Single Lane Change Maneuver Research Articles

Safe autonomous lane changes and impact on traffic flow in a connected vehicle environment

Despite the recent advancements in autonomous vehicle technologies, performing safe lane changes and merging in dense traffic environments remains an open challenge. One important question is how to find a space to merge into without placing any vehicle in a collision-prone situation. Moreover, what is the traffic flow impact of doing so? To tackle this issue, we start by adopting a safety definition based on a worst-case braking scenario. We then propose a decentralized controller-agnostic cooperative approach for lane changes that relies only on Vehicle-to-Vehicle communications and guarantees safety. In our method, the merging vehicle operates as having two possible leaders, one in its own lane and one in the destination lane, till the lane change maneuver is completed. Furthermore, the future following vehicle in the destination lane cooperates by acting as if the merging vehicle has already changed lanes. The merging vehicle only performs the lane change maneuver when the gaps are safe. Vehicle-level simulations are used to evaluate the approach in detail for a single lane change maneuver. Then, the method’s impact on highway safety and traffic flow is assessed using microscopic traffic simulations based on the commercial software VISSIM. We simulate three types of vehicles: adaptive cruise control equipped vehicles, autonomous vehicles, and connected and autonomous vehicles. The various simulations allow us to identify which improvements are brought by cooperative lane changes. Results indicate that connected and autonomous vehicles can achieve almost zero collision risk in congested and uncongested scenarios while improving traffic flow by 26% in the congested case. However, energy consumption increases significantly in the uncongested scenario. Moreover, our method outperforms an existing cooperative lane-changing method regarding safety and traffic flow. Our simulations with mixed traffic suggest that the proposed method is advantageous only at high penetrations of autonomous vehicles or in congested situations.

Reinforcement Learning Enabled Safety-Critical Tracking of Automated Vehicles with Uncertainties via Integrated Control-Dependent, Time-Varying Barrier Function, and Control Lyapunov Function

Model uncertainties are considered in a learning-based control framework that combines control dependent barrier function (CDBF), time-varying control barrier function (TCBF), and control Lyapunov function (CLF). Tracking control is achieved by CLF, while safety-critical constraints during tracking are guaranteed by CDBF and TCBF. A reinforcement learning (RL) method is applied to jointly learn model uncertainties that related to CDBF, TCBF, and CLF. The learning-based framework eventually formulates a quadratic programming (QP) with different constraints of CDBF, TCBF and CLF involving model uncertainties. It is the first time to apply the proposed learning-based framework for safety-guaranteed tracking control of automated vehicles with uncertainties. The control performances are validated for two different single-lane change maneuvers via Simulink/CarSim® co-simulation and compared for the cases with and without learning. Moreover, the learning effects are discussed through explainable constraints in the QP formulation.

High-speed lateral stability and trajectory tracking performance for a tractor-semitrailer with active trailer steering.

An active trailer steering (ATS) controller is investigated to improve the lateral stability and trajectory tracking performance of the tractor-semitrailer. First of all, a linear yaw-roll dynamic model of the tractor-semitrailer with steerable trailer wheels is established, and the model accuracy is verified. Then a linear quadratic regulator (LQR) for actively steering the trailer's wheels is designed. For the LQR controller, the lateral acceleration and the sideslip angle at the center of gravity (CG) of the trailer are taken as the optimization objectives, and the steering angle of the wheel on the middle axle of the trailer is set as the control input. Finally, the effectiveness of the designed controller is tested based on the co-simulation platform under the single lane-change (SLC) maneuver at the speed of 100 km/h and the double lane-change (DLC) maneuver at the speed of 80 km/h and 88km/h. Research results show that under the high-speed SLC maneuver, the designed LQR controller can significantly improves the lateral stability and trajectory tracking performance of the trailer, and cannot affect apparently the trajectory and dynamic responses of the tractor. Under the high-speed DLC maneuver, the designed controller can still make the tractor-semitrailer reach a new steady state in a short time, and improve the vehicle lateral stability and the trajectory tracking performance of the trailer at the same time.

Optimization of Gain Scheduled Controller for an Active Trailer Steering System Using an Evolutionary Algorithm

Car–trailer combinations can experience unstable motion modes such as trailer-sway, jackknifing and rollover that can lead to fatal accidents. These unstable motions can be mitigated with the use of an active trailer steering (ATS) system. Prior studies in ATS have leveraged the linear quadratic regulator (LQR) as an ATS controller but for many of these designs it was assumed that the vehicle and operating parameters were constant. In reality, vehicle and operating parameters may vary and have an impact on the stability of a car–trailer combination. In this paper, the weighting matrices of the LQR controller are determined using the GDE3 evolutionary optimization algorithm with the objective of addressing the design trade off between minimizing the car–trailer’s path-following performance for low vehicle speeds and minimizing the rearward amplification for high vehicle speeds. The effectiveness of the approach is demonstrated using a numerical simulation car–trailer model developed in the CarSim simulator. Our results show that the multi-objective tuned gain scheduling controller outperforms a non-tuned gain scheduling controller in terms of improving the lateral stability and the path following performance of car–trailer combinations in driver in the loop single lane-change maneuvers at a constant vehicle forward speed.

Lateral Stability Control of a Tractor-Semitrailer at High Speed

To improve the high-speed lateral stability of the tractor-semitrailer, a lateral stability control strategy based on the additional yaw moment caused by differential braking is proposed and investigated based on the co-simulation environment. First of all, a five-degree-of-freedom (5-DOF) yaw-roll dynamic model of the tractor-semitrailer is established, and the model accuracy is verified. Secondly, the lateral stability control strategy of the tractor-semitrailer is proposed, two yaw moment controllers and the braking torque distributor are designed. Then, the effectiveness of the proposed control strategy and the influence of the yaw moment controller on the lateral stability of the tractor-semitrailer are investigated under the high-speed lane-change maneuvers. Finally, the controller robustness is discussed. Research results show that the proposed high-speed lateral stability control strategy can ensure the tractor-semitrailer to perform safely the single lane-change (SLC) maneuver at 110 km/h and the double lane-change (DLC) maneuver at 88 km/h; the yaw moment controller has significant influence on the lateral dynamic performance of the tractor-semitrailer; compared with the proportional-derivative (PD) control, the model predictive control (MPC) can make the tractor-semitrailer obtain better lateral stability under high-speed lane-change maneuvers; MPC and PD controllers exhibit good robustness to the considered vehicle parameter uncertainties.

Evaluating Coordinated Cooperative Control of Three Active Car Systems Using Fuzzy-Logic

Vehicle systems automation is an inevitable process as a result of the growing safety requirements and the trend of increasing car operation autonomy by reducing driver’s influence. The paper covers the coordinated control operation of three active systems to assess the improvement of vehicle response. Direct yaw control through selective wheel braking (DYC), active front steering control (AFS) and active suspension normal force control (NFC) are included. The control method is fuzzy logic. The modelling is done in Matlab/Simulink, where 14-DOF nonlinear full vehicle model and 3-DOF reference model were introduced. Simulations were done for three manoeuvres: cornering event, single lane change and fishhook manoeuvre, at a speed of 130 km/h and dry road surface with adhesion coefficient of 0.9 was assumed. Coordination was done with adjusting scaling factors for each control. Results show improvements in the transient response behaviour of the vehicle compared with four vehicles: vehicle without any active system, vehicle with coordinated DYC and AFS, vehicle with coordinated DYC and NFC and vehicle with coordinated AFS and NFC. The biggest impact can be seen in results for sideslip angle and roll angle, coordinated cooperative control here gives lowest values in the transient period during the manoeuvre. Although it gives better results for the other variables as well. The goal of the coordinated cooperative control is fulfilled by improving the stability and handling of the vehicle, which helps the driver to maintain the feeling of manoeuvrability and the sense for the direction of movement of the vehicle. The system for direct yaw control through selective wheel braking has major impact but the coordination with the other two systems lowers the braking force per wheel. The system for active front steering control has lower impact but at lower speeds helps for maintaining better direction of vehicle movement. Considering these three active systems act simultaneously during high speeds and critical manoeuvres next step would be to define sequence of activation depending on the currently measured vehicle behaviour variables.

Active steering control based on preview theory for articulated heavy vehicles.

This paper investigates the active steering control of the tractor and the trailer for the articulated heavy vehicle (AHV) to improve its high-speed lateral stability and low-speed path following. The four-degree-of-freedom (4-DOF) single track dynamic model of the AHV with a front-wheel steered trailer is established. Considering that the road information at the driver’s focus is the most clear and those away from the focus blurred, a new kind controller based on the fractional calculus, i.e., a focus preview controller is designed to provide the steering input for the tractor to make it travel along the desired path. In addition, the active steering controllers based on the linear quadratic regulator (LQR) and single-point preview controller respectively are also proposed for the trailer. However, the latter is designed on the basis of the articulation angle between the tractor and trailer, inspired by the idea of the driver’s single-point preview controller. Finally, the single lane change maneuver and 90o turn maneuver are carried out. And the simulation results show that compared with the single-point preview controller, the new kind preview controller for the tractor can have good high speed maneuvering stability and low speed path tracking ability by adjusting the fractional order of the controller. On this basis, three different AHVs with the same tractor are simulated and the simulation results show that the AHV whose trailer adopts the single-point preview controller has better high-speed lateral stability and low-speed path tracking than the AHV whose trailer adopts the LQR controller.

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.

Fault-Tolerant Control for Intelligent Electrified Vehicles Against Front Wheel Steering Angle Sensor Faults During Trajectory Tracking

Steer-by-Wire (SBW) systems are an integral and essential component of intelligent electrified vehicles and are critical to ensure safe vehicle operations. In this paper, a fault-tolerant control method for SBW is proposed to mitigate the adverse influence of front wheel steering angle sensor faults based on the Kalman filtering technique. First, a linearized vehicle model is derived based on tire characteristics and is further combined with a steering actuator model to provide the modelling foundation for estimating the front wheel steering angle. Considering multi-source noises and their significant influence on the front wheel steering angle sensor, the Butterworth filter is employed to filter out the high-frequency noises. The front wheel steering angle estimation is realized by incorporating the measurements of the yaw rate and lateral acceleration of vehicle and the current of steering motor. In particular, a dynamic threshold mechanism is adopted to activate the fault diagnosis and fault-tolerant compensation based on the residual theory. Finally, a linear time-varying model predictive controller is presented to execute the trajectory tracking task for an intelligent electrified vehicle. The effectiveness of the proposed scheme is verified under the single lane change and double lane change maneuvers during the trajectory tracking.

Collision Imminent Steering at High Speeds on Curved Roads Using One-Level Nonlinear Model Predictive Control

Collision imminent steering is an automotive active safety feature designed to perform an evasive lane change maneuver when a forward collision cannot be avoided by braking alone. Previous work developed a nonlinear one-level model predictive controller to perform such a maneuver in a straight highway environment and at high speeds. In this article, a new formulation is presented that extends this capability to curved roads. The formulation includes a drivable tube concept, within which the maneuver is allowed to push the vehicle to its limits of handling, but only if the controller deems it necessary. Numerical simulation results showcase aggressive lane change maneuvers on curved roads at high speeds, allowing for both inside and outside lane changes, as well as single and double lane change maneuvers in a shorter distance than braking.

Real-time estimation of tire–road friction coefficient based on lateral vehicle dynamics

This study proposes a two-stage framework for real-time estimation of tire–road friction coefficient of a vehicle on the basis of lateral dynamics of the vehicle. The estimation framework employs a new cascade structure consisting of an extended Kalman filter and two unscented Kalman filters to reduce the computational burden. In the first stage, extended Kalman filter is utilized to estimate lateral velocity of the vehicle and thereby both the front and rear tires’ side-slip angles. In the second stage, a two–unscented Kalman filters sub-framework is formulated in sequence to observe both the front- and rear-axle tire forces, and to subsequently identify their respective tire–road friction coefficient, regarded as two unknown states. All the measured signals required in the study could be realized from the conventional on-board sensors. Typical double-lane change and single-lane change maneuvers were designed and the developed algorithm was verified through CarSim–MATLAB/Simulink software platform considering high-, mid-, and low-friction road conditions. The simulation results show that the proposed method can yield accurate and rapid estimations of the tire–road friction coefficient for mid- and low-friction road conditions even under a single-lane change maneuver, although double-lane change maneuver is needed to accurately estimate the tire–road friction coefficient for high-friction road condition.

Quantifying drivers’ driving skills using closed-loop dynamic simulations of articulated heavy vehicles

This paper presents a method for quantifying drivers’ driving skills using closed-loop dynamic simulations of articulated heavy vehicles (AHVs). Due to AHVs’ multi-unit configurations, large sizes, and high centers of gravity (CGs), these large vehicles exhibit poor directional performance. AHVs require wide roads and large radii of path curvature for evasive maneuvers; these large vehicles frequently display unstable motion modes, including trailer sway, jackknifing, and rollover. The directional performance of AHVs is frequently evaluated in terms of maneuverability and lateral stability. There exists a trade-off between the performance measures of maneuverability and lateral stability. An AHV, its driver and the road constitute a unique closed-loop dynamic system. The unique dynamic characteristics of AHVs impose significant challenges for safe operations of these vehicles. However, little attention has been paid to the interactions of driver-AHV. This paper tackles the problem of quantifying drivers’ driving skills considering the interactions of driver-AHV under simulated single lane-change (SLC) maneuvers. Based on two driver characteristic parameters, namely preview time (PT) and transport delay (TD), we propose two performance measures, i.e., path-following score (PFS) and combined stability score (CSS), as the indicators for assessing the driving skills of AHV drivers under the simulated maneuvers. The driver-AHV closed-loop dynamic simulation is implemented using the built-in driver model and virtual vehicles developed in TruckSim. The numerical simulation results for rearward amplification (RA) measures are validated using driver-in-the-loop (DIL) real-time simulation. The simulation identifies AHV drivers’ driving skills in terms of lateral stability, path-following, as well as balanced path-following and stability regions considering PT and TD properties.

Direct Yaw Moment Control for Enhancing Handling Quality of Lightweight Electric Vehicles with Large Load-To-Curb Weight Ratio

In this paper a vehicle dynamics control system is designed to compensate the change in vehicle handling dynamics of lightweight vehicles due to variation in loading conditions and the effectiveness of the proposed design is verified by simulations and an experimental study using a fixed-base driving simulator. Considering the electrification of future mobility, the target vehicle of this research is a lightweight vehicle equipped with in-wheel motors that can generate an additional direct yaw moment by transverse distribution of traction forces to control vehicle yawing as well as side slip motions. Previously, the change in vehicle handling dynamics for various loading conditions have been analyzed by using a linear two-wheel vehicle model in planar motion and a control law of the DYC system based on feed-forward of front steering angular velocity and feedback of vehicle yaw rate. The feed-forward controller is derived based on the model following control with approximation of the vehicle dynamics to 1st-order transfer function. To make the determination of the yaw rate feedback gain model-based and adaptable to various vehicle velocity conditions, this paper selects a method where the yaw rate feedback gain in the DYC system is determined in a way that the steady-state yaw rate gain of the controlled loaded vehicle matches the gain of the unloaded vehicle. The DYC system is simulated in a single lane change maneuver to confirm the improved responsiveness of the vehicle while simulations of a double-lane change maneuver with a driver steering model confirms the effectiveness of the DYC system to support tracking control. Finally, the effectiveness of the proposed DYC system is also verified in an experimental study with ten human drivers using a fix-based driving simulator.

Robust gain-scheduled output feedback yaw stability control for in-wheel-motor-driven electric vehicles with external yaw-moment

This paper presents a robust gain-scheduled output feedback yaw stability H∞ controller design to improve vehicle yaw stability and handling performance for in-wheel-motor-driven electric vehicles. The main control objective is to track the desired yaw references by managing the external yaw moment. Since vehicle lateral states are difficult to obtain, the state feedback controller normally requires vehicle full-state feedback is a critical challenge for vehicle lateral dynamics control. To deal with the challenge, the robust gain-scheduled output feedback controller design only uses measurements from standard sensors in modern cars as feedback signals. Meanwhile, parameter uncertainties in vehicle lateral dynamics such as tire cornering stiffness and vehicle inertial parameters are considered and handled via the norm-bounded uncertainty, and linear parameter-varying polytope vehicle model with finite vertices is established through reducing conservative. The resulting robust gain-scheduled output feedback yaw stability controller is finally designed, and solved in term of a set of linear matrix inequalities. Simulations for single lane and double lane change maneuvers are implemented to verify the effectiveness of developed approach with a high-fidelity, CarSim®, full-vehicle model. It is confirmed from the results that the proposed controller can effectively preserve vehicle yaw stability and lateral handling performance.

Design of an optimal active stabilizer mechanism for enhancing vehicle rolling resistance

Improving rollover and stability of the vehicles is the indispensable part of automotive research to prevent vehicle rollover and crashes. The main objective of this work is to develop active control mechanism based on fuzzy logic controller (FLC) and linear quadratic regulator (LQR) for improving vehicle path following, roll and handling performances simultaneously. 3-DOF vehicle model including yaw rate, lateral velocity (lateral dynamic) and roll angle (roll dynamic) were developed. The controller produces optimal moment to increase stability and roll margin of vehicle by receiving the steering angle as an input and vehicle variables as a feedback signal. The effectiveness of proposed controller and vehicle model were evaluated during fishhook and single lane-change maneuvers. Simulation results demonstrate that in both cases (FLC and LQR controllers) by reducing roll angle, lateral acceleration and side slip angles remain under 0.6g and 4° during maneuver, which ensures vehicle stability and handling properties. Finally, the sensitivity and robustness analysis of developed controller for varying longitudinal speeds were investigated.

A Tool for Tire Handling Performance Evaluation

ABSTRACT In the past, handling performance of the tire–vehicle combination has been evaluated using tire models such as the Pacejka Magic Formula. These models usually lack realistic representation of tire–road interaction and are not suitable for combined steering and braking maneuvers that may activate the antilock braking system. The objective of this study is to develop a computationally simple and accurate tire model, which can be used in the development and evaluation of handling performance of the tire on uneven road surfaces. For an emergency obstacle avoidance maneuver at high speeds, transient tire behavior plays a crucial role in the generation of forces between tire and road. Road undulations and steering inputs both induce high-frequency tire belt vibrations, which have detrimental effects on the handling and tractive behavior of the tire. To meet these requirements, a dynamic six degrees of freedom tire model–based rigid ring approach is developed and integrated with a multiple tandem elliptical cam to include enveloping behavior of the tire. The tire model that is developed in this research is partially based on the work of Schmeitz found in the literature. The tire model was parameterized using experimental parameters found in the literature. The tire model is validated using fixed axle high-speed oblique cleat experimental data. The developed tire model is integrated with the vehicle model in CarSim®. From the simulation of successive step steering input, the increasing influence of tire belt vibrations at higher slip angles was observed due to sudden steering wheel inputs. From the simulation of the step steering input on the bad asphalt road surface with an added cleat and on the flat smooth road surface, it was observed that the lateral performance of the tire at higher slip angles is strongly influenced by the vertical load variations. A single lane change maneuver was simulated on the smooth and bad asphalt road surfaces, demonstrating the strong influence of tire lateral and vertical belt vibrations on the lateral performance of the vehicle. Simulation of high-speed emergency obstacle avoidance braking maneuvers on measured rough and smooth roads showed that the influence of high-frequency vibrations due to road undulations and step steering inputs causes large variations of longitudinal and lateral forces at the axle, thus creating large variations in slip and slip angle of the tire with a degraded braking distance on rough roads.

Tractor-Semitrailer Vehicle Stability Control Based on TruckSim-Simulink Co-Simulation

AbstractA nonlinear tractor-semitrailer vehicle simulation model is established based on TruckSim used to validate the proposed control scheme. The yaw dynamics of the entire vehicle is stabilized by applying differential braking to the wheels of the tractor and the trailer. The control system, which was designed in Matlab/Simulink, is a two-level structure with the upper yaw moment controller outputting an additional yaw moment and the lower control allocator realizing the additional yaw moment by appropriately coordinating the wheel brake actions of the tractor and the trailer. A Trucksim-Simulink co-simulation model is constructed, and based on which the proposed control scheme is validated by a single-lane change maneuver on a slippery road. Simulation results reveal that designing and evaluating vehicle stability control strategies with the TruckSim-Simulink co-simulation method is effective. The proposed control scheme can significantly improve the vehicle yaw stability and roll stability.

Active roll control for rollover prevention of heavy articulated vehicles with multiple-rollover-index minimisation

This paper presents the application of a nominal control design algorithm for rollover prevention of heavy articulated vehicles with active anti-roll-bar control. This proposed methodology is based on an extension of linear quadratic regulator control for ‘state derivative-induced (control coupled) output regulation’ problems. For heavy articulated vehicles with multiple axles, a performance index with multiple rollover indices is proposed. The proposed methodology allows us to compare the usefulness of various control configurations (i.e. actuators at different axles of the vehicle) based on the interaction of this control configuration with vehicle dynamics. Application of this methodology to a specific heavy articulated vehicle with a tractor semi-trailer shows that a single active anti-roll-bar system at the trailer unit gives better performance than multiple-axle actuators at tractor and trailer together with the single lane change manoeuvre as the external disturbance. Thus, the proposed methodology of this paper not only highlights the importance of the interactions between control and vehicle dynamics in rollover prevention problems but, in fact, proposes a novel technique to exploit the benefits of these interactions judiciously.

Virtual Prototype Co-Simulation of 4WS Vehicle

A new 4WS vehicle model is set up by means of revamping a foreign FWS model. Co-simulation is completed by loading 4WS mechanical model as a subsystem which is built to express the characteristics of physical model of vehicle in ADAMS/Car into the control system modeled in MATLAB. Moreover, the response curves of evaluating handling stability are obtained according to co-simulation results of step steering maneuvers and single lane change maneuvers. It provides theoretical basis for developing 4WS physical vehicle rapidly.

Optimal control to modelling motorcycle rider steering: local versus global coordinate systems in rider preview

The response of a motorcycle is heavily dependent on the rider’s control actions, and consequently a means of replicating the rider’s behaviour provides an important extension to motorcycle dynamics. The primary objective here is to develop effective path-following simulations and to understand how riders control motorcycles. Optimal control theory is applied to the tracking of roadway by a motorcycle, using a non-linear motorcycle model operating in free control by steering torque input. A path-following controller with road preview is designed by minimising tracking errors and control effort. Tight controls with high weightings on performance and loose controls with high weightings on control power are defined. Special attention is paid to the modelling of multipoint preview in local and global coordinate systems. The controller model is simulated over a standard single lane-change manoeuvre. It is argued that the local coordinates point of view is more representative of the way that a human rider operates and interprets information. The simulations suggest that for accurate path following, using optimal control, the problem must be solved by the local coordinates approach in order to achieve accurate results with short preview horizons. Furthermore, some weaknesses of the optimal control approach are highlighted here.