823 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 (F_Zij) and tire side slip angles (α_ij) are determined by using sensor information and estimated values while lateral wheel forces (F_Yij) are determined in accordance with a relatively simple nonlinear approximation equation. The lateral wheel force (F_Yij) and the tire side slip angle (α_ij) provide bases for adaptation of cornering stiffnesses at individual wheels. Vehicle motion is accurately described to a marginal stability by using adapted cornering stiffnesses (C_ij) 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 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 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.

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.

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.

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 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 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.

The invention relates to the safe auxiliary driving and intelligent control filed and discloses an automatic control system and a method for preventing side-slipping and side-turnover in a curve road. A vehicle-mounted automatic control system is adopted to control an intelligent vehicle. At first, information about curve road curvature is obtained; secondly, the information about curve road curvature is converted into digital signals and input into a vehicle-mounted micro-processor, curve road critical safe driving speed is calculated by a safe driving speed calculation module and current driving speed is measured by a vehicle-mounted sensor; then the current driving speed and the critical safe driving speed are judged by a safe condition judging module; and finally a system automatic control module is used to control the intelligent vehicle to pass through the curve road smoothly. The automatic control system and the method for preventing side-slipping and side-turnover in the curve road overcome the shortcoming that trajectory tracking is achieved by only using a vehicle kinematics controller, a designed vehicle dynamics controller guarantees straightaway automatic deceleration and practicability and instantaneity of maintenance in the curve road, and calculation accuracy of safe driving speed is improved. By means of dynamics control principles including equivalent control and switching control, the system chattering phenomenon can be restrained effectively and influence of external disturbances can be overcome.

Methods and systems are provided for preventing vehicle theft and carjackings. There is provided a method comprising generating an immobilization profile based on the received current vehicle data, and sending the immobilization profile to the vehicle over a wireless communication network. There is also provided a method generally comprising determining current vehicle data regarding vehicle dynamics and driving conditions, obtaining an immobilization profile based on the received current vehicle data, and adjusting vehicle throttle and/or braking (e.g., friction-type braking, engine braking, etc.) so that vehicle speed approximates the immobilization profile. In one embodiment, the above described methods further comprise communicating with the vehicle operator prior to implementing vehicle immobilization profiles.

The invention discloses a real-time predicting cruise control system based on economical driving. The real-time predicting cruise control system based on economical driving comprises an information collection module, a vehicle dynamics model establishing module and a receding horizon optimal calculation module, wherein the information collection module is used for collecting the driving state information, comprising speed information, the distance between a local vehicle and a front vehicle and the road traffic speed limiting information within the predicted distance, of the local vehicle and the front vehicle; the collected information is transmitted to the vehicle dynamics model establishing module; the vehicle dynamics model establishing module is used for establishing a vehicle dynamics model according to the collected traffic speed limiting information and the driving state information of the front vehicle and the local vehicle, establishing the control problem and determining the optimal objectives and satisfied constraint conditions; and the receding horizon optimal calculation module is used for obtaining the optimal gear sequence and an explicit solution of the optimal engine torque and braking force by optimizing through a method combing a Pontryagin minimum principle with a method of bisection on the basis of the control problem and the constraint conditions proposed by the vehicle dynamics model establishing module, and determining the optimal control rule.

A vehicle crash safety system (10) for an automotive vehicle has a pre-crash sensing system (17) generating a pre-crash signal, a vehicle dynamics detector (32) generating a vehicle dynamics signal, a pre-crash countermeasure system (40), and a pre-crash controller (12) controlling the pre-crash countermeasure system in response to the pre-crash signal and the vehicle dynamics signal. Vehicle crash safety system (10) also has a coordinated safety system controller (44) coupled to the pre-crash controller (12), an early crash sensing system (46) and an early crash countermeasure system (45). The coordinated safety system controller controls the early crash countermeasure in response to the early crash signal and signals from the pre-crash controller.

Provided is a taxi management apparatus, including a taximeter that is installed in a taxi and a driver-side device that is electrically connected to the taximeter. The taximeter generates real-time positioning data and charging data of the taxi. The driver-side device generates dynamic data of a geographic region where the taxi is located and passenger information. A taxi management system is also provided that includes a management platform electrically connected to the driver-side device. The management platform generates vehicle dynamic records and passengers' historical records of the taxi. Fast and convenient taxi management techniques are thus provided through real-time information and statistics, and the passenger efficiency of the taxi and the service quality are improved.

A method of compensating performance of low-cost MEMS (microelectro mechanical systems) inertial sensors in an integrated INS/GPS navigation system for automotive application is disclosed. The proposed method includes velocity and inertial sensor output conditions featured in ground vehicle dynamics. Using the conventional Kalman filter based INS/GPS system, implementation of the prescribed conditions in the present invention additionally to the GPS measurements shows accurate motion tracking even when GPS signal dropouts last for more than 2 minutes. Another aspect of the disclosure is an integrated INS/GPS navigation system which utilizes MEMS based inertial sensors for maintaining high position tracking accuracy even when a GPS signal is lost or unavailable for a long period of time by incorporating the predefined vehicle dynamics conditions when calculating optimum estimates through the Kalman filtering process.

The invention discloses a simulation test bed for an automatic gearbox controller. The test bed comprises a host computer, a real-time object machine, a signal generating and measuring part, an engine electric controller and a plurality of electromagnetic valves, wherein the host computer, the real-time object machine, the engine electric controller and the electromagnetic valves are connected with the signal generating and measuring part respectively; the host computer downloads a simulation model of an automatic transmission vehicle dynamic assembly to the real-time object machine; an engine model, an automatic gearbox model and a whole vehicle dynamic model are integrated in the simulation model of the automatic transmission vehicle dynamic assembly; the simulation model of the automatic transmission vehicle dynamic assembly contains hardware-in-the-loop of the gearbox controller and hardware-in-the-loop of the engine electric controller to form a virtual vehicle gearbox controller develop simulation platform close to a real vehicle, can greatly improve the developing test efficiency of the gearbox controller under a condition of ensuring the software quality of the gearbox controller. The invention also discloses a method for establishing the simulation model of the automatic transmission vehicle dynamic assembly.

An enhanced positioning system includes a vehicle positioning system adapted to transmit an output representing at least one of a current position, a future position, and a path of a vehicle and at least one module in communication with the vehicle positioning system, wherein the at least one module obtains data and information related to at least one of a road attribute, a vehicle dynamics, and a vehicle position, analyzes the data and information, and modifies the output of the vehicle positioning system in response to the analysis of the data and information.

The invention discloses a human-vehicle cooperative steering control method considering real-time allocation of driving rights. The method solves the problem of driving right allocation between drivers and vehicle automatic driving controllers in a human-vehicle cooperative driving process. The method comprises the specific steps of 1, establishing a vehicle dynamics model and a vehicle kinematicsmodel; 2, establishing a vehicle automatic driving controller; 3, establishing a human-vehicle co-driving system model; 4, adopting a model prediction method for designing a human-vehicle co-drivingsystem controller; 5, allocating the driving rights, calculating a control amount, executing the control amount, and achieving the process of controlling a vehicle to steer through the cooperation ofa driver and the vehicle automatic driving controller. By means of the method, under the condition that the vehicle automatic driving controller and the driver drive the vehicle together, the allocation of the steering driving rights of the vehicle automatic driving controller and the driver can be optimized in real time online, and the operation of steering the vehicle is cooperatively completedby the vehicle automatic driving controller and the driver.

The invention provides an accurate and rapid road violation and parking full-automatic snapshot system. The system comprises a front-end monitoring point part, a network transmission part and a centermanagement unit. The vehicle detection technologies mainly comprise the vehicle dynamic detection technology and the vehicle tracking technology. A matched vehicle tracking method is estimated by utilizing the motion detection method based on time-based motion historical images and feature-point optical flows. In order to achieve a better motion detection effect, a shadow removing method is usedand a moving vehicle is more accurately detected. The multi-feature point optical flow detection tracking mode is adopted, and the tracking accuracy is obviously improved in combination with a vehicleshielding solution. The system is powerful in function and is sufficient to meet the requirements of various application occasions. By adopting the system, the functions of the remote control on a ball machine at a front end monitoring point, the previewing of the video monitoring, the automatic detection of the illegal parking, the automatic identification of license plate numbers, the association of the high-definition video recording with illegal behavior videos, the storage and the browsing of the vehicle information, the system management, the remote maintenance and the like are realized.

The invention discloses a whole vehicle energy management method for an internal combustion generating extend range type electric vehicle. The SOC level of a current electric vehicle storage battery is obtained according to voltage and charging and discharging current information of the two ends of the storage battery, a high SOC threshold and a low SOC threshold are set to determine the on-off state of a range extender, and excessive charging and discharging of the storage battery and frequent start and stop of the range extender are avoided. In a range extend mode, drive demanded power of the whole vehicle is obtained according to discharge voltage and current information of a drive motor and a controller of the drive motor, an economic travel line of an internal combustion engine of the range extender and the current SOC value of the storage battery serve as bases, the optimal operation condition of the range extender is reasonably determined, charging and discharging demands of the storage battery are taken into consideration, accordingly whole vehicle dynamic performance and fuel economical efficiency are guaranteed, and meanwhile the energy storage level of the storage battery is optimized.

The invention provides a big data acquisition and processing system and an electric vehicle endurance estimation method based on the same; the system includes a vehicle-mounted sensor, a GPS positioning system, a road information perception system, a cloud data receiving system, a data transmission processing system and an online computing system. Real-time road, traffic, weather and other information are obtained from a cloud server, and then a future driving state of an electric vehicle is forecasted based on the data. According to the vehicle real-time data under an actual driving state, a vehicle dynamic model and a battery model obtained from calculation are more accurate than a traditional vehicle dynamic model obtained based on a physical equation in an electric vehicle driving process. The estimation method can improve the estimation accuracy of the remaining endurance mileage of the networking pure electric vehicle to a great extent. In addition, according to the real-time data obtained from the cloud, a vehicle use strategy is better planned, and the control strategy of the electric vehicle is optimized, so as to improve the service life of the electric vehicle.