Lateral Vehicle Research Articles

LFT-Based Identification of Lateral Vehicle Dynamics

This paper presents a novel estimation and identification approach for lateral vehicle dynamics. The algorithm leverages on a Linear Fraction Transform (LFT) reformulation of vehicle and tyre models, allowing for a simple and computationally efficient inclusion of complex and nonlinear dynamic models, like, for example, two-wheels, four-wheels or single-track as vehicle model, and Pacejka, brush or Fiala as tyre model. As a result, this technique can be easily adopted in the development of an online identification system, able to run on a standard embedded device, implementing a flexible identification procedure that can handle different driving conditions, up to the limits of handling, different vehicle modelling approaches, and different input measurements. Experimental results demonstrate the effectiveness of the proposal, either in a persistent excitation and in a non-persistent excitation scenario.

Effect of Distracting Factors on Driving Performance: A Review

The number of traffic accidents because of distracted driving is increasing rapidly worldwide. Hence, the main objective of the present study is to review the effects of different distracting factors on driving performance indicators. Distracting factors considered in this study are roadside advertisements (billboards), mobile use, in-built vehicle systems, and sleepiness; and driving performance indicators are lane deviation, reaction time, and speed variation. Studies from existing literature reveal that all the distracting factors distract drivers from forwarding roadways in many ways. The location and content displayed on the billboard and the use of mobile phones increase reaction time. However, the former decreases the driver’s ability to control the vehicle, and the latter increases the speed variation and reduces lane-keeping capacity. Lateral vehicle control and reaction time are compromised when drivers engage in searching for songs or videos on music players. When sleepiness occurs, drivers exhibit a higher standard deviation of speed and a decreased headway distance. Nevertheless, most of the studies in this area are carried out in developed countries like the USA and European countries. Therefore, a detailed study and further research in developing countries like India, where activities like installing billboards and mobile phone use are increasing day by day due to the rapid urbanization of major cities in the country, are quite essential. Doi: 10.28991/CEJ-2022-08-02-014 Full Text: PDF

Nonlinear Time-Delay Observer-Based Control to Estimate Vehicle States: Lateral Vehicle Model

This paper deals with the state estimation and control problem for nonlinear lateral vehicle dynamics with time delays. First, a novel time-varying delay vehicle model described as a Takagi-Sugeno fuzzy model is presented. In particular, it is considered that the lateral force contains an air resistance term which is assumed to be a quadratic function of the lateral vehicle velocity. A time-varying delay has been included in the vehicle states by a simple formula in order to capture brake actuation aspects or other practical aspects that may generate a delayed response, while the nonlinear part of the vehicle model is described as a Lipschitz function. A Takagi-Sugeno time-delay observer-based control that satisfies the Lipschitz condition is proposed to get closed-loop stability conditions. These results generalize existing ones in the literature on lateral dynamics control. Additionally, we provide a new methodology for the controller and observer gains design that can be cast as linear matrix inequality constraints. Finally, we illustrate our results with numerical examples, which also reveal the negative effect of not considering the presence of delays in the controller design.

Modeling of occupant behavior under lateral vehicle motion (Proposing of control laws based on human posture control mechanism and experimental validation)

Modeling of occupant behavior under lateral vehicle motion (Proposing of control laws based on human posture control mechanism and experimental validation)

Game-Based Lateral and Longitudinal Coupling Control for Autonomous Vehicle Trajectory Tracking

In this paper, we propose a game-based coupling controller to solve the problem of autonomous vehicles in the trajectory tracking process. The controller ensures path tracking accuracy and shows good tracking performance for the desired speed profile that changes based on navigation while driving. Furthermore, it improves ride comfort and addresses the lateral stability problems that occur when applying classical model predictive control (MPC) controllers. We implemented this controller to plan the trajectory by considering the fundamental interaction between longitudinal and lateral vehicle dynamics, as well as to track the trajectory by using a receding horizon strategy. A comparative study was conducted to compare the proposed controller with the classical MPC controller. Based on CarMaker-Matlab/Simulink co-simulations, this controller outperformed the classical MPC in terms of trajectory tracking performance. Moreover, it exhibits excellent ride comfort and lateral stability that passengers will experience.

Vehicle localization combining non-linear state observer with artificial neural network

The developing of autonomous drive is needed to make people life more comfortable and safer, and one of the important skills to make possible the reliability of the all control system is a good localization of the vehicle. In this study, a no-linear state observer was developed using the Unscented Kalman Filter (UKF) algorithm, to estimate the global position, global orientation, and local speeds of a car inside a known path. A characterization of the sensors input measures was made and the measures of longitudinal and lateral vehicle speed were added using an Artificial Neural Network (ANN) trained in simulated manoeuvres. In this way, it was possible to reduce the error that the observer make on the estimation of the lateral vehicle speed, and so of the side slip angle, making possible an improvement of the control activity. To assess this increase in performance, a Montecarlo analysis was made comparing the architecture proposed, ANN+UKF, with state observed, UKF, with no input measure of lateral speed. The tests were done in co-simulation environment of Vi-Grade’s CarRealTime software and Matlab-Simulink.

How precision teaching can shape drivers’ lateral control over time

The present work describes a driving simulator experiment aimed at verifying if different feedback systems may have a positive effect on the participants’ driving behaviour in terms of lateral vehicle control over time. Participants performed the same track 4 times: the first time (baseline) no feedback was delivered, whereas the next three times either an auditory or multimodal (that is, visual and auditory) feedback was presented in case of error. About 25% of the participants in the initial sample were tested again after a month without any feedback. The results showed that the positive effect of the feedback with respect to the baseline track in terms of lower standard deviation of lateral position (SDLP), lower mean lateral speed (LS) and lower standard deviation of steering angle (SDSTEER) was maintained over time. Thanks to the precision teaching technique, drivers improved the investigated driving parameters while maintaining a correct position inside the lane.

A New Discrete-Time Interval Estimator for Vehicle Side-Slip Angle Estimation

The information about vehicle side-slip angle is essential given its relation to lateral stability and its importance for active safety control systems. Since direct measurement of the side-slip angle is expensive, developing efficient indirect techniques to extract this information from available sensor measurements is essential. This paper proposes a novel, cost-effective indirect strategy for the robust estimation of the side-slip angle using a state observer that relies on a discrete-time Linear Parameter Varying (LPV) lateral vehicle dynamic model. The proposed algorithm relies on a finite-time interval LMI-based observer, which accounts for the uncertainties on the model parameters (cornering stiffness) with known upper and lower uncertainty bounds. The simulation results show the effective and robust estimation performance of the proposed observer under various changes in system parameters. Moreover, comparison results with ℋ∞ set-membership observer for discrete-time LPV systems show the effectiveness of the developed observer.

Incremental End-to-End Learning for Lateral Control in Autonomous Driving

The use of high-quality data is required to complete the job of lateral control utilizing Behavioral Cloning (BC) through an End-to-End (E2E) learning system. The majority of E2E learning systems gather this high-quality data all at once before beginning the training phase (i.e., the training process does not start until the end of the data collection process). The demand for high-quality data necessitates a large amount of human effort and substantial time and money spent waiting for data collection to be completed. As a result, it is critical to find a viable option to reduce both the time and cost of data collecting while also maintaining the performance of a trained vehicle controller. This paper offers a novel behavioral cloning approach for lateral vehicle control to address the aforementioned problems. The proposed technique begins by collecting the least amount of human driving data possible. The data from human drivers are utilized for training a convolutional neural network for lateral control. The trained neural network is subsequently deployed to the vehicle’s automated driving controller, replacing a human driver. At this point, a human driver is out of the loop, and an automated driving controller, trained by the initial data from a human driver, drives the vehicle to collect further training data. The driving data obtained are sent into a convolutional neural network training module, then the newly trained neural network is deployed to the automated driving controller that will drive the vehicle further. The data collection alternates neural network training processes using the collected data until the neural network learns to correctly associate an image input with a steering angle. The proposed incremental approach was extensively tested in simulated environments, and the results are promising, only 3.81% (1,061 out of 27,884) of the total data came from a human driver. The incrementally trained neural networks using data collected by automated controllers were able to drive the vehicle in two different tracks successfully. The AI chauffeur was able to drive the vehicle on Track B for more than 70% of the track even though it has not seen the track before.

AUKF based unified estimation scheme for non-linear vehicle dynamics

The paper presents an additive unscented Kalman filter (AUKF) based modular approach to estimate lateral vehicle dynamics and tyre forces. In this approach, a simplified single-track vehicular model and a dynamics-oriented tyre model are adopted to represent the vehicular motion. Subsequently, multiple observer modules for each dynamical state are designed and integrated into a unified estimation scheme (UES-AUKF). Two additional non-modular observers using AUKF and extended Kalman filter (EKF) are designed for comparative analysis of estimation accuracy and computational efficiency of the designed scheme. The simplified model and the designed estimators are simulated using double lane change (DLC) and sinusoidal (Sine) manoeuvres for high and low µ surfaces, respectively, and the results are analysed. Thereby the scheme is further validated using real vehicle dataset for estimation accuracy. Simulation results for the simplified vehicle and the tyre model conform with the standard results with acceptable deviations. Also, the proposed scheme exhibits improved accuracy with reduced computational time as compared to the non-modular observers.

T-S fuzzy-model-based adaptive cruise control for longitudinal car-following considering vehicle lateral stability

Adaptive cruise control is one of the essential technologies of advanced driver assistance systems, which is used to maintain a safe distance between an ego vehicle and a preceding vehicle and has been extensively applied in the automotive industry and control community. Note that some vehicle manoeuvres may approach handling limits to prevent collisions under complex road conditions, which often leads to vehicle lateral instability while cruising. In this study, a T-S fuzzy model predictive control framework is applied to the problem of adaptive cruise control. Variations in the preceding vehicle velocity and road surface conditions are considered to formulate adaptive cruise control as a tracking control problem of a T-S fuzzy system subject to parameter uncertainties and external persistent perturbations. Then, a robust positively invariant set is introduced to derive an admissible T-S fuzzy controller by solving a min-max optimization problem under a series of linear matrix inequality constraints. Finally, a CarSim/MATLAB joint simulation is conducted to illustrate the effectiveness of the proposed method, which ensures longitudinal adaptive cruise control for a car-following scenario with lateral vehicle stability.

Safety-Guaranteed Control of Coupled Longitudinal and Lateral Vehicle Dynamics by Considering Tire Stability via CDBFs

In high-speed emergent driving scenarios of automated ground vehicles (AGVs), a lateral lane changing maneuver is often accompanied by longitudinal deceleration and/or acceleration. Such emergency scenarios not only couple vehicle longitudinal and lateral dynamics but also involve nonlinear, unstable, and coupled longitudinal and lateral tire forces. Therefore, a safety-guaranteed control design considering coupled longitudinal and lateral vehicle dynamics and tire stability is desired. Control Lyapunov function (CLF) and control barrier function (CBF) were integrated to find a safety-guaranteed controller while tracking a reference path. However, CBF cannot describe the control-dependent invariant sets that characterize vehicle lateral stability regions with stable tire forces. Thus, an integrated CLF and control-dependent barrier function (CDBF) control is designed for coupled longitudinal and lateral vehicle dynamics by considering tire stability to ensure vehicle driving safety in emergent driving scenarios. Simulation results are shown to verify the effectiveness of the proposed control design.

Conflict and Sensitivity Analysis of Articulated Vehicle Lateral Stability Based on Single-Track Model

Lateral stability is quite essential for the vehicle. For the vehicle with an articulated steering system, the vehicle load and steering system performance is quite different from the passenger car with the Ackman steering system. To investigate the influence of the tire characteristics and vehicle parameters on lateral stability, a single-track dynamic model is established based on the vehicle dynamic theory. The accuracy of the built model is validated by the field test result. The investigated parameters include the tire cornering stiffness, vehicle load, wheelbase, and speed. Based on the snaking steering maneuver, the lateral stability criteria including the yaw rate, vehicle sideslip angle, tire sideslip angle, and lateral force are calculated and compared. The sensitivity analysis of the tire and vehicle parameters on the lateral stability indicators is initiated. The results demonstrated that the parameters that affect the lateral vehicle stability the most are the load on the rear part and the tire cornering stiffness. The findings also lay a foundation for the optimization of the vehicle’s lateral stability.

Improving Sustainable Safe Transport via Automated Vehicle Control with Closed-Loop Matching

The concept of vehicle automation is a promising approach to achieve sustainable transport systems, especially in an urban context. Automation requires the integration of learning-based approaches and methods in control theory. Through the integration, a high amount of information in automation can be incorporated. Thus, a sustainable operation, i.e., energy-efficient and safe motion with automated vehicles, can be achieved. Despite the advantages of integration with learning-based approaches, enhanced vehicle automation poses crucial safety challenges. In this paper, a novel closed-loop matching method for control-oriented purposes in the context of vehicle control systems is presented. The goal of the method is to match the nonlinear vehicle dynamics to the dynamics of a linear system in a predefined structure; thus, a control-oriented model is obtained. The matching is achieved by an additional control input from a neural network, which is designed based on the input–output signals of the nonlinear vehicle system. In this paper, the process of closed-loop matching, i.e., the dataset generation, the training, and the evaluation of the neural network, is proposed. The evaluation process of the neural network through data-driven reachability analysis and statistical performance analysis methods is carried out. The proposed method is applied to achieve the path following functionality, in which the nonlinearities of the lateral vehicle dynamics are handled. The effectiveness of the closed-loop matching and the designed control functionality through high fidelity CarMaker simulations is illustrated.

Trajectory tracking of an autonomous vehicle using immersion and invariance control

An Immersion and Invariance [I & I] controller is designed to control the nonlinear lateral vehicle’s motion, using the steering angle as the only input. Similar to most of the lateral vehicle’s dynamics control law, the cornering stiffness parameters are involved in our proposed controller. Because of the tight relation between tire/road properties and the cornering stiffness parameters, they are not available from the outputs of the sensors and therefore, should be estimated for utilizing in the control law. An online data-driven identification is employed for estimating the cornering stiffness parameters. In addition, a robust model-based fault detection and approximation method in the presence of uncertainties via neural networks is presented. The performance of the obtained control law is investigated via simulation tests in different situations and in the presence of the disturbance. Moreover, some validation tests are performed using the CarSim software to show the effectiveness of our algorithm.

The Impact of Task Difficulty on Driving Performance in Preclinical Alzheimer's Disease (AD): A Pilot Study

<h3>Research Objectives</h3> To examine the impact of task difficulty on driving performance in preclinical Alzheimer's Disease (AD). We hypothesized that performance on driving simulator tasks will discriminate between drivers with preclinical AD and controls. <h3>Design</h3> A cross-sectional, observational study. <h3>Setting</h3> University of Kansas Medical Center. <h3>Participants</h3> Twenty participants (14 women) were included: 10 with preclinical AD [age 73.6±5] and 10 controls without preclinical AD [age 74.9±6.5], matched for age, sex, and cognitive status. <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measures</h3> Participants completed a cognitively demanding car following task with and without a dual task in a semi-immersive virtual reality driving simulator. The 2-back test was used as the dual-task test. Standard deviation of lateral position (SDLP) and speed (SDS) were the primary outcomes of lateral and longitudinal vehicle control, respectively. Two-way ANOVA were employed to investigate main effects of task complexity (single versus dual task) and group (preclinical AD vs controls). <h3>Results</h3> Both groups showed significant increase in SDLP when engaged in a dual task (p = 0.001). SDLP increased from (mean ± SD in feet) 0.50 ± 0.30 in single task to 0.70 ±0.40 in dual-task conditions in preclinical AD group. SDLP increased from 0.55 ±0.33 in single task to 0.88 ±0.34 in dual-task conditions in controls. No other within or between group effects were observed. <h3>Conclusions</h3> While the measure of lateral vehicle control (SDLP) showed to be sensitive to task complexity, the measure of speed (SDS) showed less sensitivity. Neither measure discriminated between drivers with preclinical AD and controls. A better powered study with more participants may show more meaningful findings. <h3>Author(s) Disclosures</h3> Any conflict of interest will be disclosed by coauthors. I, Pedram Ahmadnezhad (author) do not have any conflicts of interest.

Collaborative Optimization of Vehicle Crashworthiness under Frontal Impacts Based on Displacement Oriented Structure

Frontal impacts are the most frequent crash types and may generate extreme body in white (BIW) deformations. Vehicles are subjected to impact evaluation rating protocols such as 100 % Front Rigid Barrier (FRB) impact, 40 % Offset Deformable Barrier (ODB) impact and 25 % overlap (small overlap) impact. This paper proposes a collaborative optimization process using optimal Latin hypercube design (Opt LHD) and response surface methodology (RSM), to improve the vehicle crashworthiness in the frontal impacts, by considering displacement oriented structure (DOS). Upper and lower engine bay structure were considered herein to increase the lateral vehicle displacement during small overlap impact and decrease the impact force that transferred to the passenger compartment in the frontal impacts. The optimal results indicated that the acceleration of the B-pillar was reduced 0.4g in the FRB impact, the intrusion of the firewall was reduced by 55.13 % in the ODB impact, and the intrusion of passenger compartment during small overlap impact was decreased by an average of 53.88 %, with a maximum percentage of 71.41 % around left toepan, leading to an IIHS rating to acceptable from poor. The proposed crashworthiness design approach is effective in vehicle structure optimization for better frontal impacts performance.

Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).

Cross-combined UKF for vehicle sideslip angle estimation with a modified Dugoff tire model: design and experimental results

The knowledge of key vehicle states is crucial to guarantee adequate safety levels for modern passenger cars, for which active safety control systems are lifesavers. In this regard, vehicle sideslip angle is a pivotal state for the characterization of lateral vehicle behavior. However, measuring sideslip angle is expensive and unpractical, which has led to many years of research on techniques to estimate it instead. This paper presents a novel method to estimate vehicle sideslip angle, with an innovative combination of a kinematic-based approach and a dynamic-based approach: part of the output of the kinematic-based approach is fed as input to the dynamic-based approach, and vice-versa. The dynamic-based approach exploits an Unscented Kalman Filter (UKF) with a double-track vehicle model and a modified Dugoff tire model, that is simple yet ensures accuracy similar to the well-known Magic Formula. The proposed method is successfully assessed on a large amount of experimental data obtained on different race tracks, and compared with a traditional approach presented in the literature. Results show that the sideslip angle is estimated with an average error of 0.5 deg, and that the implemented cross-combination allows to further improve the estimation of the vehicle longitudinal velocity compared to current state-of-the-art techniques, with interesting perspectives for future onboard implementation.

Quasi-LPV Interconnected Observer Design for Full Vehicle Dynamics Estimation With Hardware Experiments

Safety systems claim an in-depth study of vehicle motion and tire-ground interaction for the design of a partially automated driving vehicle. This article addresses the quasi-linear parameter-varying Luenberger interconnected fuzzy observer to estimate both longitudinal and lateral vehicle dynamics simultaneously. In a different manner from the commonplace state of the art of vehicle state observers that consider single driving motion, the proposed approach considers the coupled dynamics with the tire-ground interaction to estimate the most important states while reducing the complexity related to the observability and conservatism. This consideration leads to a nonlinear parameter-dependent interconnected model with unmeasured premise variables. Then, the Takagi-Sugeno fuzzy form is considered to deal with the nonlinearities of the vehicle longitudinal and lateral speeds and the slip velocities as well as the steering angle. The concept of “input to state stability” is exploited using fuzzy nonquadratic Lyapunov stability arguments to guarantee the boundness of the estimation errors. A refinement has been proposed through the so-called Polya's theorem aiming to further reduce the conservativeness. Finally, performances and effectiveness of the suggested approach are evaluated through hardware experiments performed with the well-known Sherpa dynamic car simulator under real-world driving situations.