Changes In Vehicle Dynamics Research Articles

Predicting CAM generation times through machine learning for cellular V2X communication

In the narrow Intelligent Transportation System (ITS) band, avoiding wireless channel congestion is essential. Reporting vehicle kinematics in the Cooperative Awareness Message (CAM) only when there are notable changes in vehicle dynamics is a standardized approach to reducing bandwidth usage of periodic CAM messages that are exchanged between vehicles, and is called the CAM generation rule. However, in cellular vehicle-to-everything (V2X) communication, aperiodicity due to frequent omissions of periodic CAM raises problems of resource waste and instability in resource scheduling. The problem can be solved by reserving a resource only for actual CAM transmission times in the future. This article demonstrates that a neural network-based scheme can predict the next CAM generation times at an average accuracy of over 94%, which can be utilized for resource reservation under the CAM generation rule.

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

Mind over motor mapping: Driver response to changing vehicle dynamics.

Improvements in vehicle safety require understanding of the neural systems that support the complex, dynamic task of real-world driving. We used functional near infrared spectroscopy (fNIRS) and pupilometry to quantify cortical and physiological responses during a realistic, simulated driving task in which vehicle dynamics were manipulated. Our results elucidate compensatory changes in driver behavior in response to changes in vehicle handling. We also describe associated neural and physiological responses under different levels of mental workload. The increased cortical activation we observed during the late phase of the experiment may indicate motor learning in prefrontal-parietal networks. Finally, relationships among cortical activation, steering control, and individual personality traits suggest that individual brain states and traits may be useful in predicting a driver's response to changes in vehicle dynamics. Results such as these will be useful for informing the design of automated safety systems that facilitate safe and supportive driver-car communication.

Frequency Domain-Based Pseudosliding Mode Flight Control Design

A flight control system design technique previously introduced in the literature is applied to four challenging synthesis problems: (1) design of a hypersonic vehicle autopilot, (2) design of a stability augmentation system for a fighter aircraft, including human-in-the-loop desktop simulator evaluation, (3) design of an autopilot for a cruise missile, and (4) design of a control system for an uninhabited rotorcraft. The vehicle models used in the first and third problems have been employed by other researchers using competing design approaches. Human-in-the-loop desktop simulation of the design in the second problem provides a novel evaluation of the resulting stability and command augmentation system’s ability to accommodate system failures from the standpoint of their effect upon operator dynamics. Finally, the ability of the design procedure to accommodate velocity dependent changes in vehicle dynamics is demonstrated in the rotorcraft example The nine-step synthesis procedure to be exercised is based in the frequency domain and evolves from sliding mode control theory.

Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.

An Exploratory Shockwave Approach to Estimating Queue Length Using Probe Trajectories

In this article, the authors present an innovative approach for signalized intersection performance measurement using probe vehicle trajectory data, focusing on queue length estimation. Critical points, defined as the data points representing the changes in vehicle dynamics, are the keystone of the methodology. The author then present a threshold-based critical point extraction algorithm, which has the potential to reduce the communication cost in future real-time probe data collection application. A shockwave-based method using the critical points to detect the signal timing provides the basis for cycle-by-cycle performance measurement. The authors propose a queue length estimation method as a case study for signalized intersections. This approach was tested by simulation and Next Generation Simulation Project data. Results indicate promising outcome of the trajectory-based method.

Modeling Pilot Control Behavior with Sudden Changes in Vehicle Dynamics

A pursuit tracking model of the human pilot adapting to sudden changes in vehicle dynamics is developed and exercised. The current model is based upon a simplified representation of the human pilot in multi-axis tasks previously reported in the literature. A key feature of the adaptive model is the simplicity afforded by only varying two gain parameters in each control loop to accommodate pilot adaptation. The model is first exercised in a single-loop task in which the controlled element dynamics are changed from rate command to acceleration command (with time delay), back to rate command, and finally, to position command. A second example applies the model to a simple two-axis task (two control inceptors) in which the controlled element dynamics in both control loops are changed simultaneously. The model employed for sudden changes in vehicle dynamics is also applied to a single-axis task in which the controlled element dynamics change gradually over a 10 s period. Finally, longitudinal control of a simplified model of a fighter aircraft undergoing sudden damage is considered.

Adaptive Fuzzy Strong Tracking Extended Kalman Filtering for GPS Navigation

The well-known extended Kalman filter (EKF) has been widely applied to the Global Positioning System (GPS) navigation processing. The adaptive algorithm has been one of the approaches to prevent the divergence problem of the EKF when precise knowledge on the system models are not available. One of the adaptive methods is called the strong tracking Kalman filter (STKF), which is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. Traditional approach for selecting the softening factors heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the adaptive fuzzy strong tracking Kalman filter (AFSTKF) is carried out. In the AFSTKF, the fuzzy logic reasoning system based on the Takagi-Sugeno (T-S) model is incorporated into the STKF. By monitoring the degree of divergence (DOD) parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the softening factor according to the change in vehicle dynamics. GPS navigation processing using the AFSTKF will be simulated to validate the effectiveness of the proposed strategy. The performance of the proposed scheme will be assessed and compared with those of conventional EKF and STKF

Use of numerical optimization to determine the effect of the roll stiffness distribution on race car performance

The use of active systems for controlling suspensions has been used before in Formula One and other open wheel racing series but at present this form of active control is not allowed by Formula One regulations. The roll stiffness distribution is a key element of suspension design and it has an influence on the dynamic weight transfer of the vehicle. In this paper, the roll stiffness distribution has been optimized during a calculation process to simulate and quantify the possible benefits of controlling the distribution of roll stiffness between the front and rear axles of an open wheel race car. The roll axis concept has been used to provide the roll component to the lateral load transfer evaluation. The benefits shown by the optimization of the roll stiffness distribution have created a mean handling performance improvement over the base vehicle of over 10 per cent, which on the lap of the Barcelona Grand Prix circuit yielded a lap time improvement of 2.71 s, using a quasi-steady-state calculation method. Additionally, it has been shown how this improvement has been realized in terms of the change in vehicle dynamics. This research also shows that circuit dependent analysis is possible to exploit the value of the static setting of the roll stiffness distribution. Further work and validation are required to explore this avenue of vehicle set-up optimization, but it does appear that the optimized calculation approach offers a way of helping to make an objective choice of a static roll stiffness distribution setting for particular circuits.

Two Degree of Freedom/H∞Controller Synthesis for Active Four Wheel Steering Vehicles

SUMMARY This paper proposes an active front and rear wheel steering control system that simultaneously achieves both lateral acceleration and yaw rate responses always desirable regardless of changes in vehicle dynamics. First, this paper describes a method to accurately estimate physical parameters in the four wheel steering vehicle model, including the dynamics of a steering actuator, by applying the maximum likelihood estimation method. Next, the structure of the proposed front and rear steering control system is described. This control system has for its purpose to coincide both the lateral acceleration and yaw rate responses of the vehicle with the responses of the respective desirable reference models, as well as to make it a robust system against changes in vehicle dynamics and external disturbances. To achieve both objectives, a two-degree-of-freedom control system theory is employed. Then the paper shows the viability of the proposed steering control system through computer simulations. Finally, the results of proving ground tests carried out with a test vehicle equipped with a front and rear wheel steering mechanism driven by the digital computer and electric powered actuator are shown. From these results, it was confirmed that the proposed active front and rear wheel steering control system would contribute greatly to the improvement of vehicle handling qualities and preventive safety.

A ROBUST ADAPTATIVE REAR WHEEL STEERING CONTROL SYSTEM FOR HANDLING IMPROVEMENT OF FOUR-WHEEL STEERING VEHICLES

SUMMARY This paper proposes the structure of and a design method for an Adaptive Rear Wheel Steering Control System (ARWSCS) that maintains desirable vehicle responses though computer control regardless of changes in vehicle dynamics due to operating conditions. The proposed system, based on a Self-Tuning Controller (STC), controls the rear wheels in such a manner that the vehicle makes the same response as those of the reference model which gives the desired response to driver's input. Digital simulations and proving-ground tests verified the practical usefulness of the proposed control system.

Comparison of Primary and Secondary Task Measures as a Function of Simulated Vehicle Dynamics and Driving Conditions

In a previously reported experiment involving a moving base driving simulator with computer-generated display, secondary task measures of workload showed significant increases as a function of large changes in vehicle dynamics and disturbance levels. Because the secondary task measures appeared less sensitive than desired, driving performance measures recorded during the same experiment were later analyzed. Particular emphasis in examining the driving performance data was placed on (1) determining the degree of intrusion of the secondary task on the driving task as a function of the independent variables, and (2) on comparing the sensitivity of the primary and secondary task measures. The results showed the secondary task does intrude significantly upon the driving task performance at low workload levels, but that it does not significantly intrude at high workload levels. Also, when the four primary task measures were analyzed for sensitivity to the independent variables, new information was obtained indicating greater sensitivity than is obtained with the single secondary task measure. Steering ratio, for example, is found to affect performance at high disturbance levels—a result not obtained in examining the secondary task by itself. The merits of primary and secondary task performance analysis are discussed, and suggestions are made for future work.