1795 results about "Vehicle dynamics" patented technology

A system and method for providing steering control for lane changing and lane centering purposes in an autonomous or semi-autonomous vehicle system. A vehicle vision system calculates roadway lane marking information, such as lateral offset, yaw angle and roadway curvature with respect to the vehicle's centered coordinate system. The roadway is then modeled as a second order polynomial equation. The method then predicts roadway lateral position and yaw angle over a pre-defined lane change completion time using a vehicle dynamic model. The method then compares a predicted vehicle path with a desired vehicle path to generate an error value, and calculates a steering angle command to minimize the error value, where the steering angle command is calculated as a function of vehicle lateral position, vehicle lateral speed, vehicle yaw rate and vehicle yaw angle. The steering angle command is then sent to the vehicle steering system.

The invention discloses an unmanned vehicle dynamic path programming method based on environment uncertainty, comprising steps of establishing a vehicle kinetics model, establishing a dynamic environment model and necessary conditions for anew programming a path, obtaining a motion state original value of the unmanned vehicle, a vehicle motion state original target value and a vehicle motion state candidate target value, generating a candidate path, obtaining an optimal path through selection based on a safety index and a rapidity index, and reprogramming the optimal path of the unmanned vehicle when the unmanned vehicle motion environment satisfies the condition where the new path can be programmed. Compared with the prior art, the invention can not only satisfy the safety requirement for safety driving, but guarantees the driving efficiency under the constraint of the vehicle model. The invention realizes the coordination optimization of the performance index through distribution of various weights, realizes the real-time planning under the condition where a plurality of dynamic obstacles exist, and effectively improves the safety of the unmanned vehicle driving.

A system and method for providing visual assistance through a graphic overlay super-imposed on a back-up camera image for assisting a vehicle operator when backing up a vehicle to align a tow ball with a trailer tongue. The method includes providing camera modeling to correlate the camera image in vehicle coordinates to world coordinates, where the camera modeling provides the graphic overlay to include a tow line having a height in the camera image that is determined by an estimated height of the trailer tongue. The method also includes providing vehicle dynamic modeling for identifying the motion of the vehicle as it moves around a center of rotation. The method then predicts the path of the vehicle as it is being steered including calculating the center of rotation.

A vehicle dynamics behavior reproduction system capable of describing accurately behavior of a motor vehicle in a lateral direction even for nonlinear driving situation includes a vertical wheel force arithmetic means (105), a lateral wheel force arithmetic means (110), a cornering stiffness adaptation means (115), a state space model / observer unit (120), a selector (130), a delay means (135), and a tire side slip angle arithmetic means (125). Vertical wheel forces (FZij) and tire side slip angles (αij) are determined by using sensor information and estimated values while lateral wheel forces (FYij) are determined in accordance with a relatively simple nonlinear approximation equation. The lateral wheel force (FYij) and the tire side slip angle (αij) provide bases for adaptation of cornering stiffnesses at individual wheels. Vehicle motion is accurately described to a marginal stability by using adapted cornering stiffnesses (Cij) and other information.

In a vehicle, an optimal path curvature limited by one or more constraints may be determined. The constraints may be related to lateral jerk and one or more vehicle dynamics constraints. Based on the optimal path curvature, an optimal vehicle path around an object may be determined. The optimal vehicle path may be output to a collision avoidance control system. The collision avoidance control system may cause the vehicle to take a certain path.

An accelerometer sensor equipped device uses GPS and known alignment data to determine the alignment of the accelerometer sub-system when the vehicle is stationary and in motion. The alignment data is determined from known surface information, measured GPS velocity, and measured GPS Heading.

A signal waveform analysis method includes: determining a trend component of an object signal by applying a zero-phase filter to the object signal; determining an oscillatory component of the object signal by removing the trend component from the object signal; determining a wavelet scalogram by performing a wavelet analysis on the oscillatory component; and determining an oscillation period of the object signal with which the wavelet scalogram indicates a maximum. The signal waveform analysis method is employed to evaluate vehicle dynamic characteristics.

A device for a traffic flow assistant for a vehicle includes a surroundings sensor system, which recognizes traffic-relevant objects on a traffic lane, on which the vehicle is traveling, and on at least one further adjacent lane, gaps in the traffic are recognized with the aid of the surroundings sensor system and vehicle-dynamic parameters of the objects are determined and future gaps in the flow of traffic are able to be predicted therefrom. For these recognized gaps and predicted gaps, lane change options are ascertained and, from this and the present and / or predicted presence of gaps in the traffic suitable for changing lanes and the vehicle-dynamic state of the vehicle, a signal for the lane selection is generated, which is dependent on the lane change options and an optimization strategy.

A control system (11) for a vehicle (10) includes vehicle dynamics sensors (35-47) providing a vehicle dynamics signal. Tire monitoring system sensors (20) in each wheel generate tire signals including temperature, pressure and acceleration data. A controller (26) communicates with the tire monitoring system sensors (20) and at least one vehicle dynamics sensor, and generates a roadway surface condition estimation value as a function of the multi-axis acceleration data of the tire signals. The roadway surface condition estimation value is transmitted to a suspension control system (33) to adjust the vevhicle suspension characteristics in response to the roadway surface condition estimation value.

A system for estimation vehicle dynamics, including vehicle position and velocity, using a stationary object. The system includes an object sensor that provides object signals of the stationary object. The system also includes in-vehicle sensors that provide signals representative of vehicle motion. The system also includes an association processor that receives the object signals, and provides object tracking through multiple frames of data. The system also includes a longitudinal state estimation processor that receives the object signals and the sensor signals, and provides a correction of the vehicle speed in a forward direction. The system also includes a lateral state estimation processor that receives the object signals and the sensor signals, and provides a correction of the vehicle speed in the lateral direction.

A GNSS integrated multi-sensor guidance system for a vehicle assembly includes a suite of sensor units, including a global navigation satellite system (GNSS) sensor unit comprising a receiver and an antenna. An inertial measurement unit (IMU) outputs vehicle dynamic information for combining with the output of the GNSS unit. A controller with a processor receives the outputs of the sensor suite and computes steering solutions, which are utilized by vehicle actuators, including an automatic steering control unit connected to the vehicle steering for guiding the vehicle. The processor is programmed to define multiple behavior-based automatons comprising self-operating entities in the guidance system, which perform respective behaviors using data output from one or more sensor units for achieving the behaviors. A GNSS integrated multi-sensor vehicle guidance method is also disclosed.

A control system (11) for a vehicle (10) includes vehicle dynamics sensors (35-47) providing a vehicle dynamics signal. Tire monitoring system sensors (20) in each wheel generate tire signals including temperature, pressure and acceleration data. A controller (26) communicates with the tire monitoring system sensors (20) and at least one vehicle dynamics sensor, and generates a tire abnormality value as a function of the multi-axis acceleration data of the tire signals. Tire multi-axis acceleration data is also used to detect a roadway departure, and an oversteer or understeer event.

A GNSS integrated multi-sensor guidance system for a vehicle assembly includes a suite of sensor units, including a global navigation satellite system (GNSS) sensor unit comprising a receiver and an antenna. An inertial measurement unit (IMU) outputs vehicle dynamic information for combining with the output of the GNSS unit. A controller with a processor receives the outputs of the sensor suite and computes steering solutions, which are utilized by vehicle actuators, including an automatic steering control unit connected to the vehicle steering for guiding the vehicle. The processor is programmed to define multiple behavior-based automatons comprising self-operating entities in the guidance system, which perform respective behaviors using data output from one or more sensor units for achieving the behaviors. A GNSS integrated multi-sensor vehicle guidance method is also disclosed.

In a method for identifying a trailering mode in the context of a towing vehicle, in particular as part of a vehicle dynamics control system having a trailer roll logic function for stabilizing the combination of towing vehicle and trailer, that identification of the trailering mode is accomplished by a comparison of an actual signal characterizing the vehicle state with a corresponding target signal.

Various embodiments of the present invention are directed to a fleet management system configured for capturing and evaluating vehicle telematics data, such as data captured from one or more vehicle telematics devices indicative of one or more vehicle dynamics, and service data, such as data captured from one or more portable data acquisition devices indicative of one or more service dynamics. In certain embodiments, the fleet management system is configured to associate captured vehicle telematics data with captured service data based on the contextual attributes of each, such as the time, date, and location of data capture. By synching the vehicle telematics data to the service data, the operational data can be uniquely assessed for various operational efficiencies.

A system and method relate to estimating a driver intention for driver assistance systems control, including an analysis device that receives data from each of a vehicle environment sensor, a vehicle dynamics sensor, and a driver attributes sensor, such that the analysis device makes a prediction of the driver intention based on the received data. A control device controls a vehicle and a driver partially based on the predicted driver intention.

A vehicle includes a semi-active suspension including suspension dampers controllably adjustable in accordance with electronic stability control commands and ride and handling commands. Vehicle steering response states, turning direction states and vehicle dynamics states are binary coded in respective state variables and suspension control calibrations are binary coded in calibration words. Integrity and security of state variables and calibration words are ensured in efficient binary digit resource allocation schemes.

The invention discloses an optical flow-based four-rotor unmanned aerial vehicle flight control method. The method comprises the following steps: calculating optical flow information by utilizing an image pyramid-based Lucas. Canard method; processing the optical flow information by adopting a Kalman filtering method; performing data fusion on an optical flow and an attitude angle, and calculating the horizontal displacement of an unmanned aerial vehicle; and designing a proportional-differential controller, including determining a four-rotor unmanned aerial vehicle dynamic model and designing a control algorithm. By the optical flow-based four-rotor unmanned aerial vehicle flight control method disclosed by the invention, the horizontal position information of the unmanned aerial vehicle is calculated by fusing the image information and the attitude angle information acquired by utilizing an airborne camera; and the position of a small unmanned aerial vehicle is controlled by taking the unmanned aerial vehicle horizontal position information as the feedback information of an outer ring PD (proportional-differential).

A method for controlling vehicle dynamics includes acquiring steering torque data indicative of forces acting on at least one tire of a vehicle and acquiring image data by capturing images of an area outside the vehicle. The friction coefficient between a tire of the vehicle and a road surface is determined as a function of vehicle data including at least the steering torque data. The lateral velocity of the vehicle is determined as a function of vehicle data including the steering torque data and / or the image data. A vehicle dynamics control is performed as a function of the lateral velocity and the friction coefficient.

A seat belt tightener has an electrical tightening drive that can be operated in dependence on sensor signals of sensors assessed in an evaluating device. The sensors detect specific vehicle dynamic conditions and transmit electrical indicating signals that are proportional to the detected condition to the evaluating device. The evaluation device compares the signals with allocated threshold values to determine whether or not a potential crash situation is present. Should a potential crash situation be present, the electrical tightening drive is controlled accordingly.

A safety system for a host vehicle includes a pre-crash sensing system generating host vehicle dynamics data, a target vehicle threat assessment, and target vehicle bumper or doorsill location data. A ride-height, Dynamic State Self-Turning (DSST) controller generates a reference ride-height signal as a function of the host vehicle dynamics data, target vehicle threat assessment, and target vehicle bumper or doorsill location data. A Rule-Based Height Regulator (RBHR) controller is feedback communication with an adjustable suspension system, is programmed to continuously adjust the host vehicle ride-height with reference to the ride-height signal, and the host vehicle bumper location to optimize the collision conditions between the two vehicles until just prior to impact.

The invention relates to a trajectory tracking control method and a control device for a driverless vehicle. The control method comprises that error of present driving trajectory and a reference trajectory of a vehicle is calculated by a data preprocessor, a target performance indicator function which is corresponding to the present driving model is obtained simultaneously; an upper layer controller predicts driving states of the vehicle over a period of time through a vehicle dynamics model; transition switch is conducted to function parameters according to switching control algorithm, and a performance indicator function at present sampling time is obtained; optimal controlled quantity of present time is calculated by considering performance requirement constraint conditions at the same time according to predicted driving states and the performance indicator function; a lower layer controller calculates throttle opening, braking pedal pressure and steering wheel turning angle according to the optimal controlled quantity; and the control device comprises the data preprocessor, the upper layer controller and the lower layer controller. Compared with the prior art, the trajectory tracking control method and the control device for the driverless vehicle have the advantages of being good in control effect, high in practicability, capable of improving stability and safety of vehicles and the like.

The invention relates to a dangerous goods transport vehicle dynamic monitoring method and an early warning device. The method comprises the following steps: (1) arranging the early warning device which comprises a collision, rollover and temperature and pressure early warning module, an early warning arithmetic and a threshold value; (2) acquiring the acceleration, the dip angle, the temperature / pressure and image signals of a goods wagon; (3) carrying out calculation on each signal and comparing the signal with the preset threshold value and sending out a corresponding alarm command; (4) displaying alarm information and sending the alarm information to a remote monitoring center through a global position system / general packet radio system (GPS / GPRS); (5) after the alarm is completed, returning to the step (2); and (6) carrying out real-time monitoring on a goods state and the alarm information by a driver through displaying. The corresponding early warning device comprises the GPS / GPRS, cameras, a card reader, a triaxial acceleration sensor, a display screen, an artificial alarm, a buzzing alarm, a temperature / pressure sensor, a dip angle sensor, a secure digital (SD) card storage and an electronic control unit which comprises a microprocessor. The collision, rollover and temperature and pressure early warning module is arranged in the microprocessor. The invention can be used for carrying out real-time monitoring on collision, rollover and temperature and pressure, thereby being beneficial to driving safety and rescue operation.

The invention relates to a vehicle adaptive cruise control system. The vehicle adaptive cruise control system is characterized in that an information collecting unit, a lane changing early warning unit, an adaptive cruise control unit and a vehicle dynamics unit are included; the information collecting unit collects and processes the driving state information of a vehicle, and sends the driving state information to the lane changing early warning unit and the adaptive cruise control unit; the lane changing early warning unit calculates the lane changing minimum safety distance between the own vehicle and surrounding vehicles according to the received effective vehicle movement information, and judges lane changing risks according to a calculation result, an early warning is carried out on the vehicle according to a judgment result, and the judgment result is sent to the vehicle adaptive cruise control unit; the vehicle adaptive cruise control unit selects a control mode according to the vehicle movement information and the lane changing risk judgment result, calculates expected longitudinal acceleration needed by vehicle longitudinal driving, and sends the calculated expected longitudinal acceleration to the vehicle dynamics unit; the vehicle dynamics unit converts the expected longitudinal acceleration into an expected air valve opening degree or braking pressure and sends the expected air valve opening degree or the braking pressure to a vehicle object, and the longitudinal control over the vehicle object is completed.

A method and system for computing lane curvature and a host vehicle's position and orientation relative to lane boundaries, using image data from forward-view and rear-view cameras and vehicle dynamics sensors as input. A host vehicle includes cameras at the front and rear, which can be used to detect lane boundaries such as curbs and lane stripes, among other purposes. The host vehicle also includes vehicle dynamics sensors including vehicle speed and yaw rate. A method is developed which computes lane curvature and the host vehicle's position relative to a lane reference path, where the lane reference path is derived from the lane boundaries extracted from a fusion of the front and rear camera images. Mathematical models provided in the disclosure include a Kalman filter tracking routine and a particle filter tracking routine.

In an unified control of a plurality of active chassis systems a role of each chassis system in applying a corrective net force and a corrective moment to a vehicle is determined. In determining the roles, control influence coefficients and a control authority of each active chassis system is determined. An activation status of each active chassis system based on the control influence coefficients and the control authority is subsequently determined.

A technique for simulating natural behaviors of moving objects is provided. A road traffic simulation apparatus includes a simulation unit for performing a traffic simulation, a display unit for displaying a progress status of the traffic simulation and a control unit for controlling the simulation unit. The simulation unit has a traffic environment database and a moving object model. The moving object model has a driver model in which a driving operation of a driver is modeled and a vehicle dynamics model in which a vehicle behavior is modeled. The driver model performs different processes in parallel in respective different cycles. The results of each cycle process are integrated and output to the vehicle dynamics model.

A method, computer program product and apparatus for estimating the pneumatic pressure of a tire on a vehicle, comprising the steps of: receiving as an input a vehicle status sensor signal; estimating first parameter values of an adaptive filter based on a predetermined model on the vehicle status dependent on the vehicle status sensor signal; calculating, dependent on said first model parameter values, a first tire pressure indication value (delta) being dependent on and indicative of the pneumatic pressure of the tire. Specifically, the vehicle status signal may be an input a wheel angular velocity signal (1) and a lateral movement indication signal; the adaptive filter is based on a predetermined model on lateral vehicle dynamics dependent on the angular velocity of said wheel and the lateral movement of said vehicle.

A vehicle dynamics control device includes: a control unit that executes braking / driving torque control for controlling at least either a braking torque or a driving torque at each wheel based upon at least either external information pertaining to an environment of a vehicle or vehicle information that includes operation input information indicating an operation input by a driver and a vehicle dynamics information. And the operation input information includes a lateral motion operation index pertaining to a lateral motion operation executed to generate a lateral motion in the vehicle; the vehicle dynamics information includes a longitudinal acceleration generated in the vehicle and a lateral motion index indicating a lateral motion occurring in the vehicle; and the control unit determines a handling assurance acceleration limit with a maximum longitudinal acceleration value that assumes a substantially linear proportional relationship with the lateral motion operation index and the lateral motion index over a range in which the lateral motion operation index assumes a value equal to or less than a predetermined value or the lateral motion index assumes a value equal to or less than a predetermined value, and executes the braking / driving torque control by setting the handling assurance acceleration limit as an upper limit to a longitudinal acceleration to be generated in the vehicle under the braking / driving torque control.