Driver Command Research Articles

Implementing Model Predictive Control (MPC) in Steer-by-Wire Systems for Future Automated Vehicles

Abstract The need for future green sustainable transportation represents a motivation for the researchers in order to create a better society. In this paper a proposed Steer-by-Wire system for future automated vehicles is analysed by implementing model predictive control (MPC) controller that would optimise the desired behaviour of the vehicle. By using MATLAB/Simulink, a bicycle vehicle model is simulated in standardised test manoeuvres according to standard ISO:7401. These results are used as referent data for the desired trajectory of the vehicle and as input data for the MPC controller. The idea of using these results for the controller is generated by the desire of creating a shared information intelligent systems. Shared information and control in the future intelligent systems would be crucial for creating the desired sustainable transport. By traveling, the vehicle would collect data of the desired measured variables and those data would be send to every other vehicle. Therefore, the next vehicle would possess the previous data and would try to improve the vehicle response and safety. Every optimised trajectory would be shared with the rest of the vehicles and this cycle would result in further optimization and in creation of a safer, more comfortable and more sustainable transport. The optimisation is done by the MPC controller which is responsible for defining the steering wheel angle and therefore creating the Steer-by-Wire system that is entirely defined by the MPC controller without driver commands. By comparing the trajectory of the automated vehicle and the desired trajectory gained from the previous simulations, the desired steering wheel angle is defined. This results in creation of a Steer-by-Wire system that improves the vehicle dynamics, safety and dynamic response of the vehicle. The mathematical modelling, results of the simulations and advantages of using the MPC controller for the Steer-by-Wire system in automated vehicle are presented.

New generation of passenger vehicles: FCV or HEV?

This paper compares the performance and parameter characteristics of Fuel Cell Vehicles (FCV) and Hybrid Electric Vehicles (HEV) with a view towards an objective assessment of the relative performance of these vehicles. Firstly, the main characteristics of hybrid electric vehicles (HEVs) as low emission vehicles (LEVs), including presumed high efficiency is considered. Then, comparisons for well-to-wheels emissions for various vehicles are presented. Well-to-wheels efficiencies, emissions, and fuel economy are also compared for FCVs and HEVs. In addition, other issues like battery types for HEVs and HFCVs are explored in this paper. The potential control strategies for FCVs and HEVs will be discussed and compared. In both FCVs and HEVs, best control strategies need to rely on predicting the driver command, which presents a particularly challenging opportunity for further development. Finally, this paper gives the comparison of total costs for FCVs and HEVs.

Measurements-based constrained control optimization in presence of uncertainties with application to the driver commands for high-speed trains

The railway world is undergoing major changes. The advent of new technologies allows us to rethink the train system and face new challenges, but one must not forget all the ecological constraints that are now accentuated by the increase in energy costs. This paper focuses on the optimization of the driver commands to limit the energy consumption of the trains under punctuality and security constraints. This problem falls within the framework of control optimization problems for nonlinear dynamic mechanical systems in the presence of constraints and uncertainties. A four-step approach is then proposed in this paper to solve this problem: (1) the introduction of simplified and fast-to-evaluate models to model the nonlinear dynamic behavior of the train and its energy consumption; (2) the identification in a Bayesian formalism of the parameters on which these models depend from on-track measurements on commercial trains; (3) the reformulation of the optimization problem so that it integrates the uncertainties related to an imperfect knowledge of these estimated parameters; (4) the resolution of the optimization problem using evolutionary algorithms. The main specificity of this work lies in the fact that not only the objective function to be minimized, here the energy consumed by the train, is impacted by the uncertainties, but also the admissibility constraints of the solution, here punctuality and operating safety. The integration of the uncertainties in the search for the control function is thus not trivial and requires several original adaptations in order to make the final optimization problem well posed.

Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling.

This paper presents a novel motion control strategy based on model predictive control (MPC) for distributed drive electric vehicles (DDEVs), aiming to simultaneously control the longitudinal and lateral motion while considering efficiency and the driving feeling. Initially, we analyze the vehicle's dynamic model, considering the vehicle body and in-wheel motors, to establish the foundation for model predictive control. Subsequently, we propose a model predictive direct motion control (MPDMC) approach that utilizes a single CPU to directly follow the driver's commands by generating voltage references with a minimum cost function. The cost function of MPDMC is constructed, incorporating factors such as the longitudinal velocity, yaw rate, lateral displacement, and efficiency. We extensively analyze the weighting parameters of the cost function and introduce an optimization algorithm based on particle swarm optimization (PSO). This algorithm takes into account the aforementioned factors as well as the driving feeling, which is evaluated using a trained long short-term memory (LSTM) neural network. The LSTM network labels the response under different weighting parameters in various working conditions, i.e., "Nor", "Eco", and "Spt". Finally, we evaluate the performance of the optimized MPDMC through simulations conducted using MATLAB and CarSim software. Four typical scenarios are considered, and the results demonstrate that the optimized MPDMC outperforms the baseline methods, achieving the best performance.

Shared Steering Control With Predictive Risk Field Enabled by Digital Twin

A core issue of safe human-machine cooperative driving lies in dynamic assessment of the rapidly evolving driving risks. Given that, this paper proposes a shared controller for safe and human-friendly cooperative driving based on predictive risk assessment enabled by Digital Twin technologies. In the digital world, we create a fine-grained digital replica of the driving scene comprising historical motions of vehicles, as well as roadway geometries and topologies. On the basis of that, spatial-temporal interactive features are obtained with a deep learning-based model and subsequently decoded to predict future trajectories of each target vehicle in the neighborhood of the ego-vehicle. In the physical world, the predicted trajectories of neighboring vehicles are integrated into the risk distribution to construct predictive risk fields. A novel shared controller in the framework of multi-objective MPC is designed to minimize the driving risk while matching driver's commands, so that safe cooperative driving is achieved in a smooth and minimal-intervention manner. The results of driver-in-the-loop simulation experiments demonstrate the enabling role of the Digital Twin in improving the assessment of risk in highly dynamic scenes through taking the motion trends of dynamic agents into account. The results also show the superiority of the Digital Twin-based shared controller in terms of implementing cooperation in time while honoring the driver's commands whenever possible.

Indirect Shared Control Strategy for Human-Machine Cooperative Driving on Hazardous Curvy Roads

Effective and friendly human-machine cooperative driving is considered as a feasible solution to filling the gap between assisted driving and highly automated driving. This paper proposes an indirect shared control strategy, in both longitudinal and lateral directions, for safe cooperative driving on curvy roads, which often pose hazards in real-world driving. At the tactical decision level of the proposed strategy, driving authorities are dynamically allocated between human and machine based on the risk assessed with a data-driven gaussian processes regression (GPR) model, which is trained to generate dynamic safety envelopes of longitudinal and lateral control commands, namely vehicle speed and front wheel steering angle, from observed road conditions and vehicle states. At the maneuver execution level, a multi-objective hierarchical MPC controller is built to combine human-machine control commands smoothly for collision-free driving while simultaneously maintaining vehicle stability and mitigating human-machine conflicts. Both matching the driver's commands and tracking the reference trajectories are formulated into objectives of the optimization-based controller. The weight factor of each objective is adjusted according to the authorities allocated at the decision level. The results of experiments conducted on a 3 degree of freedom motion-base driving simulator show that the proposed strategy accomplishes effective and friendly cooperative driving on curvy roads. It's also verified that the proposed strategy is adaptable to unseen scenarios, mainly owing to the superiority of the GPR-based risk assessment model, driven by class-balanced training data, in modeling the nonlinearity and uncertainty of driving risks.

ANALYSIS OF BRAKE SYSTEMS IN MOTOR VEHICLES USING PRACTICAL EXAMPLES FROM THE ASPECT OF THEIR DIAGNOSTICS

The part of the braking system that has the task of transmitting the command activated by the driver to thebrakes is called the transmission mechanism. The transmission mechanism itself can be different depending on howit is constructed and conceptually executed. As for the conceptual solution, the question arises as to whether thetransmission method itself must be such that the driver's command is only transmitted to the brakes or the driver'scommand itself is handed over to a separate energy system. The energy system itself can be such that it additionallyhelps the activation of the brakes (servo brake force boosters) or completely takes over the activation of the brakes,with the creation of a certain braking force on the wheels, and these are the so-called mechanisms with full servoaction. Today we have the following transmission mechanisms in use: Mechanical transmission, hydraulic with orwithout servo amplification, hydraulic with full servo action, pneumatic with full servo action, hydro-pneumaticwith servo amplification or with full servo action. The very choice of these systems depends on a large number offactors, but the main one is - how much energy must be delivered to the brakes. Each of these systems is explainedseparately in the paper. A mechanical transmission mechanism is a system that does not have any additional servoamplification, but the command of the driver or the person operating the machine is directly transmitted to thebrakes. Based on this, we can conclude that the application of this transmission mechanism in brake systems is quitelimited. Today, this transmission mechanism is only used as a service brake on some slower trucks and tractors. Thehydraulic transmission mechanism is the system that is most common in brake systems of passenger, light cargo anddelivery vehicles. In the case of vehicles weighing up to 1000 kg, the driver alone is sufficient to develop thenecessary energy for braking, so it is not necessary to additionally support the braking force with servo boosters. Butthat's why smaller trucks and delivery vehicles need additional help from a servo booster to activate the brakingforce. Servo amplifiers have become an integral part of the equipment in passenger vehicles primarily due to thesafety, security and comfort of passengers. In contrast to the mechanical transmission, this system is morecomplicated in terms of performance and its operation is based on the transmission of pressure through the brakefluid from the main brake cylinder to the brake cylinder in the brakes. The pressure created by the brake fluid actson the pistons in the cylinder itself and in this way force is created and the brakes are activated. The main advantageof this system is the very safety and safer braking, because with the hydraulic system it is possible to make adistribution in several independent branches to the cylinders on the brakes, and this is one of the basic satisfactoryrequirements in the ECE regulation that the brakes must also have an auxiliary braking system in case dismissal ofthe principal. The system itself consists of: the pedal, which is activated by pressing the foot on the pedal itself, themain brake cylinder, the distribution system, the working brake cylinders in the brakes and the brake itself.

Optimisation of train speed to limit energy consumption

The speed profile of a train plays an important role in energy consumption and resulting costs. The industrial objective of this work is thus to develop a method to reduce the energy consumed by a train over a journey by playing on the driver commands (traction and braking forces) while respecting punctuality constraints. First, a rigid body approach (Lagrangian formalism) is introduced to characterise the dynamics of the train. In particular, the aerodynamic (including the wind effect), traction, and braking forces are taken into account, and a special attention is paid to the vertical and lateral characteristics of the track as they play a key role in the train dynamics. Second, a model for energy consumption and recovery (thanks to dynamic braking) is introduced. Experimental measurements of a high-speed line are then used to estimate the parameters on which the two previous models are based and to validate their predictive capacities. The optimisation problem under constraints is finally solved using an evolutionary algorithm where the constraints are implemented using an augmented Lagrangian formalism. The performance of the proposed method in terms of speed optimisation and energy consumption reduction is compared to measurements associated with commercial trains.

Reference‐free approach for mitigating human–machine conflicts in shared control of automated vehicles

Shared control is a promising approach that can facilitate the mutual understanding and cooperative control between human and machine. In this study, a novel reference-free framework for shared control of automated vehicles is proposed with the aim of mitigating conflicts between the human driver and the automatic system during their interactions. Position constraint and time to collision are deployed to prevent collision and guarantee stability by limiting the yaw rate and sideslip angle of the vehicle. The design of the shared controller is formulated as a model predictive control problem. It determines vehicle state based on the current driver command and implements control actions only if the vehicle state induced by the human driver violates the pre-defined constraints. The system models, safety constraints and the proposed shared controller are then integrated. The algorithm is validated through computer simulation under two different driving scenarios. The simulation results show that the proposed reference-free approach can offer the human driver more degrees of freedom during shared control, effectively mitigating human–machine conflicts, compared to the previous work. In addition, the algorithm ensures the safety and stability of the system under risky driving conditions, validating its feasibility and effectiveness.

Feed-forward modeling and real-time implementation of an intelligent fuzzy logic-based energy management strategy in a series–parallel hybrid electric vehicle to improve fuel economy

A hybrid electric vehicle is powered by: the internal combustion engine and the battery-powered electric motor. These sources have specific operational characteristics, and it is necessary to match these characteristics for the efficient and smooth functioning of the vehicle. The nonlinearity and uncertainties in hybrid electric vehicle model require an intelligent controller to control the energy sharing between battery and engine. In this work, a fuzzy logic-enabled energy management strategy for the hybrid electric vehicle based on torque demand, battery state of charge and regenerative braking is designed and implemented. The proposed energy management strategy allows engine and motor to maneuver in their efficient operating regions. The designed hybrid electric vehicle and its control strategy follow the driver commands and regulations on vehicle performance and liquid fuel consumption. MATLAB/Simulink is used to carry out simulations, and then, the whole system is validated in real time on hardware-in-the-loop testing platform. This work employs an FPGA-based MicroLabBox hardware controller to validate real-time behavior. The proposed scheme results in better fuel economy, faster response and almost nil mismatch between desired and achieved vehicle speeds.

Power Electronic Control Design for Stable EV Motor and Battery Operation during a Route

Electric vehicles (EVs), during a route, should normally operate at the desired speed by effectively controlling the power that flows between their batteries and the electric motor/generator. To implement this task, in this paper, the voltage source AC/DC converter is considered as a controlled power interface between the electric machine and the output of the DC storage device; the DC/DC converter is used to automatically regulate the battery operating condition in accordance to the profile of the acting on the vehicle wheels, unknown external torque. Particularly, the speed is continuously regulated by the vehicle driver via the pedal while all other regulations for absorbing or regenerating energy are internally controlled. The driver command is acting as speed reference input on a PI outer-loop motor speed controller which, in its turn, drives a fast P inner-loop current controller operating in cascaded mode. In a similar manner, the machine and the battery performance are self-regulated by a pure PI current controller that achieves maximum electric torque per ampere operation of the motor and by a PI/P cascaded scheme for the DC-voltage/battery–current regulation, respectively. In order to exclude any possibility of instabilities and adverse impacts between the different parts, a rigorous analysis is deployed on the complete electromechanical system that involves the motor, the batteries, the converter dynamic models and the proposed controllers. Modeling the system in Euler–Lagrange nonlinear form and applying sequentially suitable Lyapunov techniques and the time-scale separation principle, a systematic method for tuning the gains of the inner- and outer-loop controllers is derived. Therefore, the proposed controller design procedure guarantees asymptotic stability by considering the accurate system model as a whole. Finally, the proposed approach is validated by simulating realistic route conditions, performed under unknown external torque variations.

Coordinated Longitudinal and Lateral Motion Control for Four Wheel Independent Motor-Drive Electric Vehicle

For four wheel independent motor-drive electric vehicle, the vehicle longitudinal and lateral motion can be controlled by distributing the driving and regenerative braking torques of four wheel motors. To meet the driving command of driver and keep the vehicle lateral stability, a hierarchical control system is proposed in this paper. In the upper layer, a nonlinear model predictive control is implemented to solve the nonlinear multiinput multioutput, over-actuated problem. The controller is based on a nonlinear three degree-of-freedom model with nonlinear tire model, considering wheel slips as virtual control input. In the lower layer, the wheel slips are manipulated by a PID controller for generating driving and regenerative braking torques of the independent motors. This proposed controller is tested in a hardware-in-the-loop system with four typical maneuvers, which are constant velocity, accelerating, decelerating, and low road adhesion coefficient situations to show different driver command. The results show that the driver command of longitudinal and lateral motion control are both satisfied.

Optimal design of adaptive shaking vibration control for electric vehicles

ABSTRACTThis paper provides a complete design solution about adaptive optimal shaking vibration control for electric vehicles. A general 4-DOF and 5-order linear torsional vibration model is established under given wheel speed, and the frequency characteristics of the vibration system are elaborately analysed in terms of variation of wheel speed and different model parameters. Aiming at decreasing the shaking vibration at the least sacrifice of acceleration loss, and improving the robustness of the system against external disturbance, a combination of feed-forward and feed-backward adaptive control structure is proposed. Further, a non-linear multi-constraint optimisation problem is formulated for solving the optimal adaptive control variables within the two-dimensional design space composed of the wheel speed and driver's torque command. Furthermore, the distribution of the optimal adaptive control variables within the design space and its extended application under different tyre road conditions are discussed. Eventually, several simulation test cases are particularly designed to verify the performances of the controller on all aspects. Test results show that the optimal adaptive controller achieves satisfactory anti-shaking vibration, acceleration maintaining and robustness performances within the whole adaptive design space as desired.

Nonlinear Driver Parameter Estimation and Driver Steering Behavior Analysis for ADAS Using Field Test Data

In the development of advanced driver-assist systems (ADAS) for lane-keeping or cornering, one important design objective is to appropriately share the steering control with the driver. The steering behavior of the driver must therefore be well characterized for the design of a high-performance ADAS controller. This paper adopts the well-known two-point visual driver model to characterize the steering behavior of the driver, and conducts a series of field tests to identify the model parameters and validate this model in real-world scenarios. An extended Kalman filter and an unscented Kalman filter are implemented for estimating the driver parameters using either a joint-state estimation algorithm or a dual estimation algorithm. The estimated parameters for different types of drivers are analyzed and compared. The results show that the two-point visual driver model captures realistic driving behavior with time-varying, but not necessarily constant, parameters. A wavelet analysis of the driver steering command shows that distinct driver classes can be identified by analyzing the smoothness of the driver command using the Lipschitz exponents of the recorded signals.

Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems

To the average driver, the concept of automation in driving infers that they can become completely ‘hands and feet free’. This is a common misconception, however, one that has been shown through the application of Network Analysis to new Cruise Assist technologies that may feature on our roads by 2020. Through the adoption of a Systems Theoretic approach, this paper introduces the concept of driver-initiated automation which reflects the role of the driver in highly automated driving systems. Using a combination of traditional task analysis and the application of quantitative network metrics, this agent-based modelling paper shows how the role of the driver remains an integral part of the driving system implicating the need for designers to ensure they are provided with the tools necessary to remain actively in-the-loop despite giving increasing opportunities to delegate their control to the automated subsystems.Practitioner Summary: This paper describes and analyses a driver-initiated command and control system of automation using representations afforded by task and social networks to understand how drivers remain actively involved in the task. A network analysis of different driver commands suggests that such a strategy does maintain the driver in the control loop.

Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability

Steer-by-wire technology enables vehicle safety systems to share control with a driver through augmentation of the driver's steering commands. Advances in sensing technologies empower these systems further with real-time information about the surrounding environment. Leveraging these advancements in vehicle actuation and sensing, the authors present a shared control framework for obstacle avoidance and stability control using two safe driving envelopes. One of these envelopes is defined by the vehicle handling limits, whereas the other is defined by spatial limitations imposed by lane boundaries and obstacles. A model predictive control (MPC) scheme determines at each time step if the current driver command allows for a safe vehicle trajectory within these two envelopes, intervening only when such a trajectory does not exist. In this way, the controller shares control with the driver in a minimally invasive manner while avoiding obstacles and preventing loss of control. The optimal control problem underlying the controller is inherently nonconvex but is solved as a set of convex problems allowing for reliable real-time implementation. This approach is validated on an experimental vehicle working with human drivers to negotiate obstacles in a low friction environment.

Multi-sensor selection optimization and driver warning decision for dynamical virtual driving simulator

This article presents a framework for the design and development of a multi-sensor selection optimization mechanism for a driver assistance simulation system. The multi-sensor selection mechanism is formulated as an optimization problem and solved by integer linear programming. Both static and dynamic driving scenarios are considered in the formulation; where a static scenario denotes an off-line multi-sensor selection problem and a dynamic scenario denotes an online driving condition transformation problem. The objective function for determining an optimal set of sensors is in terms of guaranteed coverage, energy, bandwidth, and reliability of sensors. For different driving conditions, and driver commands, a selection algorithm is developed for identifying the environment around the vehicle and warning the driver. The selection algorithm and multiple-vehicle driving examples are successfully implemented in a virtual driving simulator.

2A1-J10 LiDARを用いた電動車椅子の運転アシストシステム

In this paper, we propose a driving assist system for electric wheelchair. In several countries as the rate of senior citizens increases, the number of electric wheelchair users and the accidents by the senior becomes larger. To decrease these numbers, the wheelchair can avoid or stop before the confliction with the obstacles, is needed. Using the pointcloud from tilted 2D LiDAR, we construct the environment map around own position and generate the practicable paths. Considering the driver's command, safety, and smoothness, the path is selected and followed finally. To prove the validity of our method, we ride on the wheelchair and drive the several environment in our campus.

Development of an optimal driver command interpreter for vehicle dynamics control

This paper introduces a new driver command interpreter (DCI) for vehicle dynamics control, providing optimal calculation of desired CG forces and moments based on driver inputs and road conditions. The proposed DCI is established based on principles of optimal linear quadratic regulator (LQR) theory and vehicle dynamics. The optimal feed-forward and feedback gains of proposed DCI can be updated in real time by online matrix calculation. Analytical simulations and experimental test results under various driving conditions are presented to evaluate the proposed DCI and vehicle dynamics control system. The simulation and experimental results indicate that the vehicle dynamics control system using the proposed DCI can effectively stabilise the vehicle motion and improve the vehicle handling under critical driving conditions.

A path-following driver/vehicle model with optimized lateral dynamic controller

Reduction in traffic congestion and overall number of accidents, especially within the last decade, can be attributed to the enormous progress in active safety. Vehicle path following control with the presence of driver commands can be regarded as one of the important issues in vehicle active safety systems development and more realistic explanation of vehicle path tracking problem. In this paper, an integrated driver/DYC control system is presented that regulates the steering angle and yaw moment, considering driver previewed path. Thus, the driver previewed distance, the heading error and the lateral deviation between the vehicle and desired path are used as inputs. Then, the controller determines and applies a corrective steering angle and a direct yaw moment to make the vehicle follow the desired path. A PID controller with optimized gains is used for the control of integrated driver/DYC system. Genetic Algorithm as an intelligent optimization method is utilized to adapt PID controller gains for various working situations. Proposed integrated driver/DYC controller is examined on lane change manuvers andthe sensitivity of the control system is investigated through the changes in the driver model and vehicle parameters. Simulation results show the pronounced effectiveness of the controller in vehicle path following and stability.