Kinematic Model Of Vehicle Research Articles

Model predictive driver model considering the steering characteristics of the skilled drivers

First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers’ steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a β-spline curve.

Mixed local motion planning and tracking control framework for autonomous vehicles based on model predictive control

This study proposes a novel mixed motion planning and tracking (MPT) control framework for autonomous vehicles (AVs) based on model predictive control (MPC), which is made up of an MPC-based longitudinal motion planning module, a feed-forward longitudinal motion tracking module, and an MPC-based integrated lateral motion planning and tracking module. First, given the global reference path and the surroundings information obtained from onboard devices and V2X network, the longitudinal motion planning based on a vehicle kinematics model is applied to determine the local target path, the desired longitudinal acceleration, and velocity considering the longitudinal safety priority. Then, based on the planned target path and longitudinal velocity, the integrated lateral MPT module based on a 2 degree-of-freedom vehicle model is developed to determine the optimal steering angle while satisfying the multiple kinematics and dynamics constraints. Finally, based on the desired longitudinal acceleration and the steering angle, the longitudinal forces of tires are determined. More importantly, co-simulations under several typical scenarios between MATLAB/Simulink and CarSim are conducted, and the results demonstrate excellent performance of the proposed mixed framework in both planning and tracking and also its real-time implementation.

Trajectory Planning and Tracking for Autonomous Vehicle Based on State Lattice and Model Predictive Control

Trajectory planning and tracking control are two keys of collision avoidance for autonomous vehicles in critical traffic scenarios. It requires not only the system functionality, but also strong real-time. In this paper, we integrated trajectory planner and tracking controller for autonomous vehicle to implement trace planning and tracking for obstacle avoidance. The trajectory planner is based on the state lattice approach and the tracking controller is designed based on the model predictive control using the vehicle kinematics model. The simulation shows that the planner can generate smooth trajectories which could be selected as references for the controller. The maximum tracking error is less than 0.2 m when the vehicle speed is below 50 km/h. Additionally, the on field test shows that the test vehicle with this method is capable of following the reference path accurately, even at sharp corners.

Cooperative collision avoidance algorithm for multi-vehicle based on vehicle networking technology

This paper addresses the problem of low success rate of traditional algorithm collision avoidance and poor control of parking distance. The vehicle network system and the vehicle kinematics model are established. The paper describes accurate estimation of the vehicle driving state in tunnel intranet based on vehicle sensor and vehicle GPS data. Constraints are established for fast response disturbances, rapid stabilisation, reduced braking acceleration and deceleration, and safe distances. From the energy point of view, we consider the relative kinetic energy of any two vehicles under the vehicle network, measure the collision risk, establish the objective function, and analyse the relative kinetic energy of all vehicles approaching the preceding vehicle. In this way, a multi-vehicle cooperative collision avoidance method based on vehicle network technology is realised. The experimental results show that the algorithm has strong collision avoidance performance, and the parking distance control is better, which can ensure safety.

Simulation and flight experiments of a quadrotor tail-sitter vertical take-off and landing unmanned aerial vehicle with wide flight envelope

This paper presents the modeling, simulation, and control of a small-scale electric powered quadrotor tail-sitter vertical take-off and landing unmanned aerial vehicle. In the modeling part, a full attitude wind tunnel test is performed on the full-scale unmanned aerial vehicle to capture its aerodynamics over the flight envelope. To accurately capture the degradation of motor thrust and torque at the presence of the forward speed, a wind tunnel test on the motor and propeller is also carried out. The extensive wind tunnel tests, when combined with the unmanned aerial vehicle kinematics model, dynamics model and other practical constraints such as motor saturation and delay, lead to a complete flight simulator that can accurately reveal the actual aircraft dynamics as verified by actual flight experiments. Based on the developed model, a unified attitude controller and a stable transition controller are designed and verified. Both simulation and experiments show that the developed attitude controller can stabilize the unmanned aerial vehicle attitude over the entire flight envelope and the transition controller can successfully transit the unmanned aerial vehicle from vertical flight to level flight with negligible altitude dropping, a common and fundamental challenge for tail-sitter vertical take-off and landing aircrafts. Finally, when supplied with the designed controller, the tail-sitter unmanned aerial vehicle can achieve a wide flight speed envelope ranging from stationary hovering to fast level flight. This feature dramatically distinguishes our aircraft from conventional fixed-wing airplanes.

Neural network adaptive sliding mode control for omnidirectional vehicle with uncertainties

This paper presents a novel neural network adaptive sliding mode control (NNASMC) method to design the dynamic control system for an omnidirectional vehicle. The omnidirectional vehicle is equipped with four Mecanum wheels that are actuated by separate motors, and thus has the omnidirectional mobility and excellent athletic ability in a narrow space. Considering various uncertainties and unknown external disturbances, kinematic and dynamic models of the omnidirectional vehicle are established. The inner-loop controller is designed based the sliding mode control (SMC) method, while the out-loop controller uses the proportion integral derivative (PID) method. In order to achieve the stable and robust performance, the artificial neural network (ANN) based adaptive law is introduced to model and estimated the various uncertainties disturbances. Stability and robustness of the proposed control method are analyzed using the Lyapunov theory. The performance of the proposed NNASMC method is verified and compared with the classical PID controller and SMC controller through both the computer simulation and the platform experiment. Results validate the effectiveness and robustness of the NNASMC method in presence of uncertainties and unknown external disturbances.

Multirate Lane-Keeping System With Kinematic Vehicle Model

In this paper, a novel multirate lane-keeping system (LKS) is introduced by using a kinematic-based model considering a look-ahead distance. First, we developed a kinematic lateral motion model for LKS with respect to the road. To increase a system damping of vehicle, the unique look-ahead output measurement matrix was introduced. The decentralized multirate lane-keeping control scheme with a multirate Kalman filter was also applied to resolve the asynchronous and irregular sampling time of multi sensors in autonomous vehicles. We validated the performance of the proposed kinematic-based multirate LKS using the dynamic vehicle solver CarSim, MATLAB/Simulink, and electric power steering hardware-in-the-loop system. Experimental validation was also conducted using a real test vehicle. Both simulation and experimental results showed that the proposed kinematic-based LKS performed lane-keeping capability comparable to that of dynamic-based LKS. In particular, the experimental results showed that the proposed method, compared to a human driver, can also maintain the lane within reasonable specifications. The experimental validation was performed at vehicle speeds of up to 120 km/h.

An Improved Yaw Estimation Algorithm for Land Vehicles Using MARG Sensors.

This paper presents a linear Kalman filter for yaw estimation of land vehicles using magnetic angular rate and gravity (MARG) sensors. A gyroscope measurement update depending on the vehicle status and constraining yaw estimation is introduced. To determine the vehicle status, the correlations between outputs from different sensors are analyzed based on the vehicle kinematic model and Coriolis theorem, and a vehicle status marker is constructed. In addition, a two-step measurement update method is designed. The method treats the magnetometer measurement update separately after the other updates and eliminates its impact on attitude estimation. The performances of the proposed algorithm are tested in experiments and the results show that: the introduced measurement update is an effective supplement to the magnetometer measurement update in magnetically disturbed environments; the two-step measurement update method makes attitude estimation immune to errors induced by magnetometer measurement update, and the proposed algorithm provides more reliable yaw estimation for land vehicles than the conventional algorithm.

The Design of Fixed-Time Observer and Finite-Time Fault-Tolerant Control for Hypersonic Gliding Vehicles

This paper proposes a fault-tolerant control (FTC) scheme for a hypersonic gliding vehicle to counteract actuator faults and model uncertainties. Starting from the kinematic and aerodynamic models of the hypersonic vehicle, the control-oriented model subject to actuator faults is built. The observers are designed to estimate the information of actuator faults and model uncertainties, and to guarantee the estimation errors for converging to zero in fixed settling time. Subsequently, the finite-time multivariable terminal sliding mode control and composite-loop design are pursued to enable integration into the FTC, which can ensure the safety of the postfault vehicle in a timely manner. Simulation studies of a six degree-of-freedom nonlinear model of the hypersonic gliding vehicle are carried out to manifest the effectiveness of the investigated FTC system.

Real-Time Obstacles Detection and Status Classification for Collision Warning in a Vehicle Active Safety System

This paper presents real-time obstacles detection and their status classification method for collision warning in the vehicle active safety system. Specifically, stereo cameras and millimeter wave (mmw)-radar are fused to help the driving ego-vehicle to find “Danger” or “Potential Danger” in a timely way through combining with the vehicle kinematic model. The proposed method makes full use of the unique advantages of stereo cameras and mmw-radar to sense the environment through several modules. Cameras are mainly used to detect the near or lateral dynamic objects and to obtain the obstacles region of interest (ROI) considering its rich information and high sensitivity to the lateral displacement, while far or longitudinal relative dynamic objects are detected by mmw-radar according to its observational ability to make up for the disadvantage of cameras. In detail, a cameras detector utilizes ”error vectors” rather than the optical flow to obtain dynamic classes through two times clustering. Mmw-radar mainly detects relative dynamic objects, whose absolute speed can be computed according to the ego-vehicle's state. Then, the detected objects of these two detectors are integrated in an obstacles ROI map, which is obtained through an UV-disparity obstacles detection algorithm to get the final dynamic and relative dynamic objects. Finally, they are classified by comparing them with a dangerous area that is acquired according to the vehicle kinematic model in a special vehicle coordinate system, which is fixed to the ground temporarily. This method is tested on our mobile platforms and the results prove that it can work effectively even though the ego-vehicle drives quickly.

Reliable Sideslip Angle Estimation of Four-Wheel Independent Drive Electric Vehicle by Information Iteration and Fusion

Accurate estimation of longitudinal force and sideslip angle is significant to stability control of four-wheel independent driven electric vehicle. The observer design problem for the longitudinal force and sideslip angle estimation is investigated in this work. The electric driving wheel model is introduced into the longitudinal force estimation, considering the longitudinal force is the unknown input of the system, the proportional integral observer is applied to restructure the differential equation of longitudinal force, and the extended Kalman filter is utilized to estimate the unbiased longitudinal force. Using the estimated longitudinal force, considering the unknown disturbances and uncertainties of vehicle model, the robust sideslip angle estimator is proposed based on vehicle dynamics model. Moreover, the recursive least squares algorithm with forgetting factor is applied to vehicle state estimation based on the vehicle kinematics model. In order to integrate the advantages of the dynamics-model-based observer and kinematics-model-based observer and improve adaptability of observer system in complex working conditions, a vehicle sideslip angle fusion estimation strategy is proposed. The simulations and experiments are implemented and the performance of proposed estimation method is validated.

Research on the compensation method of Indirect Tire Pressure Monitoring under Sinusoidal Driving Condition

Indirect tire pressure monitoring method (ITPMS) is based on the wheel speed signal differences indirectly infer that the change of the tire pressure. In the steering mode, there is a large yaw rate, and there is a big deviation between the four wheels’ speed. The traditional indirect tire pressure monitoring method based on wheel speed comparison method is invalid. That the automobiles which is matched electronic stability control system estimate the four-wheel speed according to the yaw rate and steering wheel angle become an important method to improve the accuracy of Indirect tire pressure monitoring. Four-wheel vehicle kinematics model for turning condition is established to compensate for the wheel speed in the steering mode, avoid the misjudgment of indirect tire pressure monitoring system. The seven degree of freedom vehicle model and the four wheel speed model are established firstly, and analyse the four wheel speed change of the vehicle under the condition of a certain corner input in the simulation. Then conduct a test on the vehicle road, calculated using the four wheel speed distribution module model of vehicle wheel speed on the theoretical value of the actual value of the wheel speed compensation, so it can monitor the tire pressure in turning vehicle. When the error of the wheel speed is beyond the threshold, the wheel can be judged to be in the state of lack of gas.

Basic Simulation Environment for Highly Customized Connected and Autonomous Vehicle Kinematic Scenarios.

To enhance the reality of Connected and Autonomous Vehicles (CAVs) kinematic simulation scenarios and to guarantee the accuracy and reliability of the verification, a four-layer CAVs kinematic simulation framework, which is composed with road network layer, vehicle operating layer, uncertainties modelling layer and demonstrating layer, is proposed in this paper. Properties of the intersections are defined to describe the road network. A target position based vehicle position updating method is designed to simulate such vehicle behaviors as lane changing and turning. Vehicle kinematic models are implemented to maintain the status of the vehicles when they are moving towards the target position. Priorities for individual vehicle control are authorized for different layers. Operation mechanisms of CAVs uncertainties, which are defined as position error and communication delay in this paper, are implemented in the simulation to enhance the reality of the simulation. A simulation platform is developed based on the proposed methodology. A comparison of simulated and theoretical vehicle delay has been analyzed to prove the validity and the creditability of the platform. The scenario of rear-end collision avoidance is conducted to verify the uncertainties operating mechanisms, and a slot-based intersections (SIs) control strategy is realized and verified in the simulation platform to show the supports of the platform to CAVs kinematic simulation and verification.

Control of Mobile Robots with Bounded Inputs in a Rigid Formation

This study presents controllers for trajectory tracking for the kinematic model of an Unmanned Ground Vehicle (UGV) subject to bounded inputs. The proposed controllers are based on smooth uniformly bounded functions that can easily be realized. Some results are demonstrated.

Dynamic modelling of the steering performance of an articulated tracked vehicle using shear stress analysis of the soil

The steering performance is one of the major concerns in special engineering vehicle fields. This study aims to investigate the steering performance of an articulated tracked vehicle. An improved dynamic steering model for an articulated tracked vehicle is proposed when considering the shear stress–shear displacement relation of the soil at the track–ground interface. In this model, the mechanical, kinematic and dynamic models of the articulated unit are constructed to evaluate the movement performance of the articulated unit, and the kinematic and dynamic models of the front vehicle and the rear vehicle are developed to evaluate the steering performance. The created dynamic model is then applied to analyse the steering performance of a specific articulated tracked vehicle. A mechanically controlled united simulation model and actual tests of an articulated tracked vehicle are utilized to verify the established steering model. The results of the comparisons show the effectiveness of the proposed dynamic steering model.

Lane changing trajectory planning and tracking control for intelligent vehicle on curved road.

This paper explores lane changing trajectory planning and tracking control for intelligent vehicle on curved road. A novel arcs trajectory is planned for the desired lane changing trajectory. A kinematic controller and a dynamics controller are designed to implement the trajectory tracking control. Firstly, the kinematic model and dynamics model of intelligent vehicle with non-holonomic constraint are established. Secondly, two constraints of lane changing on curved road in practice (LCCP) are proposed. Thirdly, two arcs with same curvature are constructed for the desired lane changing trajectory. According to the geometrical characteristics of arcs trajectory, equations of desired state can be calculated. Finally, the backstepping method is employed to design a kinematic trajectory tracking controller. Then the sliding-mode dynamics controller is designed to ensure that the motion of the intelligent vehicle can follow the desired velocity generated by kinematic controller. The stability of control system is proved by Lyapunov theory. Computer simulation demonstrates that the desired arcs trajectory and state curves with B-spline optimization can meet the requirements of LCCP constraints and the proposed control schemes can make tracking errors to converge uniformly.

A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning.

In this paper, we propose a novel positioning solution for land vehicles which is highly reliable and cost-efficient. The proposed positioning system fuses information from the MEMS-based reduced inertial sensor system (RISS) which consists of one vertical gyroscope and two horizontal accelerometers, low-cost GPS, and supplementary sensors and sources. First, pitch and roll angle are accurately estimated based on a vehicle kinematic model. Meanwhile, the negative effect of the uncertain nonlinear drift of MEMS inertial sensors is eliminated by an H∞ filter. Further, a distributed-dual-H∞ filtering (DDHF) mechanism is adopted to address the uncertain nonlinear drift of the MEMS-RISS and make full use of the supplementary sensors and sources. The DDHF is composed of a main H∞ filter (MHF) and an auxiliary H∞ filter (AHF). Finally, a generalized regression neural network (GRNN) module with good approximation capability is specially designed for the MEMS-RISS. A hybrid methodology which combines the GRNN module and the AHF is utilized to compensate for RISS position errors during GPS outages. To verify the effectiveness of the proposed solution, road-test experiments with various scenarios were performed. The experimental results illustrate that the proposed system can achieve accurate and reliable positioning for land vehicles.

평행주차를 위한 자동 조향 제어시스템 개발 및 성능평가

This research is conducted to develop an automatic steering system for parallel parking, and the performance of the system was evaluated by parallel parking a conventional vehicle. The automatic steering system consisted of MDPS (motor driven power steering) to control steering, ESC (electronic stability control) to acquire wheel speed, ultrasonic sensors to recognize the parking space, and a controller to communicate and handle data. The parallel parking process using the automatic steering control consisted of parking space recognition, parking path generation, and parking path tracking. The path for parallel parking was generated based on a kinematic model of a conventional vehicle, and a PI controller was used to control the steering angle for path tracking. Parallel parking using the automatic steering control was conducted according to vehicle speed conditions. The results show that the errors on the x-axis and y-axis were below 0.54 m and 0.14 m, respectively, and the error on the steering angle was less than <TEX>$1^{\circ}$</TEX>. Therefore, it is possible to implement parallel parking using an automatic steering control system for conventional vehicles.

A new AUV navigation system exploiting unscented Kalman filter

The development of precise and robust navigation strategies for Autonomous Underwater Vehicles (AUVs) is fundamental to reach the high level of performance required by complex underwater tasks, often including more than one AUV. One of the main factors affecting the accuracy of AUVs navigation systems is the algorithm used to estimate the vehicle motion, usually based on kinematic vehicle models and linear estimators. A precise and reliable navigation system is indeed fundamental to AUVs: the Global Positioning System (GPS) signal is not available underwater, thus making it very hard to know the position of the vehicle in real-time. In this paper, the authors present an innovative navigation strategy specifically designed for AUVs, based on the Unscented Kalman Filter (UKF). The new algorithm proves to be effective if applied to this class of vehicles and allows us to achieve a satisfying accuracy improvement compared to standard navigation algorithms. The proposed strategy has been experimentally validated using the navigation data acquired in suitable sea tests performed in Biograd Na Moru (Croatia) in the framework of the FP7 European ARROWS project tests performed during the Breaking the Surface 2014 (BtS 2014) workshop. The vehicles involved are the two Typhoon AUVs, developed and built by the Department of Industrial Engineering of the University of Florence within the THESAURUS Tuscany Region project for exploration and surveillance of underwater archaeological sites. The experiment, described in the paper, was performed to preliminary test the cooperative navigation between these AUVs. The new algorithm has been initially tested offline, and the validation of the proposed strategy provided accurate results in estimating the vehicle dynamic behaviour.

Stable and robust vehicle steering control using an overhead guide in greenhouse tasks

We have developed an autonomous vehicle steering control algorithm to aid with harvesting and spraying tasks in greenhouses to alleviate the problem of shortage of workers. We use an overhead guide constructed in the greenhouse to mark the desired path of the vehicle. A rigid bar is then connected from the guide to the vehicle. The vehicle steers and corrects itself by sensing the angle and distance of the vehicle from the guide. The control algorithm was developed for a two-dimensional kinematic vehicle model which was found to be valid for low-speed vehicles. The steering control was examined by numerical simulation in order to investigate the effects of parameters such as control-point location on stability deviations of the vehicle path. The asymptotic stability of the system was verified analytically and confirmed using numerical simulations. The control system was then simulated using a multibody dynamics simulation in conditions that better simulated an agricultural environment, thus confirming that the control system also maintained stability under rough conditions. Finally, in accordance with the control algorithm and the simulations, a proof-of-concept prototype of the vehicle was developed and the control algorithm was successfully deployed on a set of experiments.