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A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting multiple model-based probabilistic behavior recognition algorithm. With this method, TV preview driving characteristic and lateral motion states are combined for precise TV lateral behavior description. Furthermore, to predict short-term lateral behavior, a preview lateral velocity-dependent transition probability matrix model constructed with Gaussian cumulative distribution function is proposed. Simulation and experimental results show that the proposed method considering vehicle preview driving characteristic predicts TV lateral behavior earlier than the conventional method.

Although autonomous vehicles (AV) have been rapidly developed, their control technology is not sufficiently mature for daily use, yet not human-centred enough. Some studies regarding trajectory planning are overly conservative and the vehicle avoids obstacles in an unhuman-like trajectory which causes discomfort to passengers; meanwhile, other studies are overly simplistic, the transport scenario, and vehicle trajectory are disregarded. The potential field (PF) algorithm is one of the most frequently used methods for the trajectory planning of AVs; however, most studies regarding the PF algorithm do not consider driving comfort and smoothness. This paper introduces optimised human-centred dynamic trajectory planning for AVs. The PF algorithm is implemented in a vehicle simulation model, which is integrated with model predictive control (MPC). The reference path is planned by PF algorithm and improved by MPC. The human-centred AV control is proposed in a simulation environment. The proposed planning method achieves a trade-off between safety, driving comfort, and driving smoothness and is validated with several driving simulation scenarios.

This paper proposes a novel robust path-following strategy for autonomous road vehicles based on type-2 fuzzy PID neural network (PIDT2FNN) method coupled to an Extended Kalman Filter-based Fuzzy Neural Network (EKFNN) observer. Uncertain Gaussian membership functions (MFs) are employed to self-adjust the universe of discourse for MFs using the adaptation mechanism derived from Lyapunov stability theory and Barbalat’s lemma. External disturbances are significant in autonomous vehicles by changing the driving condition. Furthermore, parametric uncertainties related to the physical limits of tires and the change of the vehicle mass may significantly affect the desired performance of autonomous vehicles. The robustness of the proposed controller against the parametric uncertainties and external disturbances is compared with one active disturbance rejection control (ADRC) algorithm, and a linear-quadratic tracking (LQT) method. The obtained results in terms of the maximum error and root mean square error (RMSE), demonstrate the effectiveness of the proposed control algorithm to reach the minimized path-tracking error.

With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.

In the design of a path-tracking controller for autonomous vehicles, mediating the conflicting objectives among path-tracking, vehicle stabilisation, and low computational cost is a challenging iss...

In this paper, a novel algorithm is proposed for the motion planning and path following automated cars with the incorporation of a collision avoidance strategy. This approach is aligned with an optimal reinforcement learning (RL) coupled with a new risk assessment approach. For this purpose, a probabilistic function-based collision avoidance strategy is developed, and the proposed RL approach learns the probability distributions of the adjacent and leading vehicles. Subsequently, the nonlinear model predictive control (NMPC) algorithm approximates the optimal steering input and the required yaw moment to follow the safest and shortest path through the optimal RL-based probabilistic risk function framework. Additionally, it is attempted to maintain the travel speed for the ego vehicle stable such that the ride comfort is also offered for the vehicle occupants. For this purpose, the steering system dynamics are also incorporated to provide a thorough understanding of the vehicle dynamics characteristic. Different driving scenarios are employed in the present paper to evaluate the proposed algorithm’s effectiveness.

Vibrational energy harvesting systems with linear electromechanical generators have been largely studied, due to their simple structures and convenience in application. However, the ambient vibration characteristics such as random, time-varying or low-frequency properties make the linear electromagnetic devices cannot be excited at their resonances, which will greatly affect the energy harvesting efficiency. Some attempts to improve the efficiency of a vibration energy harvesting system involve both expand the operating bandwidth and enlarging the magnitude of power output. To this end, a scissor-like energy harvesting system with equivalent nonlinear damping and linear stiffness has been developed in this study. It has been shown that, due to the beneficial nonlinear damping effects provided by the scissor-like structure, the proposed system can greatly improve the vibration energy harvesting performance in terms of the magnitude of power output and energy harvesting bandwidth. On the other hand, the structure parameters of the proposed system can be modified to achieve more significant energy harvesting performance. Moreover, to achieve tunable resonant frequency of the energy harvesting system, the scissor-like structure has been updated by attaching a new lever-type system. In this way, the energy harvesting performance can be improved by tuning the proposed system according to the properties of the surrounding vibration sources. In this paper, both harmonic and random excitations have been adopted to verify the performance of the proposed energy harvesting system.

This paper investigates the lane keeping control for four-wheel independently actuated autonomous vehicles. To guarantee the vehicle safety when the active-steering motor entirely fails, the steering manoeuvre is realized by the differential drive assisted steering (DDAS) that is generated by the differential moment between the front wheels. A novel adaptive multivariable super-twisting control strategy is proposed to realize the control objective in finite time, considering the multiple unknown and mismatched disturbances of the steering system with the chattering effect removed. In the sliding surface, a nonlinear function is designed to adaptively change the damping ratio of the closed-loop system so as to improve the transient performance of the lane keeping control in the faulty steering condition. The controller design has avoided the use of the lateral velocity which is usually hard to measure in practice. Instead, the lane keeping errors and their time derivatives are estimated with a high-order sliding mode observer based on a vision system. The finite-time convergence of the closed-loop system is proved by the Lyapunov method. Results of CarSim-Simulink simulations with the proposed control strategy compared with a tradition sliding mode controller based on a high-fidelity and full-car model have verified the effectiveness and robustness of the proposed controller in the lane keeping control via DDAS with the guaranteed high performance.

© 2018 Elsevier Ltd Driven by the increasing requirement for road conditions in the field of the controllable suspension system, this paper presents a novel nonlinear road-excitation classification procedure for arbitrary suspension control strategy. The proposed procedure includes four steps: the definition of controller parameters, selection of insensitive frequency ranges, calculation of superior features, and generation of the classifier. To better illustrate the proposed procedure, the clipped optimal control strategy is taken as an example in the simulation part. Simulation results reveal that the proposed method can accurately estimate road excitation level for various controller parameters, vehicle speeds, and vehicle models. Three contributions have been made in this paper: (1) A road classification procedure that can be used for road adaptive suspension control with any control algorithm is developed; (2) In order to improve classification accuracy, the concept of insensitive index which is based on the time-frequency analysis is proposed; (3) Experimental validation with a quarter vehicle test rig is performed, which has verified the effectiveness of the proposed method for the adaptive controllable suspension system.

Differential drive assistance steering (DDAS) is an emerging assisted steering mechanism in in-wheel-motor driven (IWMD) electric vehicles, yielded by the differential moment of the front tires in the steering system. DDAS can steer the front wheels when there is no steering power from the steering motor, and thus can be used as a redundant steering mechanism. To realize the yaw control when the active front steering entirely breaks down and guarantee the transient control performance therein, this paper proposes an integral sliding mode control (ISMC) approach for IWMD electric vehicles steered by DDAS. Two contributions are made in this paper: 1) An improved disturbance observer based ISMC strategy is designed to cope with the unknown mismatched disturbances, and the composite nonlinear feedback technique is employed to design the nominal part of the controller to restrain overshoots and remove steady-state errors considering the tire force saturations; 2) An adaptive super-twisting control approach is proposed to deal with the disturbances with unknown boundaries using a continuous controller while eliminating the chattering effect. The system stability and robustness are proved via Lyapunov approach. CarSim-Simulink simulation has verified the effectiveness of the proposed control approach in the case of the steering fault.

Vehicles are subject to a variety of road unevenness and random road excitations that potentially cause the vehicle to undergo a significant amount of energy dissipation. The energy loss due to vibration can be harvested/recaptured from the vehicle suspension system and the present paper aims to assess the energy harvesting potential from vehicle suspension under harmonic and random road excitations. In this manner, a mathematical model of half vehicle model was developed and different parameters such as magnitude, frequency, vehicle velocity and the relative velocity between the sprung mass and front and rear unsprung masses were included for harmonic based road type. For random excitations, two typical roads of highway with gravels and smooth runway were used and the results of average power were analysed. It was concluded that for the average harvested power versus vehicle velocity, the greatest value of 57.84 W is obtained at 13 km/h. Also, the average power increases by road amplitude with the minimum and maximum values of 51.54 and 1289 W. For the random excitations, the amount of instantaneous power that corresponds to highway with gravels is much greater than that of smooth highway and by the increase of vehicle velocity from 10 to 50 km/h, there is an increase of average power for the both of tested randomly distributed irregular road types.

In order to accurately predict vehicle dynamic responses when traversing high obstacles or large bumps, appropriate tyre models need to be developed and characterised. Tyre models used in vehicle ride and durability are usually characterised by experimental tests on the tyre. However, limitations in rig design and operating conditions restrict the range of test conditions under which the tyre can be tested, hence characterisation of the tyre behaviour during extreme manoeuvres may not be possible using physical tests. In this study, a combination of experimental tests and finite-element (FE) modelling is used in deriving Flexible Ring Tire (FTire) Models appropriate for different levels of tyre/road interaction severity. It is shown that FE modelling can be used to accurately characterise the behaviour of a tyre where limitations in experimental facilities prevent tyre characterisation using the required level of input severity in physical tests. Multi-body simulation is used to demonstrate that t...

This study investigates the influence of tyre structural layup and material properties on the relaxation length of a rolling tyre using finite element analysis. Relaxation length for rolling tyre under different operating conditions has been studied recently. However, the effects of tyre structural layup and material properties on relaxation length were ignored. In this present work, a finite element (FE) tyre model was built based on the material and geometry properties obtained from measurements of the tyre provided by a vehicle company. Rather than the common method (steady state rolling analysis) used for cornering behaviour simulations, ABAQUS/Explicit program was used for prediction of the cornering performance and relaxation length for a constant slip angle of the rolling tyre. Two different steer inputs were applied to the rolling tyre in terms of slip angle variation, namely step input and ramp input. The effects of various factors, including cross-section area, spacing, crown angle and strength of the tyre reinforcement cords, on relaxation length of the rolling tyre were investigated by numerical experiments using the design of experiment (DOE) method. Keywords: Relaxation length, Finite element analysis, DOE, Input function

This study mainly focuses on the mechanism of wheel tread spalling through wheelset longitudinal vibration that has been often neglected. Analysis of two actual cases of the wheel tread spalling problem leads to the conclusion that the wheel tread spalling is closely related to the wheelset longitudinal vibration in some locomotives, and many of these problems can be reasonably explained if the wheelset longitudinal vibration is considered. For better understanding of some abnormal wheel spalling problems, the formations of the wheelset longitudinal vibration and the wheel/rail contact parameters were analysed in the initial wheel tread spalling. With the preliminary analytical results, the wheelset longitudinal dynamic behaviour, the characteristics of wheel/rail contact and the mechanics in the condition of the wheelset longitudinal vibration were further studied quantitatively. The results showed that the wheelset longitudinal vibration changed not only the limit of these parameters and the position of...

This paper developed a detailed fluid dynamics model and a parallel computing scheme for air brake systems on long freight trains. The model consists of subsystem models for pipes, locomotive brake valves, and wagon brake valves. A new efficient hose connection boundary condition that considers pressure loss across the connection was developed. Simulations with 150 sets of wagon brake systems were conducted and validated against experimental data; the simulated results and measured results reached an agreement with the maximum difference of 15%; all important air brake system features were well simulated. Computing time was compared for simulations with and without parallel computing. The computing time for the conventional sequential computing scheme was about 6.7 times slower than real-time. Parallel computing using four computing cores decreased the computing time by 70%. Real-time simulations were achieved by parallel computing using eight computer cores.

Tire models used in vehicle dynamics simulation for CAE durability and ride comfort assessment need to be capable of predicting the non-linear deformation and enveloping characteristics which occur when traversing large road obstacles. Normally, transient dynamic characteristics of a rolling tire are determined from tire rig tests, and the tire parameters are transferred into multi-body system for vehicle dynamic analysis. However rig design limitations mean that tests cannot be carried out in the most severe conditions, particularly for traversing high ramp or large obstacles. However, using detailed FE tire models, such tests can be carried out virtually. A FE tire model was developed specifically for this purpose using explicit integration in ABAQUS™. Tire enveloping tests in traversing obstacles of different sizes were then carried out, virtually, using the validated FE tire model. Satisfactory results of transient responses were obtained by comparison with the experimental tests for the tire traversing obstacles with different heights. Tire transient dynamic behaviour was investigated by analysing the influence of tire rolling velocity and height of road obstacle on transient spindle responses, dynamic stiffness, together with tire deformation for the tire impacting obstacles. Finally, the investigation showed that longitudinal dynamic stiffness decreases when the tire traverses a higher obstacle. In addition, with the increase of height of road obstacle, the resonant amplitude of spindle force response as well as the tire deformation becomes larger in both longitudinal and vertical directions, especially for the tire rolling over 25 mm × 25 mm rectangular obstacle. Also, it is found that higher travelling velocity of the tire leads to higher resonant amplitude of spindle forces in the vertical direction.

Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire–obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire–obstacle contact interface. A new Takagi–Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics.

Aiming at accurate predictions of coupler/daft gear systems' dynamic behaviour. Based on the analysis of structural and kinematic relations of two typical systems, hysteretic nonlinear draft gear models and 9DOF (Degree of freedom) detailed coupler/draft gear system models were developed. Frictional pairs on coupling surfaces and coupler tails along with interactive aligning shoulder models were integrated into system models. Utilizing two detailed locomotive models simulations were performed on a section of special track to validate the rationalities of coupler/draft gear system models. Simulation results indicate that all parts in coupler/draft gear system models can fullfill their roles, and the system models themselves can reproduce the run-time behaviour of coupler/draft gear systems with high reliability and accuracy.

As a very substantial aspect of vehicle dynamics and vibrational controlling, it is essential to ascertain the role of tyre-obstacle collision kinetics. Ride comfort assessments are pivotal in predicting the non-linear deformation, particularly when traversing large road obstacles. The present study focuses on the assessment of tyres travelling over different obstacles using a well-equipped soil bin facility and a single-wheel tester. The vertically and longitudinally oriented load cells were responsible for measuring the vertical and longitudinal force variations. Additionally, a finite element tyre model was developed specially for this purpose using explicit integration in ABAQUS ™. It was found that with the increase of the resonant amplitude of spindle force response as well as the tyre deformation becomes greater in both longitudinal and vertical directions. It was revealed that the new proposed model can serve as a functional alternative to classical modelling tools for the prediction of nonlinear wheel dynamics.

Finite element (FE) tyre models are widely used for predicting tyre characteristics and dynamic properties used for vehicle dynamics simulation for CAE durability, ride comfort and handling assessment. Particularly, tyre FE simulation can be used to predict variation in tyre characteristics which would be too tedious to measure using laboratory tests, such as those due to changes in the tyre reinforcement material or structural layup. The cornering force characteristics of a tyre is one of the most influential factors in full vehicle dynamics simulation, having a significant effect on the directional control and handling stability of the vehicle. A validated FE tyre model developed using ABAQUS was used in parametric studies to determine factors affecting cornering force and aligning moment characteristics using design of experiments (DOE) method. Prediction of the cornering properties of the rolling tyre demonstrated the sensitivity of tyre cornering properties to tyre design parameters and operating conditions.