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Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method.

The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm’s potential for practical application.

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model’s overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model’s generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization.

Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation.

Abstract Lane‐changing manoeuvres are essential for daily driving tasks. Safety and comfort are the primary issues to be considered during the lane‐changing manoeuvres of autonomous vehicles. However, because the traffic environment is complicated and changeable, executing fixed lane‐changing manoeuvres cannot guarantee safety and comfort. Thus, it is important to find a flexible method that can plan and re‐plan a lane‐changing path according to the traffic environment in real time. But that remains smooth to ensure the vehicle occupants stay comfortable. Therefore, this paper proposes a method for planning a flexible and smooth lane‐changing path in real time. The proposed method uses the bi‐elementary paths to ensure comfort, and clothoid and arc curves are superimposed on the bi‐elementary paths to add re‐planning flexibility. Additionally, lookup tables and reasonable approximations are employed to quickly generate and evaluate lane‐changing paths. The results of the simulations demonstrate that the proposed method is able to plan and re‐plan a smooth lane‐changing path. Then, the results of vehicle experiments performed based on the embedded controller platform show that the proposed method guarantees real‐time performance, that the planned path is easy to follow, and that the planned paths make autonomous vehicle lane‐changing manoeuvres safe and comfortable.

This paper investigated the problem of multi-target tracking (MTT) over a vehicle sensor network. A novel adaptive square root cubature joint probabilistic data association (ASRCJPDA) was proposed. Motivated by enhancing the stability of joint probabilistic data association (JPDA) in practical application, the proposed methodology implemented a numerically stabled cubature Kalman filter for JPDA state estimate process. It improved numerical stability and acquired more accurate estimated results. Additionally, enlightened by enhancing the real time efficiency of JPDA, an adaptive tracking gate designed for the JPDA measurement associate process was proposed. It combined with the kinematics of vehicle to reduce the computational complexity of data association, which improved the robustness of MTT in complex scenarios. The virtual vehicle target tracking scenarios were built in PreScan software in order to better simulate the real traffic condition. Simulations of target tracking examples are presented to show great effectiveness and superiority of the proposed method.

The driving torques of all four wheels of distributed drive electric vehicles are independently controllable, and acceleration slip regulation (ASR) can be realized through the coordinated effort of torque actuators. Considering the multiple actuator coupling, nonlinearity, uncertainty and actuator faults in an ASR system, an adaptive nonsingular terminal sliding mode (NTSM) fault-tolerant control method-based multi-agent system (MAS) is proposed to address the above problems of an ASR system in this paper. First, based on multi-agent theory, a four- wheel independent drive ASR system is decomposed into four separate driving wheel agent systems to reduce the model dimension and transform the design of the ASR system controller into the design of a single driving wheel agent controller to reduce the computational complexity. Second, to address the unknown uncertainty of an actuator fault in an ASR system, an adaptive NTSM controller for a single driving wheel agent is designed to make the actual slip ratio track the ideal slip ratio for the ASR system in finite time. The controller switch item gains are selected by using an adaptive estimation mechanism for a single driving wheel agent controller to address the gain overestimation problem. This approach ensures that the actual control signal is smooth and that chattering phenomena and energy consumption are reduced. For actuator faults, a Lyapunov function based on multiagent theory is designed for a single driving wheel agent to avoid the impact of the coupling subsystem fault. Third, the Simulink and CarSim cosimulation results show that the proposed method improves the fault tolerance and robustness. The system can realize the actual slip ratio, track the optimal slip ratio in a finite time under different road adhesion conditions and effectively avoid the wheel slippage problem.

The problem that it is difficult to balance vehicle stability and economy at the same time under the starting steering condition of a four-wheel independent drive electric vehicle (4WIDEV) is addressed. In this paper, we propose a coordinated optimal control method of AFS and DYC for a four-wheel independent drive electric vehicle based on the MAS model. Firstly, the angular velocity of the transverse pendulum at the center of mass and the lateral deflection angle of the center of mass are decoupled by vector transformation, and the two-degree-of-freedom eight-input model of the vehicle is transformed into four two-degree-of-freedom two-input models, and the reduced-dimensional system is regarded as four agents. Based on the hardware connection structure and communication topology of the four-wheel independent drive electric vehicle, the reduced-dimensional model of 4WIDEV AFS and DYC coordinated optimal control is established based on graph theory. Secondly, the deviation of the vehicle transverse swing angular velocity and mass lateral deflection angle from their ideal values is oriented by combining sliding mode variable structure control (SMC) with distributed model predictive control (DMPC). A discrete dynamic sliding mode surface function is proposed for the ith agent to improve the robustness of the system in response to parameter variations and disturbances. Considering the stability and economy of the ith agent, an active front wheel steering and drive torque optimization control method based on SMC and DMPC is proposed for engineering applications. Finally, a hardware-in-the-loop (HIL) test bench is built for experimental verification, and the results show that the steering angle is in the range of 0–5°, and the proposed method effectively weighs the system dynamic performance, computational efficiency, and the economy of the whole vehicle. Compared with the conventional centralized control method, the torque-solving speed is improved by 32.33 times, and the electrical consumption of the wheel motor is reduced by 16.6%.

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

Inspired by the empirical dynamic characteristics of the load of real logistics network, we propose a dynamic cascading failure model against cascading failure, which is more suitable for complex logistics network by adding dynamic factors based on the nonlinear load-capacity model under initial residual capacity load-redistribution strategy. The simulation is executed on the complex logistics network model and the results show that the controllability robustness and economy after cascading failure based on the dynamic cascading failure model is feasible and effective. It can effectively reduce the logistics cost and enhance controllability robustness against cascading failure by adjusting the network cost e and capacity parameter γ, so as to balance the controllability robustness and economy for the complex logistics network.

In this study, a hierarchical chassis control strategy is designed to enhance vehicle economy and safety for four-wheel independent-drive electric vehicles (FWID-EVs). In the upper-level, a vehicle dynamics model based on multiple agents is proposed, and a distributed model predictive control (DMPC) method is designed to dimensionally solve the problem of tracking the center-of-mass torque of the demanded velocity trajectory and stability parameters. In the bottom-level, a multi-objective torque distribution strategy that weighs safety, dynamics and economy based on multi-agent theory is designed by comprehensively considering the motor efficiency and tire energy loss. Finally, a hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this paper. The results show that the method in this paper is effective in tracking the desired trajectory and further enhancing the stability of the vehicle under various conditions. Compared with other algorithms, while guaranteeing safety and dynamics, the energy consumption of the powertrain is reduced by 9.51%.

In this article, a driver model–based direct yaw moment controller, selected as the upper controller, is developed, of which the control target is determined through a reference driver model in accordance with the driver’s intention. The sliding surface is chosen by the difference between the desired yaw rate and the real output yaw rate. Then, the desired yaw moment is calculated by the sliding mode control. In the lower controller, a novel control torque distribution strategy is designed based on the analysis of the tire characteristics. In addition, an admissible control set of the control torques is calculated in real time through an embedded tire model “UniTire.” Finally, a driver-in-the-loop experiment, via the driving simulator, is conducted to verify the proposed direct yaw moment controller.

Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.

In this paper, an axisymmetric finite element (FE) model of an air spring was carried out with the software ABAQUS to design its target vertical stiffness. The bellows was simulated by the reinforced surface element. The compressed gas in the cavity of the air spring was represented by the hydrostatic fluid element. The target stiffness is obtained by modifying the valid area of the cross section. At last, the results of experiment coincided well with the simulation data. The study shows that the static stiffness of air spring is sensitive to the effective area of the cross section. The conclusion has certain practical significance for the design and the optimization of the same kind of air spring. [ABSTRACT FROM AUTHOR]

Based on the preview optimal simple artificial neural network driver model (POSANN), a new driver model, considering jerky dynamics and the tracing error between the real track and the planned path, is established. In this paper, themodeling for the drivervehicle system is firstly described, and the relationship between weighting coefficients of driver model and system parameters is examined through test data. Secondly, the corresponding road test results are presented in order to verify the vehicle model and obtain the information on drive model and vehicle parameters. Finally, the simulations are carried out via CarSim. Simulation results indicate that the jerky dynamics need to be considered and the proposed new driver model can achieve a better pathfollowing performance compared with the POSANN driver model. [ABSTRACT FROM AUTHOR]

Vehicle dynamics are directly dependent on tire-road contact forces and torqueswhich are themselves dependent on thewheels' load and tire-road friction characteristics. An acquisition of the road disturbance property is essential for the enhancement of vehicle suspension control systems. This paper focuses on designing an adaptive real-time road profile estimation observer considering load variation via vehicle vertical dynamics. Firstly, a road profile estimator based on a linear Kalman filter is proposed, which has great advantages on vehicle online control. Secondly, to minimize the estimation errors, an online identification system based on the Recursive Least-Squares Estimation is applied to estimate sprung mass, which is used to refresh the system matrix of the adaptive observer to improve the road estimation efficiency. Last, for mining road category from the estimated various road profile sequencse, a road categorizer considering road frequency and amplitude simultaneously is approached and its efficiency is validated via numerical simulations, in which the road condition is categorized into six special ranges, and this road detection strategy can provide the suspension control system with a better compromise for the vehicle ride comfort, handling, and safety performance. [ABSTRACT FROM AUTHOR]

Vehicle behaviors under extreme cornering are complex events that might result in rollover accidents. Simulations have been extensively used in the auto industry to protect vehicles from such accidents. Predictive capability of simulation relies on how accurate the math-based model represents the vehicle and its operating condition. Camber effects of tire are studied in this paper to promote the accuracy of simulation. First, a tire model with simplified camber effect is introduced, and the comparison between the model results and test data is shown. Then, a more precise model that includes camber effects on cornering stiffness are friction coefficient is presented. The comparison between these model results and test data is also given. In addition, the camber effects on tire overturning moment and loaded radius are studied. Finally, vehicle fishhook simulation with different tire models is conducted to further investigate the effect of tire camber on vehicle dynamics. [ABSTRACT FROM AUTHOR]

The UniTire model is a nonlinear and non-steady-state tyre model for vehicle dynamics simulation and control under complex wheel motion inputs involving large lateral slip, longitudinal slip, turn slip and camber. The model is now installed in the driving simulator in the Automobile Dynamic Simulation Laboratory at Jilin University for studying vehicle dynamics and their control systems. Firstly, the nonlinear semiphysical steady-state tyre model, which compiles with analytical boundary conditions up to the third order of the simplified physical model, is presented. Special attention has been paid to the expression for the dynamic friction coefficient between the tyre and the road surface and to the modification of the direction of the resultant force under combined-slip conditions. Based on the analytical non-steady-state tyre model, the effective slip ratios and quasi-steady-state concept are introduced to represent the non-steady-state nonlinear dynamic tyre properties in transient and large-slip-ratio cases. Non-steady-state tyre models of first-order approximation and of high-order approximation are developed on the basis of contact stress propagation processes. The UniTire model has been verified by different pure and combined test data and its simulation covered various complex wheel motion inputs, such as large lateral slip, longitudinal slip, turn slip and camber. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the effects of dynamic road friction on a tyre's mechanical properties. Despite the fact that most existing analytical tyre models tend to ignore the speed-dependent effects on the tyre's forces, many experiments have demonstrated that the force and moment of a tyre actually vary with the travelling speed especially when the force and moment are nearly saturated. For this reason, the speed-dependent effects of tyre-road friction need to be reviewed intensively in an attempt to enhance the accuracy and reliability of the existing tyre models. In this paper, a tyre rubber friction tester and a series of testing methods are first developed. An analytical tyre model based on dynamic friction is then established by incorporating the speed effects into the model. Finally, comparison between test data and simulation results of the analytical model and the semiphysical model are conducted and analysed prior to the conclusions. [ABSTRACT FROM AUTHOR]

Driver modeling is identified as an important process, which may aid the evaluation and optimization of the vehicle performance and the development of intelligent vehicles as well. This paper aims to establish a directional control driver model with efficiency, accuracy and driver preference. Error Elimination Algorithm (EEA), an analytical method in driver modeling based on the Preview-Follower theory and the error tracking analysis, is presented. Driver preference parameter is then discussed. Numerical simulations of the proposed driver model is typical maneuvers are studied and compared with human driver test results. [ABSTRACT FROM AUTHOR]

Semi-active suspension has been increasing interests for its outstanding performance and high cost effectiveness, and the magnetorheological (MR) damper is a hotspot in these decades. For the MR damper design, here still are some farther challenges, such as, (1) how to cascade the ideal damper characteristic from full vehicle performance, and what is the ideal off-state performance and the controllable ratio; (2) how to fix the MR damper brought additional mass since the MR fluid density is three times than the traditional damper oil’s, and a promising lightweight and effective configuration and its systematical design approaches should be explored. In this article, the novel MR valve controlled damper (MRVD) and its top-down design methodology are proposed via both the configuration design and system syntheses. Results manifest that the developed MRVD prototype matches the target minibus requirement very well with consumption of 3 W drive power and 6.1 ml MR fluids, demonstrating superior dynamic performance, lightweight compact structure, low energy consumption and cost, and a promising application in semi-active suspensions. [ABSTRACT FROM AUTHOR]

Magnetorheological (MR) devices have been investigated intensively nowadays, of which MR valve is an important and hot application with the challenges of acquiring high pressure drop within compact configurations. Hence, a novel squeeze mode based MR valve (SMRV) is proposed in this paper, with highlights of high pressure drop and low power consumption within a compact and transplantable structure. SMRV’s characteristics are studied and its core parts are designed including the initial gaps, magnetic circuit and returning spring. The uniform-saturation magnetic intensity principle is proposed and a co-simulation optimal platform is developed to optimize magnetic intensity of the SMRV dimensions. Then, a prototype is developed and its steady-state performance is evaluated. The test results demonstrate that a pressure drop of 10.8 MPa and a controllable ratio of 5 at 1.0 A applied current are achieved within a transplantable configuration. Meanwhile, SMRV only consumes 1/400 W control power to dissipate 1 W fluid power and its power–volume consumption rate, PC · V/PD, is 3.3 × 102 mm3, which has a brilliant application prospect in hydraulic or mechatronic systems. [ABSTRACT FROM AUTHOR]