Vehicle Longitudinal Motion Research Articles

Analysis of vibration responses in a large airport ground transportation centre caused by maglev and subway trains

This study aims to assess the vibration response of the ground transportation centre (GTC) at an airport integrated transportation hub during the starting and braking processes of underground maglev and subway trains. In this work, we considered the actual motion states of trains entering and exiting the GTC and calculated the traction characteristics of maglev and subway trains. On this basis, the vertical coupling vibration models for a medium- and low-speed maglev train-guideway and a subway train-floating slab track considering variable-speed motion, as well as the vehicle longitudinal motion models, were established, and the vertical and longitudinal fastener forces of the tracks were obtained accordingly. Finally, based on the two-step analysis method, the vertical and longitudinal fastener forces were simultaneously applied to the track–GTC–soil finite element model to evaluate the structural vibrations induced by maglev and subway trains. The results show that the amplitude of the vertical fastener force is an order of magnitude greater than that of the longitudinal fastener force, and the amplitude of the vertical fastener force caused by subway trains is greater than that caused by maglev trains. The starting process of both maglev trains and subway trains results in a greater vertical vibration response of the GTC than the braking process, with the impact of subway trains being greater than that of maglev trains. Within the same area, the vertical vibration level observed at structural column observation points is lower than that at slab observation points. When maglev trains and subway trains start simultaneously, the vibration level response at some points exceeds the specified limit of 75dB.

Study on Lane-Change Replanning and Trajectory Tracking for Intelligent Vehicles Based on Model Predictive Control

When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which was divided into a lane-change trajectory correction strategy, a lane-change switchback strategy and forward active collision avoidance strategy according to collision risk. Based on the collision risk function of the rectangular safety neighborhood, the objective functions were designed according to the specific requirements of different strategies. The vehicle lateral controller based on MPC and the vehicle longitudinal motion controller were established. The longitudinal velocity was taken as the joint point to establish the lateral and longitudinal integrated controller. The trajectory planning module, trajectory replanning module and trajectory tracking module were integrated in layers, and the three trajectory replanning strategies of lane-change trajectory correction, lane-change switchback and forward active collision avoidance were respectively simulated and verified. The simulation results showed the trajectory replanning strategy achieves collision avoidance under different scenarios and ensures the vehicle’s driving stability. The trajectory tracking layer achieves accurate tracking of the conventional lane-change trajectory and has good driving stability and comfort.

Design and Evaluation of Lane-Change Collision Avoidance Systems in Semi-Automated Driving

Hands on the steering wheel partial driving automation, in which the system controls the lateral and longitudinal vehicle motion while the driver holds the steering wheel, monitors the roadway, and intervenes when necessary, is an example of shared control driving. To insure mutual safety in shared control driving, the system also needs to guide the driver's interventions to avoid hazardous actions, such as risky lane changes. This study proposes three steering interventions that activate automatically when the system detects a lane change by monitoring the steering wheel angle inputted by the driver and road section and determining that the two vehicles are on a collision course. A driving simulation experiment with 80 drivers was conducted to compare four conditions: 1) no steering intervention; 2) a haptic steering intervention that increases stiffness against steering toward the lane where a collision may occur; 3) an automatic steering intervention that decouples the driver's steering input and autonomously steered the vehicle away from the hazard; and 4) multiple steering interventions that generate different degrees of haptic and automatic steering interventions based on the time headway and time-to-collision between vehicles. Analysis of driving performance and safety under conditions with and without steering interventions indicate that all participants, at some points, initiated lane changes that are likely to result in a crash during partial driving automation. However, the three interventions effectively reduced lane-change collisions compared to the baseline. While the automatic steering intervention avoided all collisions, the multiple steering interventions were determined to provide sufficient safety while being more thoroughly accepted by the drivers than the haptic and automatic interventions separately. These findings have implications for developing adaptive collision avoidance systems considering user preference and driving performance.

State-Estimation-Based Control Strategy Design for Connected Cruise Control With Delays

The research on connected and automated vehicles attracts much attention recently. In connected vehicle systems, connected cruise control (CCC) is a wildly concerned technique to improve traffic safety and mitigate congestion. In this article, an optimal state-estimation-based (SE-based) control algorithm is proposed in a backward recursion manner to regulate CCC vehicle's longitudinal motion under stochastic communication delays, state noises, and channel interferences. Based on the analysis of error dynamics of vehicles and optimal state estimation strategy, the SE-based system dynamics is first formulated by using the Kalman filtering method. Then, a two-step SE-based control algorithm aiming at minimizing the quadratic system cost is proposed to enhance the vehicular platoon stability. In particular, the optimal control strategy can be generated online as a linear function of the estimated state information and previous control signals, and the corresponding coefficient can be iteratively calculated in an offline step. Finally, the results are successfully extended to the case of arbitrary communication delays, and the condition for the asymptotically mean square stability of the proposed optimal SE-based CCC algorithm is derived. Simulation results indicate that the proposed SE-based control algorithm has better system performance and control stability compared with existing works.

Longitudinal Vehicle Motion Prediction in Urban Settings With Traffic Light Interaction

Predictive cruise control functions designed to reduce the energy consumption of intelligent and automated vehicles require an accurate prediction of the upcoming traffic situation in general and the preceding vehicle in particular. This article presents the implementation of prediction models which do not rely on explicit information of the entire preceding vehicle queue. Using this assumption, prediction algorithms based on Conditional Linear Gauss (CLG) models and Deep Neural Network (DNNs) were trained using real-world measurements in an urban setting. The training was conducted with 6.6 hours of driving data specifically collected for this study. The results show that both approaches can provide accurate estimates of a preceding vehicle’s motion. For the CLG models, a queue-estimation logic was implemented to improve the prediction accuracy. The most accurate prediction could be obtained when DNNs are set up with Long Short-Term Memory (LSTM) layers, capable of modeling time dependencies. The results also show that accurate short and mid term predictions of a vehicle’s trajectory can be made based primarily on sensor data, even when no or only little data from vehicle-to-vehicle (V2V) communication is available.

Comparison of fuel consumption and recoverable energy according to NEDC and WLTP cycles of a vehicle

Since 1997, the NEDC (New European Driving Cycle) has been used to measure CO2 emissions. However, because this cycle is unable to accurately replicate real-world driving conditions, a new procedure has been developed. The WLTP (Worldwide Harmonised Light Vehicles Test Procedure), which is 10 minutes longer and more dynamic than NEDC, has been used since late 2017. In this paper, fuel consumption, CO2 emissions, and energy demand of these two cycles are compared. The vehicle mathematical model was created in a MATLAB program using vehicle longitudinal motion equations for a light commercial vehicle with a diesel engine. The speed profiles of the commonly used NEDC and WLTP cycles were defined in the model, and the fuel consumption, CO2 emission values, and the total energy values required for each cycle were calculated. Furthermore, the recoverable energy potential of the cycle has been revealed. According to the WLTP cycle, the vehicle's fuel consumption and CO2 emission values were calculated at approximately 11% more than the NEDC cycle. The recoverable energy potential is 2.64 times higher in the WLTP cycle compared to the NEDC cycle. Thus, for vehicle designers, it is a very useful tool that can calculate the fuel and CO2 consumption of a vehicle in 100 km according to certain cycles, based on vehicle parameters.

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms.

The age of algorithms is here, and it is really changing people's lives. More and more ethical problems related to algorithms have attracted people's attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has been promoted and applied in the market. At present, most of the studies on the longitudinal collision avoidance system of vehicles use collision warning or emergency braking to avoid collision. However, when the vehicle is in a special situation such as high speed and slippery road, emergency steering is more effective. In order to further improve the vehicle safety and ethical algorithm design points, this article revolves around vehicle lateral active collision avoidance control method research, the collision avoidance decision-making, and path planning and collision avoidance transverse vehicle longitudinal motion control is analyzed, and based on automated driving simulation experiment, the tests carried out to verify the designed control strategy. The experimental results show that the proposed method not only has a good effect of preventing automatic driving collision but also can meet the requirements of algorithm ethics. This research can effectively guide the research of algorithmic ethics in the field of autonomous driving and effectively reduce the occurrence of traffic accidents.

Vehicle State Estimation Using Interacting Multiple Model Based on Square Root Cubature Kalman Filter

The distributed drive arrangement form has better potential for cooperative control of dynamics, but this drive arrangement form increases the parameter acquisition workload of the control system and increases the difficulty of vehicle control accordingly. In order to observe the vehicle motion state accurately and in real-time, while reducing the effect of uncertainty in noise statistical information, the vehicle state observer is designed based on interacting multiple model theory with square root cubature Kalman filter (IMM-SCKF). The IMM-SCKF algorithm sub-model considers different state noise and measurement noise, and the introduction of the square root filter reduces the complexity of the algorithm while ensuring accuracy and real-time performance. To estimate the vehicle longitudinal, lateral, and yaw motion states, the algorithm uses a three degree of freedom (3-DOF) vehicle dynamics model and a nonlinear brush tire model, which is then validated in a Carsim-Simulink co-simulation platform for multiple operating conditions. The results show that the IMM-SCKF algorithm’s fusion output results can effectively follow the sub-model with smaller output errors, and that the IMM-SCKF algorithm’s results are superior to the traditional SCKF algorithm’s results.

Linearising longitudinal vehicle dynamics through adaptive control techniques for platooning applications

Vehicle platooning provides avenues to improve road transportation. However, to safely operate a vehicle platoon, stringent conditions, e.g., string stability, on the closed-loop platoon dynamics must be guaranteed. To meet such platoon control specifications, linear longitudinal vehicle dynamics are usually assumed for the platoon control design and are imposed by using mid-level control systems. However, disturbances and model mismatches can limit the compensation of the nonlinear vehicle dynamics. To systematically impose the linear behaviour to each vehicle in a platoon despite model uncertainties and disturbances, two adaptive solutions are proposed and compared: an adaptive solution for systems in Brunovsky's form and the enhanced model reference adaptive control. Numerical results confirm that both adaptive techniques are effective in imposing the linear dynamics to the longitudinal vehicle motion, also when integrated within a platoon control architecture. The closed-loop platoon performance is assessed via a set of performance indicators for different platoon lengths.

An Integrated Observer for Real-Time Estimation of Vehicle Center of Gravity Height

This paper introduces a new integrated observer for estimating the vehicle center of gravity (CG) height in real time. It can assist the vehicle in monitoring the real-time rollover risk and improving the performance of vehicle safety control systems. The proposed integrated observer consists of three parts: a linearized recursive least square (LRLS) algorithm on vehicle longitudinal motion, an adaptation law on vehicle lateral motion, and observer synthesis. First, the LRLS algorithm performs estimation of vehicle mass and CG height during longitudinal braking and exploits the characteristic that normalized longitudinal tire stiffness is the same in the front and rear axles. Second, the adaptation law, accompanied by a roll angle observer, estimates CG height on the vehicle lateral motion based on Lyapunov stability analysis. It includes the following contributions: verification of robustness to the vehicle mass estimation error and prevention of integration drift. Finally, in the observer synthesis, the final estimation of CG height combining the above two results is derived. The overall estimation algorithm has high practicality due to the following features. 1) CG height can be obtained on both longitudinal and lateral motions of the vehicle. This point leads to a fast convergence rate in CG height estimation. 2) The fact that it does not cause any computational burden issues in real-time implementation is also a great advantage in terms of practicality. 3) It utilizes only readily available sensors. An experimental study with various driving scenarios evaluates the effectiveness of the proposed algorithm in real-car application.

Lateral Motion of Towed Underwater Vehicle within the Problem of Continental Shelf Monitoring

Lateral motion dynamics was studied of a robotic towed underwater system designed to monitor the continental shelf and consisting of a towed vehicle and a tow wireline. In regard to underwater vehicles of the type in question, it is quite correct to represent spatial motion in the form of a super-position consisting of two flat motions, i.e., longitudinal motion in the vertical plane and lateral motion in the horizontal plane. Dynamics of the towed system longitudinal motion within the monitoring problem was considered in a previously published work by the authors. The present work is its natural continuation and development traditionally accepted in the problems of the underwater vehicles spatial motion mechanics. Diagram of the towed vehicle operation and its hydrodynamic characteristics are presented; besides, mathematical model of a wireline and also a model of the wireline-towed vehicle system lateral motion were constructed. Probable steady system motions were analyzed, issues of balancing, as well as those of the towed vehicle dynamic stability when moving at a constant depth were considered. Results of numerical calculations were provided. The results obtained were considered in conjunction with the results of the authors' above mentioned work related to the towed vehicle longitudinal motion and make it possible to select such system parameters that provide the specified character of spatial movements in the process of monitoring the continental shelf taking into consideration the need to perform turns in the horizontal plane at changing directions and to ensure vertical maneuvers when avoiding underwater obstacles.

Closed-Form Solution of a Special Case of a Vehicle Longitudinal Motion Model

One of the most common approaches in modern engineering research, including vehicle dynamics, is to formulate an accurate, but typically complex, mathematical model of a system or phenomenon and then use a software package to solve it. Typically, the solution is obtained in the form of a large data set, which may be difficult to analyse and interpret. This paper represents a purely theoretical analysis of a special case of vehicle longitudinal motion. Starting from a simplified mathematical model, a set of transcendental equations was derived that represents the exact solution of the model (i.e., in a closed form). The equations are analysed and interpreted in terms of what is their physical meaning. Although the equations derived here have only limited application in studying real world problems, due to the simplicity of the mathematical model, they offer a deeper insight into the nature of vehicle longitudinal motion.

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively.

Synthesis and Approximation of Control in Adaptive Cruise Control Systems of Commercial Vehicles

In this paper, we consider the problem of the development of an algorithm of the adaptive cruise control functioning operating in the conditions of powertrain gear ratio varying in a wide range and vehicle velocity changing. The functioning of a classical cruise control system is generally based on the usage of a PID-controller with constant coefficients. However, despite the easiness of its tuning and physical realization and also its relatively high robustness this class of control devices cannot guarantee the cruise control system optimal functioning in all driving conditions because the plant is not timeinvariant and linear. To overcome the above shortcomings, in this research we consider the possibility of neural network realization of a commercial vehicle adaptive cruise control algorithm.In this paper, we propose the mathematical model of a commercial vehicle longitudinal motion designed for the control system analysis and synthesis. We carry out the PI-controller coefficients tuning to control the vehicle longitudinal velocity in various driving conditions of a commercial vehicle. We show that the controller coefficients vary according to a rather complex law. Therefore, we propose the algorithm of the adaptive cruise control functioning based on the approximation of the controller coefficients by the artificial neural network. The network used is the multilayer perceptron and it has ten neurons in the hidden layer to provide the high quality of the approximation. We carry out the training of the neural network by the Levenberg-Marquardt method with a sample of a total volume of 500 points, obtained using standard methods of controller synthesis. We verify the correctness of the obtained results through the computer simulations of the vehicle acceleration from 0 to 100 km/h, proving that the PI-controller coefficients, providing the required transient responses, significantly vary depending on the current state of the vehicle. The approach of the PI-controller coefficients approximation presented in this paper may be further used in the design of adaptive control systems able to function effectively in various operating modes.

Control-Oriented Models for vehicle longitudinal motion

The use of mathematical models is widespread both in simulating the dynamic behavior of vehicle longitudinal motion and in designing related controllers. This paper focuses on control-oriented models for longitudinal motion which better captured the plant dynamics for vehicles with internal combustion engines. Firstly, a review of some simplified models is presented and secondly, two more complex control-oriented models which take into account the powertrain dynamics are proposed.

On model-based longitudinal feed-forward motion control for low velocities on known road profiles

ABSTRACTSo far, longitudinal motion control has focused on situations like highway driving, where disturbances of the road profile can be neglected. In this paper, we show how the Two Point Tire Model can be used to derive a novel feed-forward control law for a vehicle's longitudinal motion that considers the effects of the road profile and can complement existing control approaches. For this purpose, we recapitulate the basic model assumptions and equations and briefly discuss how it can be used on arbitrary road profiles. Two approaches for implementation in a real vehicle are presented. Comparisons of these approaches in simulation and to a human driver of an experimental vehicle show that the controller can deal with stepped obstacles of up to 14 cm in height. However, the control performance is essentially limited by the actuator delay and human drivers outperform the controller due to their ability of sensing subtle vehicle motions. The results indicate that the control performance can be further improved by using a preview on the necessary drive torque, which can be provided by the solution that we propose.

Robust control of a small-scale supercavitating vehicle: From modeling to testing

By traveling inside a gas cavity, a supercavitating vehicle can reduce hydrodynamic drag, increase speed, and minimize power consumption. The main challenge for controlling a supercavitating vehicle is a nonlinear force that arises when the vehicle back-end pierces the cavity. This force, referred to as planing, leads to oscillatory motion and instability. In an effort to develop robust control technologies for supercavitating vehicles, we developed and tested a control methodology for a small-scale system capable of freely rotating about its pitch axis in a high-speed water tunnel. We constructed a low-order nonlinear model of the longitudinal vehicle motion that is based on experimental observations and physical principles. By using this model, we developed a controller design method that delivers a proof of performance in the face of nonlinear and uncertain planing forces. Experiments with the vehicle prototype in the water tunnel suggest that the proposed control technique ensured higher performance when the uncertain planing dynamics are considered in the control synthesis.

Coordinated Longitudinal and Lateral Motion Control for Four Wheel Independent Motor-Drive Electric Vehicle

For four wheel independent motor-drive electric vehicle, the vehicle longitudinal and lateral motion can be controlled by distributing the driving and regenerative braking torques of four wheel motors. To meet the driving command of driver and keep the vehicle lateral stability, a hierarchical control system is proposed in this paper. In the upper layer, a nonlinear model predictive control is implemented to solve the nonlinear multiinput multioutput, over-actuated problem. The controller is based on a nonlinear three degree-of-freedom model with nonlinear tire model, considering wheel slips as virtual control input. In the lower layer, the wheel slips are manipulated by a PID controller for generating driving and regenerative braking torques of the independent motors. This proposed controller is tested in a hardware-in-the-loop system with four typical maneuvers, which are constant velocity, accelerating, decelerating, and low road adhesion coefficient situations to show different driver command. The results show that the driver command of longitudinal and lateral motion control are both satisfied.

Simulation analysis of adaptive cruise prediction control

Predictive control is suitable for multi-variable and multi-constraint system control.In order to discuss the effect of predictive control on the vehicle longitudinal motion, this paper establishes the expected spacing model by combining variable pitch spacing and the of safety distance strategy. The model predictive control theory and the optimization method based on secondary planning are designed to obtain and track the best expected acceleration trajectory quickly. Simulation models are established including predictive and adaptive fuzzy control. Simulation results show that predictive control can realize the basic function of the system while ensuring the safety. The application of predictive and fuzzy adaptive algorithm in cruise condition indicates that the predictive control effect is better.

Vehicle state estimation for anti-lock control with nonlinear observer

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results.