CarSim Vehicle Model Research Articles

Autonomous Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds

An autonomous vehicle (AV) uses high-level decision making and lower-level actuator controls, such as throttle (acceleration), braking (deceleration), and steering (change in lateral direction) to navigate through various types of road networks. Path planning and path following for highway driving are currently available in series-produced highly automated vehicles. In addition to these, emergency collision avoidance decision making and maneuvering are another key and essential feature that is needed in a series production AV at highway driving speeds. For reliability, low cost, and fast computation, such an emergency obstacle avoidance maneuvering system should use well-established conventional methods as opposed to data-driven neural networks or reinforcement learning methods, which are currently not suitable for use in highway AV driving. This paper presents a novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for AVs traveling at higher speeds and lower road surface friction, involving time-critical maneuver determination and control. The proposed EOAM framework offers usage of the AV’s sensing, perception, control, and actuation system abilities as one cohesive system to avoid an on-road obstacle, based first on performance feasibility and second on passenger comfort, and it is designed to be well integrated within an AV’s high-level control and decision-making system. To demonstrate the efficacy of the proposed method, co-simulation including the AV’s EOAM logic in Simulink and a vehicle model in CarSim is conducted with speeds ranging from 55 to 165 km/h and on road surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and interpreted in the context of an entire AV system, with implications for future work.

Fault-Tolerant Predictive Control With Deep-Reinforcement-Learning-Based Torque Distribution for Four In-Wheel Motor Drive Electric Vehicles

This article proposes a fault-tolerant control (FTC) method for four in-wheel motor drive electric vehicles considering both vehicle stability and motor power consumption. First, a seven-degree-of-freedom vehicle nonlinear model integrating motor faults is built to design a hierarchical FTC control scheme. The control structure is composed of two levels: an upper level nonlinear model-predictive controller and a lower level fault-tolerant coordinated controller. The upper level controller provides an appropriate reference in terms of additional yaw moment and vehicle longitudinal force, required for vehicle stability control, to the lower level controller. This latter aims at distributing the four-wheel torques taking into account both vehicle stability and power consumption. Specifically, the weighting factor involved in the optimization-based design of the lower level controller is determined online by the randomized ensembled double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning reinforcement learning algorithm to achieve an optimal control strategy for the whole vehicle operating range. Moreover, the tradeoff between vehicle stability and power consumption is analyzed, and the necessity of using reinforcement learning is discussed. Numerical experiments are performed under various driving scenarios with a high-fidelity CarSim vehicle model to demonstrate the effectiveness of the proposed control method. Via a comparative study, we highlight the advantages of the new FTC control method over many related existing control results in terms of improving the vehicle stability and driver comfort as well as reducing the power consumption.

A fast analytical local path planning method with applications in parking scenarios

In this paper an analytical and geometry-based path planning method is proposed that provides non-holonomic and collision free paths for parking maneuvers. The proposed method is straightforward in the sense that it does not require any sampling or exploring of the configuration space, or relies on any optimization process. Furthermore, the calculation is fast enough to be used in real-time applications for guiding the vehicle in parking scenarios. For any pair of start-goal configurations, the planner presents an infinite set of solutions. These paths have minimum number of driving direction changes as this can be more important for a driver than the path length. They may have any curvature lower than or equal to the maximum specified curvature. Simulations show the superiority of the method in local path planning over conventional methods such as hybrid A* and optimization based approaches in terms of computation time and path quality attributes such as the number of motion primitives and the number of direction changes. To compose a complete motion planning system, the proposed local planner is applied along with hybrid A* as global planner and a path tracking controller. The performance of the motion planning system is evaluated by simulating a valet parking scenario. MATLAB/Simulink is used to simulate the system, and a comprehensive vehicle model in CARSIM is used to represent the vehicle. Simulation results shows the capability of the proposed motion planner to implement automated valet parking tasks while respecting the physical limitations of the vehicle subsystems and driving comfort requirements.

A Shared Control Design for Steering Assistance System Considering Driver Behaviors

With the rapid development of advanced driver assistance systems, driver-automation cooperative control becomes more challenging due to the uncertainty of driver behaviors. To this end, this paper proposes a shared control approach to assist drivers in path-tracking and collision avoidance. A high-fidelity CarSim vehicle model embedded driver simulator is built to collect the driver data, based on which the normalized steering angle input and the time to collision are defined to quantify the driving performance. Then, these performance indices are employed to develop the fuzzy controller and determine the control authority for the steering assistance system (SAS). A multi-constraints model predictive control is designed to follow the desired path, and simultaneously ensure the vehicle stability. Different from the traditional constraints handling method, the state constraint is transformed into the input constraint considering the nonlinearity. The effectiveness of the proposed method is verified with two less experienced drivers. The test results show that the path-tracking performance and vehicle stability can be guaranteed with less control effort.

Study on the Stability Control of Vehicle Tire Blowout Based on Run-Flat Tire

In order to study the stability of a vehicle with inserts supporting run-flat tires after blowout, a run-flat tire model suitable for the conditions of a blowout tire was established. The unsteady nonlinear tire foundation model was constructed using Simulink, and the model was modified according to the discrete curve of tire mechanical properties under steady conditions. The improved tire blowout model was imported into the Carsim vehicle model to complete the construction of the co-simulation platform. CarSim was used to simulate the tire blowout of front and rear wheels under straight driving condition, and the control strategy of differential braking was adopted. The results show that the improved run-flat tire model can be applied to tire blowout simulation, and the performance of inserts supporting run-flat tires is superior to that of normal tires after tire blowout. This study has reference significance for run-flat tire performance optimization.

Research on four‐wheel independent steering intelligent control strategy based on minimum load

AbstractAround the new four‐wheel independent steering system, the adjustment principle of Akman angle is studied in this article. In order to improve steering stability and reduce tire wear, a new algorithm for optimal Ackerman angle allocation at full speed is proposed. Furthermore, combined with the minimum load rule, the four‐wheel rotation angle and driving force are optimized. In this process, Carsim vehicle model and MATLAB/Simulink force and angle allocation algorithm model are established. The final system simulation and performance evaluation demonstrate the effectiveness of the proposed force and angle distribution algorithm for vehicle stability and tire wear optimization.

Cooperative path-planning and tracking controller evaluation using vehicle models of varying complexities

This paper discusses cooperative path-planning and tracking controller for autonomous vehicles using a distributed model predictive control approach. Mixed-integer quadratic programming approach is used for optimal trajectory generation using a linear model predictive control for path-tracking. Cooperative behaviour is introduced by broadcasting the planned trajectories of two connected automated vehicles. The controller generates steering and torque inputs. The steering and drive motor actuator constraints are incorporated in the control law. Computational simulations are performed to evaluate the controller for vehicle models of varying complexities. A 12-degrees-of-freedom vehicle model is developed and is subsequently linearised to be used as the plant model for the linearised model predictive control-based tracking controller. The model behaviour is compared against the kinematic, bicycle and the sophisticated high-fidelity multi-body dynamics CarSim model of the vehicle. Vehicle trajectories used for tracking are longitudinal and lateral positions, velocities and yaw rate. A cooperative obstacle avoidance manoeuvre is performed at different speeds using a co-simulation between the controller model in Simulink and the high-fidelity vehicle model in CarSim. The simulation results demonstrate the effectiveness of the proposed method.

MPC-based path tracking with PID speed control for autonomous vehicles

In this paper, a new coupled lateral and longitudinal controller based on model predictive control (MPC) framework was proposed for an autonomous vehicle to track the desired trajectory and speed. Considering the constraints of control input limit and state output admissible, we used a spatial-based 8 degrees of freedom (DOF) vehicle model as the prediction model and used a high-fidelity model, i.e., a 14-DOF vehicle model as the plant model in the formulation of MPC algorithm. For the lateral control, the MPC controller generates the optimal road-wheel steering angle; for the longitudinal control, the PID controller embedded in the optimization solution generates the total driving or braking wheel torque. All these control inputs were passed to the plant simultaneously. The developed vehicle models were simulated with step steering input and compared with the simulation result of CarSim vehicle model for validation. We implemented the proposed controller for path tracking and speed control with MATLAB considering an 8-shaped curved trajectory as the reference. The simulation results showed that the path tracking and speed tracking performance were good using the combined lateral and longitudinal control strategy.

Design of an active front steering system for a vehicle using an active disturbance rejection control method.

A linear vehicle model is commonly employed in the controller design for an active front steering (AFS). However, this simplified model has a considerable influence on the accuracy of the controller. In this article, an AFS controller using an active disturbance rejection control (ADRC) technique is proposed to prevent this problem. The AFS controller was established in MATLAB/Simulink to control the CarSim vehicle model for verification of the simulation. Under the straight-line driving disturbance condition, proportion-integration-differentiation (PID) control and ARDC substantially decreased with respect to the uncontrolled lateral offset and ADRC performed better than PID control. Under the double lane change (DLC) test working condition, the tracking error of the path, yaw rate, roll angle, and lateral acceleration, and error of the driving direction were used to evaluate the vehicle's controllability and stability. These evaluation indexes were substantially improved by PID control and ADRC; similarly, ADRC was better than PID control. The tracking error of the ADRC in the presence of parameter variance and external disturbance was significantly smaller than that of PID control. The results have verified that the AFS controller based on ADRC can significantly improve vehicle controllability and stability.

Vehicle dynamics control of an electric-all-wheel-drive hybrid electric vehicle using tyre force optimisation and allocation

ABSTRACTHybrid Electric Vehicles (HEV) offer improved fuel efficiency compared to conventional vehicles at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with electric All-Wheel-Drive capabilities via the use of torque vectoring electric rear axle drive (TVeRAD) to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To improve the handling characteristics of the vehicle, the TVeRAD provides torque vectoring at the rear axle. A bond graph model of the drivetrain is developed and used in co-simulation with CarSim. The paper proposes a control system which utilises control allocation to optimise tyre forces. The proposed control system is tested in the simulation environment with a high fidelity CarSim vehicle model. Simulation results show the control system is able to maximise vehicle longitudinal performance while avoiding tyre saturation on low mu surfaces. More importantly, the control system is able to track the desired yaw moment request on a high speed double lane change manoeuvre through the use of the TVeRAD to improve the handling characteristic of the vehicle.

Emergency Steering Evasion Assistance Control Based on Driving Behavior Analysis

Research on collision avoidance has received significant attention in academia, but few attempts have been made regarding the impact of the real driver. Focusing on the hybrid vehicle controlled by a real driver, this paper proposes a combined yaw moment and steering torque control method based on driving behaviors during the emergency steering evasion (ESE). First, a CarSim vehicle model and a vision-based driving simulator are built to acquire and analyze ESE driving behaviors, and the trigger condition of the assistance is defined. Second, a preview distance adaptive driver steering model is created and followed by the calculation of the desired value of the steering angle and yaw rate from the planned collision avoidance path. Third, an ESE assistance controller is designed on the basis of the electric power steering (EPS) torque assistance law during normal driving and driving behaviors for the steering evasion lane change. Controllers used for this paper include a yaw rate tracking controller and a steering torque assistance fuzzy controller. The actuators are EPS, an electric stability program, and two hub motors in the rear axle. The preview distance is optimized by the particle swarm optimization method and the driving behavior-based ESE controller is tested with the driving simulator. The result shows that the optimized preview distance is credible, and the path tracking accuracy and the vehicle stability are improved with the help of the proposed ESE assistance controller by giving full consideration to driving behaviors in ESE.

Newly Developed Nonlinear Vehicle Model for an Active Anti-roll Bar System

This paper presents the development of a newly developed nonlinear vehicle model is used in the validation process of the vehicle model. The parameters chosen in a newly developed vehicle model is developed based on CARSIM vehicle model by using non-dominated sorting genetic algorithm version II (NSGA-II) optimization method. The ride comfort and handling performances have been one of the main objective to fulfil the expectation of customers in the vehicle development. Full nonlinear vehicle model which consists of ride, handling and Magic tyre subsystems has been derived and developed in MATLAB/Simulink. Then, optimum values of the full nonlinear vehicle parameters are investigated by using NSGA-II. The two objective functions are established based on RMS error between simulation and benchmark system. A stiffer suspension provides good stability and handling during manoeuvres while softer suspension gives better ride quality. The final results indicated that the newly developed nonlinear vehicle model is behaving accurately with input ride and manoeuvre. The outputs trend are successfully replicated.

Autonomous driving motion planning with obstacles prioritization using lexicographic optimization

There are driving situations that avoiding all obstacles is infeasible. In such situations, an autonomous vehicle should avoid vulnerable obstacles like pedestrians. In this paper, a motion planning method is presented that avoids obstacles according to their priority orders. The method utilizes a model predictive controller with obstacle constraints and applies lexicographic optimization to the controller to prioritize the constraints, and subsequently, prioritize the obstacles. The proposed method is simulated on a high fidelity CarSim vehicle model. The results show that when avoiding all obstacles is not feasible, the proposed method avoids the obstacles with the highest priority orders.

Vehicle Dynamics Control of eAWD Hybrid Electric Vehicle Using Slip Ratio Optimization and Allocation

Hybrid electric vehicles (HEV) offer improved fuel efficiency compared to their conventional counterparts at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with electric all-wheel drive (eAWD) capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To further improve the handling characteristics of the vehicle, the TVeRAD unit allows for wheel torque vectoring (TV) at the rear axle. A bond graph model of the proposed drivetrain model is developed and used in cosimulation with carsim. The paper proposes a control system, which utilizes slip ratio optimization to allocate control to each tire. The optimization algorithm is used to obtain optimal slip ratio targets to at each tire such that the targets avoid tire saturation. The Youla parameterization technique is used to develop robust tracking controllers for each axle. The proposed control system is ultimately tested on the drivetrain model with a high fidelity carsim vehicle model for validation. Simulation results show that the control system is able to maximize vehicle longitudinal performance while avoiding tire saturation on a low μ surface. More importantly, the control system is able to track the desired yaw moment request on a high-speed double-lane change (DLC) maneuver through the use of the TVeRAD to improve the handling characteristic of the vehicle.

Design of Hardware-In-Loop Simulation Platform for Air Suspension Systems

With the development of control algorithms and structural features in the control system, the hardware-in-loop simulation technology has been widely used after unremitting research and exploration on the basis of off-line simulation. In this paper, the dSPACE real-time simulation system is used to design and develop a hardware loop simulation experiment platform for an air suspension. CarSim vehicle model and suspension control algorithms can be run in the real time. Based on the dSPACE realtime simulation platform mentioned above, a real-time monitoring interface and control program are developed to complete the test of the sliding mode control method of the 1/4 air suspension.

A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities.

A hierarchical estimator development for estimation of tire-road friction coefficient.

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified “magic formula” tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.

Obstacle Avoidance in Real Time With Nonlinear Model Predictive Control of Autonomous Vehicles

A Nonlinear model predictive control (NMPC) for trajectory tracking with the obstacle avoidance of autonomous road vehicles traveling at realistic speeds is presented in this paper, with a focus on the performance of those controllers with respect to the look-ahead horizon of the NMPC. Two different methods of obstacle avoidance are compared and then the NMPC is tested in several simulated but realistic tracking scenarios involving static obstacles on constrained roadways. In order to simplify the vehicle dynamics, a bicycle model is used for the prediction of future vehicle states in the NMPC framework. However, a high-fidelity, nonlinear CarSim vehicle model is used to evaluate the vehicle performance and test the controllers in the simulation results. The CPU time is also analyzed to evaluate these schemes for real-time applications. The results show that the NMPC controller provides satisfactory online tracking performance in a realistic scenario at normal road speeds while still satisfying the real-time constraints. In addition, it is shown that the longer prediction horizons allow for better responses of the controllers, which reduce the deviations while avoiding the obstacles, as compared with shorter horizons.

Electronic Stability Control Based on Motor Driving and Braking Torque Distribution for a Four In-Wheel Motor Drive Electric Vehicle

An electronic stability control (ESC) algorithm is proposed for a four in-wheel motor independent-drive electric vehicle (4MIDEV) utilizing motor driving and regenerative braking torque distribution control to improve vehicle stability. A stability judgment controller, an upper level controller, and a torque distribution algorithm are designed for the ESC system. The stability judgment controller is designed to generate the desired yaw rate and sideslip angle for vehicle stability, and the control mode, which is normal driving mode or ESC mode, is set according to the driver inputs and measurement signal inputs. The upper level controller consists of a speed tracking controller, a yaw moment controller, and four wheel-slip controllers to calculate the desired value of traction force, the desired value of yaw moment, and the four respective net torque inputs of the four in-wheel motors. The torque distribution algorithm is designed to generate each motor driving torque or regenerative braking torque input for each wheel. An average torque distribution strategy, a tire-dynamic-load-based torque distribution strategy, and a minimum-objective-function-based optimal torque distribution strategy are used separately in the torque distribution algorithm to control the motor driving torque or regenerative braking torque for vehicle stability enhancement. The proposed ESC algorithm was implemented and evaluated in a CarSim vehicle model and a MATLAB/Simulink control model. The three proposed torque distribution strategies can be used to regulate the vehicle to perform the following tasks: “single lane change,” “double lane change,” and “snake lane change.” The simulation studies show that the yaw rate error root mean square [RMS $(\gamma-\gamma_\mathrm{-des})$ ] decreased, on average, by 75 percent using the proposed optimal torque distribution algorithm compared with that without using stability control.

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.