment, and determine corresponding trajectories for those detected vehicles. The autonomous driving computer system may then compare the determined trajectories to an expected trajectory of a hypothetical vehicle in the driving environment. Based on the comparison, the autonomous driving computer system may determine whether the driving environment has changed and/or a probability that the driving environment has changed, relative to the previously stored information about the driving environment.

A vehicle has a plurality of control apparatuses, a user input, a geographic position component, an object detection apparatus, memory, and a display. A processor is also included and is programmed to receive the destination information, identify a route, and determine the current geographic location of the vehicle. The processor is also programmed to identify an object and object type based on object information received from the object detection apparatus and to determine at least one warning characteristic of the identified object based on at least one of: the object type, a detected proximity of the detected object to the vehicle, the location of the detected object relative to predetermined peripheral areas of the vehicle, the current geographic location of the vehicle, and the route. The processor is also configured to select and display on the display an object warning image based on the at least one warning characteristic.