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This paper introduces the characteristics of the 4 wheel independent driving/4 wheel independent steering (4WID/4WIS) electric vehicle (EV). Models of Subsystems and the vehicle are constructed based on Matlab/simulink. The vehicle model allows the inputs of different drive torques and steer angles of four wheels. The dynamic characteristics of drive motors and steer motors are considered, and also it can reflect the vehicle longitudinal dynamics change due to the increase of the mass and inertia of the four wheels. Besides, drive mode selection function that is unique to this type vehicle is involved. Simulations and analyses of crab, oblique driving and zero radius turning which are the special conditions of 4WID/4WIS EV are conducted. The results show that the model can reflect the dynamic response characteristics. The model can be used to the simulation analyses of handling, stability, energy saving and control strategies verification of 4WID/4WIS EVs.

A braking force distribution strategy in integrated braking system composed of the main braking system and the auxiliary braking system based on braking pad wear control and hitch force control under non-emergency braking condition is proposed based on the Electronically Controlled Braking System (EBS) to reduce the difference in braking pad wear between different axles and to decrease hitch force between tractors and trailers. The proposed strategy distributes the braking force based on the desired braking intensity, the degree of the braking pad wear and the limits of certain braking regulations to solve the coupling problems between braking safety, economical efficiency of braking and the comfort of drivers. Computer co-simulations of the proposed strategy are performed.

Inerters have become a hot topic in recent years, especially in vehicle, train, and building suspension systems. The performance of a passive inerter and a semi-active inerter was analyzed and compared with each other and it showed that the semi-active inerter has much better performance than the passive inerter, especially with the Hybrid control method. Eight different layouts of suspensions were analyzed with a quarter car model in this paper. The adaptation of dimensionless parameters was considered for a semi-active suspension and the semi-active inerters. The performance of the semi-active inerter suspensions with different layouts was compared with a semi-active suspension with a conventional parallel spring-damper arrangement. It shows a semi-active suspension, with more simple configuration and lower cost, has similar or better compromise between ride and handling than a semi-active inerter with the Hybrid control.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

Models for off-road vehicles, such as farm equipment and military vehicles, require an off-road tire model in order to properly understand their dynamic behavior on off-road driving surfaces. Extensive literature can be found for on-road tire modeling, but not much can be found for off-road tire modeling. This paper presents an off-road tire model that was developed for use in vehicle handling studies. An on-road, dry asphalt tire model was first developed by performing rolling road force and moment testing. Off-road testing was then performed on dirt and gravel driving surfaces to develop scaling factors that explain how the lateral force behavior of the tire will scale from an on-road to an off-road situation. The tire models were used in vehicle simulation software to simulate vehicle behavior on various driving surfaces. The simulated vehicle response was compared to actual maximum speed before sliding vs. turning radius data for the studied vehicle to assess the tire model.

The driving behaviors of young drivers under the influence of anger are analyzed by driving simulator in this paper. A total of 12 subjects are enrolled during the experiment. Standardized videos are utilized to induce the driver's anger emotion. And the driver's electrocardiogram (ECG) signal is collected synchronously and compared before and after emotional trigger, which prove the validity of emotional trigger. Based on the result, the driver's driving performance under the straight road and the curve under normal state and angry state are compared and analyzed. The results of independent sample t-test show that there are significant differences in the running time of straight sections and the standard deviation of steering wheel angle in curves between normal and angry states. In conclusion, the longitudinal and lateral operation of drivers is unstable in angry state and the driver will be more destructive to the regular driving behavior.

The performance of five different skyhook control methods is studied experimentally, using a quarter-car rig. The control methods that are analyzed include: skyhook control, groundhook control, hybrid control, displacement skyhook, and relative displacement skyhook. Upon evaluating the performance of each method in frequency domain for various control conditions, they are compared with each other as well as with passive damping. The results indicate that no one control method outperforms other control methods at both the sprung and unsprung mass natural frequencies. Each method can perform better than the other control methods in some respect. Hybrid control, however, comes close to providing the best compromise between different dynamic demands on a primary suspension. The results indicate that hybrid control can offer benefits to both the sprung and unsprung mass with control gain settings that provide equal contributions from skyhook control and groundhook control.

Research of Adaptive Cruise Control (ACC) is an important issue of intelligent vehicle (IV). As we all known, a real and experienced driver can control vehicle's speed very well under every traffic environment of ACC working. So a direct and feasible way for establishing ACC controller is to build a human-like longitudinal control algorithm with the simulation of driver behavior of speed control. In this paper, a novel fuzzy self-tuning control algorithm of ACC is established and this controller's parameters can be tuned on-line based on the evaluation indexes that can describe how the driver consider the quality of dynamical characteristic of vehicle longitudinal dynamics. With the advantage of the controller's parameter on-line self-tuning, the computational workload from matching design of ACC controller is also efficiently reduced.

Driving style and skill classification are of great significance in human-oriented advanced driver-assistance system (ADAS) development. In this paper, we propose Fully-Connected Deep Neural Networks (FC-DNN) to classify drivers’ styles and skills with naturalistic driving data. Followed by the data collection and pre-processing, FC-DNN with a series of deep learning optimization algorithms are applied. In the experimental part, the proposed model is validated and compared with other commonly used supervised learning methods including the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and multilayer perceptron (MLP). The results show that the proposed model has a higher Macro F1 score than other methods. In addition, we discussed the effect of different time window sizes on experimental results. The results show that the driving information of 1s can improve the final evaluation score of the model.

The accuracy of tire forces directly affects the vehicle dynamics model precision and determines the ability of the model to develop the simulation platform or design the control strategy. In the high slip angle, due to the complex interactions at tire-road interfaces, the forces generated by the tires are high nonlinearity and uncertainty, which pose issues in calculating tire force accurately. This paper presents a hybrid physical and data-driven tire force calculation framework, which can satisfy the high nonlinearity and uncertainty condition, improve the model accuracy and effectively leverage prior knowledge of physical laws. The parameter identification for the physical tire model and the data-based compensation for the unknown errors between the physical tire model and actual tire force data are contained in this framework. First, the parameters in the selected combined-slip Burckhardt tire model are identified by the nonlinear least square method with tire test data.

Taking full advantage of available traffic environment information, making control decisions, and then planning trajectory systematically under structured roads conditions is a critical part of intelligent vehicle. In this article, a lane-changing decision-making method for intelligent vehicle is proposed based on acceleration field. Firstly, an acceleration field related to relative velocity and relative distance was built based on the analysis of braking process, and acceleration was taken as an indicator of safety evaluation. Then, a lane-changing decision method was set up with acceleration field while considering driver’s habits, traffic efficiency and safety. Furthermore, velocity regulation was also introduced in the lane-changing decision method to make it more flexible.

Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models.

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment.

This study pertains to motion control algorithms using statistical calculations based on relative displacement measurements, in particular where the rattle space is strictly limited by fixed end-stops and a load leveling system that allows for roll to go undetected by the sensors. One such application is the cab suspension of semi trucks that use widely-spaced springs and dampers and a load leveling system that is placed between the suspensions, near the center line of the cab. In such systems it is possible for the suspension on the two sides of the vehicle to settle at different ride heights due to uneven loading or the crown of the road. This paper will compare the use of two moving average signals (one positive and one negative) to the use of one root mean square (RMS) signal, all calculated based on the relative displacement measurement.

This work pertains to laboratory testing of truck cab suspensions for the purpose of improving in-cab ride quality. It describes the testing procedure of a complete truck cab suspension while still being mounted on the vehicle. It allows for testing with minimal amount of resources, limited to two mobile actuators and minimal modifications to the stock vehicle. The actuators can be attached to any axle through a set of modified brake drums and excite the drive axle in a vertical plane. The excitation signal sent to the actuators can be in phase for a heave type motion or out of phase for a roll motion. The chassis shock absorbers are replaced with rigid links to prevent the actuator input from becoming filtered by the primary suspension. This allows the input to reach the cab suspension more directly and the cab to be excited across a broader range of frequencies.

Taking a good longitudinal braking performance on flat and level road of tractor and semi-trailer combination as a target, in order to achieve an ideal braking force distribution among axles, while the vehicle deceleration is just depend on the driver's intention, not affected by the variation of semi-trailer mass, the paper proposes a model based vehicle mass identification and braking force distribution strategy. The strategy identifies the driver's braking intention via braking pedal, estimates semi-trailer's mass during the building process of braking pressure in brake chamber, distributes braking force among axles by using the estimated mass. And a double closed-loop regulation of the vehicle deceleration and utilization adhesion coefficient of each axle is presented, in order to eliminate the bad effect of mass estimation error, and enhance the robustness of the whole algorithm. A simulation is conducted by utilizing MATLAB/Simulink and TruckSim.

A multitude of recent studies are suggestive of the EV as a paramount representative of the NEV, its development direction is transformed from “individuals adapt to vehicles” to “vehicles serve for occupants”. The multi-mode drive control technology is relatively mature in traditional auto control sphere, however, a host of EV continues to use a single control strategy, which lacks of flexibility and diversity, little if nothing interprets the vehicle performances. Furthermore, due to the complex road environment and peculiarity of vehicle occupants that different requirement has been made for vehicle performance. To solve above problems, this paper uses the key technology of mathematical statistics process in MATLAB, such as the mean, linear fitting and discrete algorithms to clean up, screening and classification the original data in general rules, and based on short trips in the segments of kinematics analysis method to establish a representative of quintessential driving cycle.

Driver Assistance and Autonomous Driving features are becoming nearly ubiquitous in new vehicles. The intent of the Driver Assistant features is to assist the driver in making safer decisions. The intent of Autonomous Driving features is to execute vehicle maneuvers, without human intervention, in a safe manner. The overall goal of Driver Assistance and Autonomous Driving features is to reduce accidents, injuries, and deaths with a comforting driving experience. However, different drivers can react differently to advanced automated driving technology. It is therefore important to consider and improve the adaptability of these advances based on driver behavior. In this paper, a human-centric approach is adopted to provide an enriching driving experience. We perform data analysis of the naturalistic behavior of drivers when performing lane change maneuvers by extracting features from extensive Second Strategic Highway Research Program (SHRP2) data of over 5,400,000 data files.

Modeling the tire forces and moments (F&M) generation, during combined slip maneuvers, which involves cornering and braking/driving at the same time, is essential for the predictive vehicle performance analysis. In this study, a new semi-empirical method is introduced to estimate the tire combined slip F&M characteristics based on flat belt testing machine measurement data. This model is intended to be used in the virtual tire design optimization process. Therefore, it should include high accuracy, ease of parameterization, and fast computational time. Regression is used to convert measured F&M into pure slip multi-dimensional interpolant functions modified by weighting functions. Accurate combined slip F&M predictions are created by modifying pure slip F&M with empirically determined shape functions. Transient effects are reproduced using standard relaxation length equations. The model calculates F&M at the center of the contact patch.

Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors.