Double Lane Change Research Articles

Artificial Intelligence for Stability Control of Actuated In–Wheel Electric Vehicles with CarSim® Validation

This paper presents an active controller for electric vehicles in which active front steering and torque vectoring are control actions combined to improve the vehicle driving safety. The electric powertrain consists of four independent in–wheel electric motors situated on each corner. The control approach relies on an inverse optimal controller based on a neural network identifier of the vehicle plant. Moreover, to minimize the number of sensors needed for control purposes, the authors present a discrete–time reduced–order state observer for the estimation of vehicle lateral and roll dynamics. The use of a neural network identifier presents some interesting advantages. Notably, unlike standard strategies, the proposed approach avoids the use of tire lateral forces or Pacejka’s tire parameters. In fact, the neural identification provides an input–affine model in which these quantities are absorbed by neural synaptic weights adapted online by an extended Kalman filter. From a practical standpoint, this eliminates the need of additional sensors, model tuning, or estimation stages. In addition, the yaw angle command given by the controller is converted into electric motor torques in order to ensure safe driving conditions. The mathematical models used to describe the electric machines are able to reproduce the dynamic behavior of Elaphe M700 in–wheel electric motors. Finally, quality and performances of the proposed control strategy are discussed in simulation, using a CarSim® full vehicle model running through a double–lane change maneuver.

Stability Control of Autonomous Ground Vehicles Using Control-Dependent Barrier Functions

In the development of autonomous ground vehicles (AGVs), guaranteeing vehicle driving safety is a major concern. Among various aspects that need to be thoughtfully considered for driving safety, vehicle stability is one of the most fundamental and important factors. In this paper, to describe a guaranteed vehicle stability control problem, a new time-varying control-dependent invariant set is introduced. Correspondingly, the concept of a time-varying control-dependent barrier function (CDBF) is proposed. The proposed time-varying CDBF is more general than conventional control barrier functions (CBF), since we additionally consider invariant sets that can be time-varying and control-dependent, which will have broader applications. Then, using the proposed framework, we design a vehicle stability control algorithm, which guarantees that the vehicle states are always kept in the time-varying and control-dependent lateral stability regions. Finally, the correctness and effectiveness of the proposed theory and control method are verified and discussed through illustrative simulation results of high-speed J-turn and double lane change maneuvers for an AGV.

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior.

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Linear Parameter Varying Path Tracking Control for Over-Actuated Electric Vehicles

This paper discusses a multi-layer linear parameter varying path tracking controller for autonomous full electric vehicles with 4 wheel drive. Many approaches for path tracking are present in the literature, but the majority of them utilize only the steering wheel. Nowadays, the massive introduction of electric vehicles opens new opportunities for vehicle dynamics control; multiple electric motors, via differential torque, offer an alternative way to influence vehicle motion. The multi-layer structure, managing separately steering wheel and electric motors, permits the exploitation of pre-existing control systems and maintains a straightforward tuning process. Furthermore, the robustness of the path tracking controller is enhanced by introducing tire nonlinearities. Linear parameter varying models offer a direct way to incorporate tire characteristics in the design process. Exploiting H∞ technique the scheduled controller can be seamlessly synthezised. The proposed strategy has been compared in simulation to a benchmark integrated, i.e., a unique controller that manages all the actuators, approach. During double lane changes beyond the vehicle limits, the multi-layer controller guarantees stability and excellent tracking with an average error of 12 and 24 cm on high and low grip respectively.

A comparison of free trajectory quasi-steady-state and transient vehicle models in minimum time manoeuvres

Lap-time optimisation programs utilising Quasi-Steady-State (QSS) vehicle models are computationally efficient, though inherent simplifications produce inaccuracies. An analysis of the prediction capabilities of a new QSS lap-time optimisation method is presented. A transient vehicle model is formulated with seven degrees-of-freedom (DOF) dynamics. The QSS vehicle model utilises point-mass dynamics, where accelerations are constrained by a bi-quintic piecewise spline surface produced with an identical transient vehicle model. Notable changes to the development of the new QSS model include, evaluation of the acceleration envelope in natural coordinate frame, and the implementation of a normal jerk limit which acts to artificially mimic the yaw inertia resistance during change of direction manoeuvres. Results of the minimum time solution are shown for left-hand turn, double lane change and full circuit analysis based on Adria International Raceway. An accuracy improvement against previous QSS models is presented. Minimum time results for the new QSS model are within for left turn, for Adria full circuit, and for double lane change. Optimal accelerations, track centreline offset and velocities are presented and compared.

Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic

Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.

Designing LQR Controllers for an Active Anti-roll Bar System with a Flexible Frame Model of a Single Unit Heavy Vehicle

Rollover accidents of heavy vehicles often cause serious consequences both in terms of vehicle and environmental damage as well the loss or injury of drivers, passengers and ordinary civilians. Currently, the active anti-roll bar system is considered as the most effective solution in enhancing vehicle roll stability. In this paper, we firstly investigated the role of a flexible frame of a single unit heavy vehicle in the rollover process. This approach is an important step forward in the research of the active anti-roll bar system. Then, the LQR control method is applied in designing controllers for the active anti-roll bar control system with this frame model. The active torque of the anti-roll bar system is considered as the control signal. The simulation results in the frequency and time domains with a double lane change maneuver show that the vehicle’s roll stability is improved by over 30 % compared to a vehicle using a passive anti-roll bar system.

Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of model predictive control (MPC) schemes and robust H_{infty } state feedback control (RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers’ performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.

Risk-Sensitive Rear-Wheel Steering Control Method Based on the Risk Potential Field

Various driving assistance systems have been developed to reduce the number of automobile accidents. However, the control laws of these assistance systems differ based on each situation, and the discontinuous control command value may be input instantaneously. Therefore, a seamless and unified control law for driving assistance systems that can be used in multiple situations is necessary to realize more versatile autonomous driving. Although studies have been conducted on four-wheel steering that steers the rear wheels, these studies considered the role of the rear wheels only to improve vehicle dynamics and not to contribute to autonomous driving. Therefore, in this study, we define the risk potential field as a uniform control law and propose a rear-wheel steering control system that actively steers the rear wheels to contribute to autonomous driving, depending on the level of the perceived risk in the driving situation. The effectiveness of the proposed method is verified by a double lane change test, which is performed assuming emergency avoidance in simulations, and subject experiments using a driving simulator. The results indicate that actively steering the rear wheels ensures a safer and smoother drive while simultaneously improving the emergency avoidance performance.

Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance

Distributed drive electric vehicles are regarded as a broadly promising transportation tool owing to their convenience and maneuverability. However, reasonable and efficient allocation of torque demand to four wheels is a challenging task. In this paper, a deep reinforcement learning-based torque distribution strategy is proposed to guarantee the active safety and energy conservation. The torque distribution task is explicitly formulated as a Markov decision process, in which the vehicle dynamic characteristics can be approximated. The actor-critic networks are utilized to approximate the action value and policy functions for a better control effect. To guarantee continuous torque output and further stabilize the learning process, a twin delayed deep deterministic policy gradient algorithm is deployed. The motor efficiency is incorporated into the cumulative reward to reduce the energy consumption. The results of double lane change demonstrate that the proposed strategy results in better handling stability performance. In addition, it can improve the vehicle transient response and eliminate the static deviation in the step steering maneuver test. For typical steering maneuvers, the proposed direct torque distribution strategy significantly improves the average motor efficiency and reduces the energy loss by 5.25%–10.51%. Finally, a hardware-in-loop experiment was implemented to validate the real-time executability of the proposed torque distribution strategy. This study provides a foundation for the practical application of intelligent safety control algorithms in future vehicles.

A data‐driven chassis coordination control strategy

Collaborative control strategy based on different driving conditions is a challenge for chassis systems with various electronic control units. This paper proposes a chassis cooperative control strategy based on vehicle inertial sensor data. Its novelty lies in the fact that it greatly simplifies the judgment logic while classifying various driving behaviours, and further reduces the possibility of bad control operation caused by misjudgement of driving state through heuristic decision logic. The clustering algorithm based on triaxial acceleration and angular velocity data was used to identify the driving behaviour of the vehicle, and the complex driving conditions were simplified into a single driving condition. The multi-axis weighted fusion method is used to extend the data and improve the generalization performance of the data. In order to improve the stability of steering control, S-type function is used to allocate the weights of AFS and DYC. The proposed control strategy is tested in the CarSim/Simulink co-simulation environment, and the simulation results of two key driving scenarios (double lane change and emergency braking) show that the proposed control strategy can effectively improve the vehicle handling and safety.

A Vehicle Handling Inverse Dynamics Method for Emergency Avoidance Path Tracking Based on Adaptive Inverse Control

In this paper, a new adaptive inverse control (AIC) method is presented to solve vehicle handling inverse dynamics problem and achieve path tracking control for emergency avoidance. The designed AIC system includes two crucial modules: the model identifier and the inverse model controller which are both the nonlinear adaptive filters constituted by neural networks. Firstly, they are both trained offline and connected with the vehicle to be the initial states of AIC system. Then, the back-propagation (BP) algorithm is used to adjust the weight parameters of the model identifier to identify the nonlinear characteristics of vehicle in real time. Simultaneously, the inverse model controller is used to be the controller of AIC system and the controller's weight parameters are tuned by the back-propagation through model (BPTM) algorithm online. A novel adaptive learning rate is introduced into the BP algorithm and the BPTM algorithm to guarantee the stability of neural networks. Finally, the inverse model controller can be regarded as the inverse of vehicle and used to solve required steering wheel angle according to the desired path to realize path tracking. The double lane change (DLC) road is used to be the desired path for emergency avoidance. The simulation results illustrate that the AIC system could realize accurate path tracking although there exist external disturbances and the AIC is an effective method to deal with the handling inverse dynamics problem for emergence avoidance.

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

Multi-axle vehicles are widely used in several applications such as transportation, industrial, and military field, because of its higher reliability in comparison with conventional two axles vehicles. Despite that, there is a paucity of research studies that consider lateral stability enhancement of these vehicles, especially on rough terrain. This simulation-based research study fills this gap and introduces a new adaptive Active Rear Steering (ARS) controller that improves the lateral stability of an 8x8 combat vehicle for rough-terrain operation. The developed controller is designed utilizing the Integral Sliding Mode Control theory (ISMC) based on Gain-Scheduled Linear Quadratic Regulator (GSLQR). Besides, the GSLQR control gains are optimized by a Genetic Algorithm (GA) toolbox using a new synthesized cost function to ensure asymptotic stability. Furthermore, a new Adaptive-ISMC (AISMC) is introduced by using genetic programming to generate control equations that can replace the developed high-dimension GSLQR gains and facilitate future hardware implementation. The developed controller is evaluated by performing a series of simulation-based Double Lane Change (DLC) maneuvers on several rough terrains. The evaluation is conducted for both high friction and slippery surfaces at high and moderate speed, consequently. The results show high fidelity and robustness of the developed controller in comparison with a previously designed optimal LQR controller.

Fault Detection Strategy of Vehicle Wheel Angle Signal via Long Short-Term Memory Network and Improved Sequential Probability Ratio Test

In order to improve the accuracy of fault detection results, this paper proposes a novel fault detection strategy of vehicle wheel angle signal via long short-term memory network (LSTM) and improved sequential probability ratio test (SPRT). Firstly, a signal estimation method based on data-driven modeling is presented, which fuses the vehicle current status information and adopts the LSTM based on deep learning to estimate the vehicle wheel angle signal. Then, the signal residual sequence is obtained by comparing the estimated wheel angle signal with the measured wheel angle signal. Based on this, the improved SPRT method based on mathematical statistics is used to analyze the signal residual sequence, so as to detect the fault signal timely and accurately. Finally, the accuracy of the estimation results is analyzed under sinusoidal condition, double-lane change condition and sinusoidal sweep frequency condition, and the effectiveness of the fault detection strategy proposed in this paper is further verified under the stuck fault condition and drift fault condition. The results indicate the effectiveness of the proposed fault detection strategy, which is of great significance to improve the safety and reliability of the vehicle.

Robust steering control for trajectory following in road traffic environments

The process of modelling vehicle motion in a road traffic environment requires the integration of trajectory generation with vehicle control. The steps involved here are generating a feasible trajectory based on the existing traffic and tracking the trajectory to control it with a steering angle input. Since the parameters of a physical system vary with changes in operating conditions, it is important to consider robustness when designing controllers. This article aims at developing a trajectory-following model with robust steering control strategies to accurately follow a generated trajectory. In this study, performance-based proportional, robust proportional and sliding mode control strategies are designed for trajectory following. The robustness of the proportional controller is established using Kharitonov’s theorem, which is compared with a proportional controller tuned for performance. Sliding mode control is designed for robustness and chattering elimination using two kinds of reaching laws – a constant reaching law and a novel power rate exponential reaching law. The controllers are designed using a dynamic bicycle model considering the error with respect to the trajectory. The controllers are then evaluated in IPG CarMaker®. The resulting trajectories and control inputs are compared for the considered control methodologies using the ISO double lane change and the Slalom tests. Sliding mode control with power rate exponential reaching law is concluded to be more robust as compared to the other controllers, with lower response times, up to 84% lower heading angle deviations from the trajectory and an overshoot of only 3.2% in lane changing.

Cornering stiffness estimation using Levenberg–Marquardt approach

Study of vehicle dynamics aggregates possibilities to enhance performance, safety and reliability, such as the integration of control systems, usually requiring knowledge on vehicle's states and parameters. However, some critical values are difficult to measure or are not disclosed. For this reason, dynamics and stability analysis of six-wheeled vehicles are compromised, and available information on this matter is limited. In this context, this paper proposes the estimation of the cornering stiffness of a 6x6 vehicle by an inverse problem approach applying the Levenberg–Marquardt (LM) method. The algorithm required data from field experiments and from simulations of a vehicle model during a double-lane change manoeuvre developed using . Experimental and theoretical values for the vehicle yaw rate were combined through LM method for cornering stiffness estimation. The excellent agreement between measured and simulated yaw rate indicates that the proposed model and the estimated parameters properly represent the vehicle dynamics response.

Cooperative-game-theoretic optimal robust path tracking control for autonomous vehicles

Modeling uncertainties are a major concern in vehicle path tracking control. As a practical engineering system, the uncertainties in vehicle lateral dynamics can be time-varying while bounded and have certain distributions wherein. The fuzzy set theory can effectively describe system uncertainties in terms of boundary and distribution. Contrary to fuzzy logic-based approaches, this article puts forward an explicit multiparameter optimal robust control law to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop path tracking dynamical system. Then, the tracking performance as well as the control cost is quantified as cost functions using fuzzy set theories. Finally, an optimization problem is established in the content of cooperative game to seek the optimal values for the tunable parameters. Simulations are conducted using CarSim and Simulink under double lane change and serpentine driving conditions. The results show that the proposed robust optimal control exhibits superior tracking performance.

Composite Control Arm Design: A Comprehensive Workflow

<div class="section abstract"><div class="htmlview paragraph">This paper presents a complete overview of the computational design of an advanced suspension control arm constructed of composite material for light weighting purposes. The proposed methodology presented in detail is split into 3 phases. Phase 1 or Vehicle Performance Simulation, in which basic modelling and a sensibility study is performed to better understand the advantages of unsprung mass reduction (compared to sprung mass reduction) with respect to the vehicle’s vertical dynamics. It followed by the development and utilization of a multibody approach to evaluate the full-vehicle response to different dynamic maneuvers, such as harsh road imperfections, sine sweep steering, and double lane change tests. The impact of the improved suspension control arm is highlighted in detail, and the loads to which it is subjected are computed to serve as inputs for the successive phases. Phase 2 or Design and Calculation Phase, where a closer look is given to the structural side of the component, understanding the specific behavior of composite materials and performing modelling of the control arm, followed by fine tuning with Finite Element Method optimization techniques. This phase consists of a topology optimization, followed by composite topography free size, size, and shuffle optimizations to arrive upon the ideal part-layup, and guarantee the desired mechanical characteristics of the component. Lastly, Phase 3 or the Production Preparation closes the design process by generating the production processes, steps, constraints, and tooling for the correct realization of the innovative control arm in a real-world application. The tools presented in this paper were created to allow the design to be completed rapidly, thus defining a blueprint for a full workflow, from engineering request to product delivery, which can be applied to different vehicles and customer requests, representing an essential step forward to the consolidation of the use of composite materials for structural suspension components.</div></div>

Robust trajectory tracking control for autonomous vehicle subject to velocity-varying and uncertain lateral disturbance

Autonomous vehicles are the most advanced intelligent vehicles and will play an important role in reducing traffic accidents, saving energy and reducing emission. Motion control for trajectory tracking is one of the core issues in the field of autonomous vehicle research. According to the characteristics of strong nonlinearity, uncertainty and chang-ing longitudinal velocity for autonomous vehicles at high speed steering condition, the robust trajectory tracking control is studied. Firstly, the vehicle system models are established and the novel target longitudinal velocity planning is carried out. This velocity planning method can not only ensure that the autonomous vehicle operates in a strong nonlinear coupling state in bend, but also easy to be constructed. Then, taking the lateral location deviation minimiz-ing to zero as the lateral control objective, a robust active disturbance rejection control path tracking controller is designed along with an extended state observer which can deal with the varying velocity and uncertain lateral dis-turbance effectively. Additionally, the feedforward-feedback control method is adopted to control the total tire torque, which is distributed according to the steering characteristics of the vehicle for additional yaw moment to enhance vehicle handing stability. Finally, the robustness of the proposed controller is evaluated under velocity-varying condi-tion and sudden lateral disturbance. The single-lane change maneuver and double-lane change maneuver under vary longitudinal velocity and different road adhesions are both simulated. The simulation results based on Matlab/Simulink show that the proposed controller can accurately observe the external disturbances and have good performance in trajectory tracking and handing stability. The maximum lateral error reduces by 0.18 meters compared with a vehicle that controlled by a feedback-feedforward path tracking controller in the single-lane change maneuver. The lateral deviation is still very small even in the double lane change case of abrupt curvature. It should be noted that our proposed control algorithm is simple and robust, thus provide great potential for engineering application.

Vehicle direct yaw moment control system based on the improved linear quadratic regulator

Purpose This study aims to propose an improved linear quadratic regulator (LQR) based on the adjusting weight coefficient, which is used to improve the performance of the vehicle direct yaw moment control (DYC) system. Design/methodology/approach After analyzing the responses of the side-slip angle and the yaw rate of the vehicle when driving under different road adhesion coefficients, the genetic algorithm and fuzzy logic theory were applied to design the parameter regulator for an improved LQR. This parameter regulator works according to the changes in the road adhesion coefficient between the tires and the road. Hardware-in-the-loop (HiL) tests with double-lane changes under low and high road surface adhesion coefficients were carried out. Findings The HiL test results demonstrate the proposed controllers’ effectiveness and reasonableness and satisfy the real-time requirement. The effectiveness of the proposed controller was also proven using the vehicle-handling stability objective evaluation method. Originality/value The objective evaluation results reveal better performance using the improved LQR DYC controller than a front wheel steering vehicle, especially in reducing driver fatigue, improving vehicle-handling stability and enhancing driving safety.