Driver-vehicle System Research Articles

An individualized robust stability control strategy for active front steering vehicles

To improve the vehicle stability and driver steering performance, this paper presents an individualized yaw stability control strategy based on H∞ robust control for active front steering (AFS) vehicles. A driver-vehicle system, including a driver steering model and a vehicle dynamics model with AFS, is formed. To analyze the steering characteristics of different drivers, a set of driving data of 36 drivers is collected, and the driver’s characteristics parameters are identified by using the particle swarm optimization (PSO) algorithm. A general evaluation function considering the trajectory tracking performance, vehicle stability, driver workloads, and driver’s characteristics parameters are established to evaluate the comprehensive steering performance. To accomplish the personalized control of vehicle yaw stability, an individualized H∞ robust yaw stability controller is presented by adjusting the gain of the weighting function according to the general evaluation of each driver. Driver-in-the-loop experiment is conducted based on the Matlab/Simulink-CarSim®-Prescan co-simulation platform, and the results demonstrates that the proposed control strategy can provide driver with individualized driving assistance while improving the overall driving performance and reducing the driver’s workloads.

Real-time forecasting of driver-vehicle dynamics on 3D roads: A deep-learning framework leveraging Bayesian optimisation

Most state-of-the-art works in trajectory forecasting for automotive target predicting the pose and orientation of the agents in the scene. This represents a particularly useful problem, for instance in autonomous driving, but it does not cover a spectrum of applications in control and simulation that require information on vehicle dynamics features other than pose and orientation. Also, multi-step dynamic simulation of complex multibody models does not seem to be a viable solution for real-time long-term prediction, due to the high computational time required. To bridge this gap, we present a deep-learning framework to model and predict the evolution of the coupled driver-vehicle system dynamics jointly on a complex road geometry. It consists of two components. The first, a neural network predictor, is based on Long Short-Term Memory autoencoders and fuses the information on the road geometry and the past driver-vehicle system dynamics to produce context-aware predictions. The second, a Bayesian optimiser, is proposed to tune some significant hyperparameters of the network. These govern the network complexity, as well as the features importance. The result is a self-tunable framework with real-time applicability, which allows the user to specify the features of interest. The approach has been validated with a case study centred on motion cueing algorithms, using a dataset collected during test sessions of a non-professional driver on a dynamic driving simulator. A 3D track with complex geometry has been employed as driving environment to render the prediction task challenging. Finally, the robustness of the neural network to changes in the driver and track was investigated to set guidelines for future works.

Personalized Lane Change Planning and Control By Imitation Learning From Drivers

In this article, we propose a novel personalized planning and control approach for lane change assistance system which can efficiently learn a model prediction control (MPC)-based driver-specific lane-changing policy via end-to-end imitation learning from a few driver demonstrations. Specifically, we build a novel learnable predictive model of the vehicle-driver system and design an adaptable cost function for the MPC-based lane change controller. We then calculate the gradient of the imitation loss with respect to the personalization parameters of the model and cost function via differentiating the optimality conditions, and update those parameters to minimize the imitation loss in an end-to-end fashion. A semi-physical simulation on a driving simulator and a closed-loop test on a real vehicle are conducted to validate the learning ability and personalized control performance. The results show that 1) the proposed method can automatically implement both the generalized and the personalized lane change planning and control by learning from demonstration data; 2) the proposed controller can adapt to different driver-specific behaviors; and 3) the proposed approach outperforms the model-free learning approach in terms of imitation accuracy, interpretability, data efficiency, and generalized performance.

Modeling and numerical simulations of multilane vehicular traffic by active particles methods

This paper deals with the modeling and numerical simulations of multilane vehicular traffic according to the kinetic theory of active particles methods. The main idea of this theory is to consider each driver–vehicle system as a micro-system, where the microscopic state of particles is described by position, velocity, and activity which is an appropriate variable for modeling the quality of the driver-vehicle. The interactions between micro-systems are modeled by stochastic game theory. This leads to the derivation of a mathematical model within the framework of the approach of kinetic theory. The well-posedness of the related Cauchy problem for the spatially homogeneous case is established. Numerical simulations are carried out to show the ability of the proposed model to reproduce the empirical data such as the asymptotic property in time and the emerging behavior of clusters with a particular focus on the road environment conditions.

Heavy-Vehicle Response to Crosswind: Evaluation of Driver Reactions Using a Dynamic Driving Simulator

Heavy vehicles exiting (or entering) a tunnel at high speed under a strong crosswind is a particularly critical condition since the aerodynamic load changes drastically, greatly affecting the lateral stability of the vehicle. Often, active control systems (active suspensions, active front steering, etc.) and infrastructure elements (e.g., wind fences) are proposed to reduce the induced risks. To help the design of these devices, the present paper investigates the response of the vehicle–driver system in the case of a high-sided lorry exiting a tunnel under crosswind, by using Driver-In-the-Loop simulations. The study was performed using the dynamic driving simulator of Politecnico di Milano and 28 test drivers. Vehicle and aerodynamic models have been developed to reproduce the phenomenon in a highly immersive environment. During the tests, several combinations of vehicle and wind speed were considered. The effect of vehicle loading condition (Empty and Laden) was also investigated. The performed tests allowed us to gain information about the sequence of the driver’s actions and associated delays, which may induce lane deviation or, in the worst case, rollover. It was found that lane invasion may happen for ratios of lateral aerodynamic force over vehicle weight force bigger than 0.1, while rollover could happen for ratios bigger than 0.3. Moreover, it was found that the driver’s response typically happens with a delay of ∼0.25 s with respect to the onset of the crosswind stimulus.

Koopman operator-based driver-vehicle dynamic model for shared control systems

Driver-automation shared control has recently proven to be a promising technical scheme to enhance the safety of vehicles, but desire to improve the user acceptance of the shared control systems requires a high-fidelity driver-vehicle dynamic model for characterizing the closed-loop driver-vehicle interaction. In this paper, an emergent pure data-driven modelling approach, i.e., the Koopman operator, is adopted to capture the intrinsic characteristics of the driver-vehicle system dynamics and yield an explicit control-oriented driver-vehicle model in an infinite-dimensional space. Moreover, an online hybrid delay-embedded extended dynamic mode decomposition algorithm is proposed to improve the finite-dimensional approximation accuracy of the Koopman operator. The resulting driver-vehicle model has a simple linear structure which facilitates further shared controller design. Driver-in-the-loop experiments on a driving simulator are conducted to collect driving data for the identification and verification of the proposed modelling approach, and the effectiveness and superiority are demonstrated by experimental results. In particular, it is found that an online updating mechanism for the Koopman model is of great importance to capture the driver’s adaptive behaviour in the course of driver-automation interaction.

Cooperative Game-Based Driver Assistance Control for Vehicles Suffering Actuator Faults

To assist different styles of drivers to maintain vehicle stability in the event of actuator function failures, this paper proposes a cooperative game-based robust fault-tolerant control (FTC) strategy for four-wheel independent actuated electric vehicles. The Takagi-Sugeno fuzzy structure is used to model a driver-vehicle system to describe driver characteristics in the FTC scheme and deal with the nonlinearity among states of the complex system. Viewing the FTC process as a dynamic cooperative game so that the actuators of the driver-vehicle system are described as the players. The interactions among players are modeled by the distributed model predictive control scheme. Finally, an adaptive compensation control mechanism is developed to make the cooperative game paradigm more robust. Simulation verification is used to reveal the influence and the inherent characteristics of task weights in the cooperative game. Besides, two severe failure cases are studied via the hardware-in-the-loop test to prove that the proposed method can maintain the stability of the failed vehicle driving by different drivers.

A Simulation Study of Human Sensory Dynamics and Driver–Vehicle Response

Abstract In previous work, a driver model with visual and vestibular sensory dynamics was developed, identified, and validated, using data from moving-base driving simulator experiments. In this paper, the predictions, applications, and limitations of the driver model are explored through a series of simulations. The aim is to address as yet unanswered questions about the role of visual and vestibular sensory dynamics in the driver–vehicle system. The visual system is found to be the dominant sensory system, with the influence of vestibular measurements increasing with the proportion of random disturbances on the vehicle. State perception errors increase significantly with the proportion of random disturbances on the vehicle. The driver's simulated control performance is unchanged with signal amplitude above perception threshold levels, although it is slightly affected by high-pass filtering of the physical motion such as might be experienced in a driving simulator. The sensory driver model led to a significantly different optimum value of vehicle center of mass position compared to that obtained using an idealized driver model. The results motivate the adoption of sensory driver models in a vehicle design setting. Further work could be undertaken to improve the sensorimotor noise model.

Human-Centered Torque Vectoring Control for Distributed Drive Electric Vehicle Considering Driving Characteristics

Most of the existing torque vectoring control (TVC) strategies are “vehicle-centered,” hence, fail to consider how driving characteristics affect the vehicle's motion. To address this issue, we propose a hierarchically structured human-<;bold/>centered TVC (HCTVC) for distributed drive electric vehicles (EVs). To do this we design the experiment to characterize the drivers' behavior with different skills. Then a global objective function is formulated considering the driver's physical and mental workload, as well as the agility and stability of the vehicle, to optimize the overall performance of the driver-vehicle system. Next, an adaptive second-order sliding mode controller (ASOSM) based on the backstepping method is designed as the upper-level controller to improve system robustness. Finally, a multi-objective optimization algorithm based on the weight self-tuning strategy is developed as the lower-level controller to solve the torque distribution problem. We further obtain the analytical solution to this optimization problem. Experimental results on the asphalt and icy roads confirm that the proposed HCTVC significantly enhances the vehicle transient response and reduces the driver's workload.

A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider

A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.

Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness.

This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.

Robust fault-tolerant control for four-wheel individually actuated electric vehicle considering driver steering characteristics

A robust fault-tolerant control scheme for distributed actuated electric vehicles is proposed to maintain vehicle stability suffering actuator faults while considering the driver personality differences. The proposed scheme integrates the cooperative game and terminal sliding mode control into the framework of the feedback linearization method (FLM). Firstly, the nonlinearities of the driver-vehicle system are treated by the knowledge of Lie derivative, and then a set of controllable virtual subsystems is obtained through diffeomorphism. To achieve multi-objective cooperation, the interaction framework of virtual subsystems is modeled based on cooperative game theory, which provides a basic feedback control scheme (BFCS). Finally, a terminal sliding mode technology-based active compensation control scheme is integrated into BFCS to handle the systemic disturbances caused by actuator faults. An implementation of hardware-in-the-loop verifies that the stability of the vehicle under the control of the developed approach can be guaranteed for different drivers and different fault types.

Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

Formalizing the heterogeneity of the vehicle-driver system to reproduce traffic oscillations

Road traffic congestion is the result of various phenomena often of random nature and not directly observable with empirical experiments. This makes it difficult to clearly understand the empirically observed traffic instabilities. The vehicles’ acceleration/deceleration patterns are known to trigger instabilities in the traffic flow under congestion. It has been empirically observed that free-flow pockets or voids may arise when there is a difference in the speeds and the spacing between the follower and the leader increases. During these moments, the trajectory is dictated mainly by the characteristics of the vehicle and the behaviour of the driver and not by the interactions with the leader. Voids have been identified as triggers for instabilities in both macro and micro level, which influence traffic externalities such as fuel consumption and emissions. In the literature, such behaviour is usually reproduced by injecting noise to the results of car-following models in order to create fluctuations in the instantaneous vehicles’ acceleration.This paper proposes a novel car-following approach that takes as input the driver and the vehicle characteristics and explicitly reproduces the impact of the vehicle dynamics and the driver’s behaviour by adopting the Microsimulation Free-flow aCceleration (MFC) model. The congested part of the model corresponds to the Lagrangian discretization of the LWR model and guarantees a full consistency at the macroscopic scale with congested waves propagating accordingly to the first-order traffic flow theory.By introducing naturalistic variation in the driving styles (timid and aggressive drivers) and the vehicle characteristics (specification from different vehicle models), the proposed model can reproduce realistic traffic flow oscillations, similar to those observed empirically. An advantage of the proposed model is that it does not require the injection of any noise in the instantaneous vehicle accelerations.The proposed methodology has been tested by studying a) the traffic flow oscillations produced by the model in a one-lane road uphill simulation scenario, b) the ability of the model to reproduce car-following instabilities observed in three car-following trajectory datasets and c) the ability of the model to produce realistic fuel consumption estimates. The results prove the robustness of the proposed model and the ability to describe traffic flow oscillations as a consequence of the combination of driving style and vehicle’s technical specifications.

Gain Scheduling Output Feedback Control for Vehicle Path Tracking Considering Input Saturation

This paper presents a gain scheduling output feedback control method to reduce driver workload and improve driving performance by considering input saturation. The driver–vehicle system model is developed by considering tire cornering stiffness uncertainties and different driver parameter uncertainties. Meanwhile, the input saturation is also considered in the driver-vehicle system. A quadratic Lyapunov function is designed to solve the optimization problem with uncertainties and input saturation. The results, which are based on the MATLAB-CarSim co-simulation platform, indicate that the robust controller not only improves the convergence rate of the state but also reduces the steering workload of the driver.

Impaired Driver Assistance Control With Gain-Scheduling Composite Nonlinear Feedback for Vehicle Trajectory Tracking

AbstractImpaired drivers have deteriorated driving performances that may greatly endanger the road safety. It is challenging to design assistance controllers for the impaired drivers because the impaired driver behaviors are difficult to be modeled and considered in the controller design. To this end, this paper proposes a gain-scheduling composite nonlinear feedback (GCNF) controller to assist the impaired drivers. A driver-vehicle system containing the impaired driver model is developed. The steering behaviors of the impaired drivers are described by deteriorating the driver model parameters and including the driver uncertainties. Based on the driver-vehicle system, a GCNF controller integrating the gain-scheduling technique, the weighted H∞ performance, and the composite nonlinear feedback algorithm is designed to handle the declined driving performances and improve the transient performances. The designed GCNF controller is validated in the carsim simulations. The simulation results show that the GCNF controller can effectively assist the impaired drivers of different impaired levels to reduce the trajectory tracking errors and improve the driving performances.

Path Tracking Control for Active Rear Steering Vehicles Considering Driver Steering Characteristics

In this paper, a path tracking control for active rear steering vehicles considering driver steering characteristics is proposed to help drivers track the desired vehicle trajectory in a tailored way. Firstly, the driver model is established, and based on the driver steering data, the driver parameters are identified by the least square method. Based on driver model parameter identification, driver and vehicle models are formed for the MPC controller design. In MPC controller design, global objective function, considering the task performance, vehicle stability, driver's physical workloads, mental workloads and actuator performance, is designed to optimize the overall performance of the driver-vehicle system. On the constraint, the driver operating limit, actuator physical limit and vehicle stability performance limit are also considered to make the controller feasible to in the real practice. Simulation results under different conditions show that the controller can significantly improve the system performance and reduce the driver's workloads.

Human Driver Interaction with Self-Balancing Vehicles’ Dynamics

This paper deals with a research activity concerning two-wheel self-balancing vehicles, with particular reference to the interaction between the driver and the vehicle’s dynamics. The usefulness and the flexibility of this kind of vehicle make it a very interesting device for smart mobility. In particular, a dynamical model of a two wheeled self-balancing vehicle and of his driver is presented here. It consists of a Multibody model of a real vehicle, designed and built in the Mechatronics and Mechanical Systems Dynamics lab. of our University, and of a driver model with 3 driven joints. The overall 3D model developed allows simulating the interaction between human (driver) and machine (vehicle), taking into consideration also the coupling between longitudinal motion and turn. The vehicle’s control system has been synthesized using the pole placement approach, starting from a simplified 2 dof planar model of the vehicle, considering the driver fixed with vehicle chassis. Through proper linearization, a state space description has been obtained and used to tune pole position, not only for stability but also for optimal response. Through co-simulation between the controller (modelled in Matlab-Simulink) and the vehicle-driver system (modelled with MSC.Adams) several tests have been performed on the full model, to assess its behaviour under different conditions like set point following and disturbance rejection, considering both a passive and an active driver. As far as the influence of the driver on the vehicle’s dynamics is concerned, the paper shows that it is possible to detect the dynamic forces exerted by the driver on the vehicle. According to these signals, control strategies to switch the vehicle in safe mode can be implemented. In the paper, some issues related to safety have been highlighted.

Driver-Automation Cooperation Oriented Approach for Shared Control of Lane Keeping Assist Systems

This paper presents a novel shared control concept for lane keeping assist (LKA) systems of intelligent vehicles. The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Two local optimal-based controllers with two predefined objectives (i.e., lane keeping and conflict management) are designed in the operational part. The control supervisor in the tactical part aims to provide a decision-making signal which allows for a smooth transition between two local controllers. The control design is based on a human-in-the-loop vehicle system to improve the mutual driver-automation understanding, thus reducing or avoiding the conflict. The closed-loop stability of the whole driver-vehicle system can be rigorously guaranteed using the Lyapunov stability argument. In particular, the control design is formulated as an LMI optimization which can be easily solved with numerical solvers. The effectiveness of the proposed shared control method is clearly demonstrated through various hardware experiments with human drivers.

Improving stability and lane-keeping performance for multi-articulated vehicles using vector follower control

ABSTRACT The present study discusses the effects of stability control for multi-articulated vehicles on the lane-keeping performance. Vector follower control (VFC) compensates the direction difference angles between the velocity vector at the front articulation point and the trailer using active steering of the trailer wheels. The control effects are evaluated by examining computer simulation with the driver-vehicle system, which consists of a multi-articulated vehicle model, a driver model, and VFC. VFC prevents amplification of the lateral displacements of rearward trailers, whereas the lane-keeping performance of a vehicle without VFC deteriorates. VFC does not, however, contribute to the performance of the tractor or the steering operation of the driver. A lane-keeping-assistance (LKA) system using direct yaw-moment control is applied to the tractor. The LKA system suppresses the lateral deviation of the tractor and the steering effort of the driver. In addition, the combination of VFC and the LKA system makes it possible to reduce the lateral deviations of all of the vehicles, as well as the steering effort of the driver.