The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via auto brake, and it is one of the key technologies of autonomous vehicles. Moreover, the vehicle lateral stability is very crucial in emergency scenarios. Due to complex traffic conditions and various road frictions, emergency brake may cause a vehicle to lose its lateral stability. Therefore, this paper proposes a lateral-stability-coordinated collision avoidance control system (LSCACS) based on the model predictive control (MPC). First, the proposed LSCACS decides which control mode to be implemented based on vehicle dynamics states, including a normal driving mode, a full auto brake mode, and a brake and stability mode. The MPC is used in the upper controller to calculate the desired deceleration and additional yaw moment. The lower controller calculates the desired tire forces of four wheels and realizes them by certain wheel cylinder hydraulic pressures. The LSCACS is validated by hardware-in-the-loop (HIL) tests, and the results show LSCACS’s effectiveness and great performance of the collision avoidance and lateral stability.

Longitudinal Collision Avoidance and Lateral Stability Adaptive Control System Based on MPC of Autonomous Vehicles

The automated steering control technology is crucial for an autonomous vehicle, but due to parametric uncertainties and time varying, the performance of automated steering control can be degraded. Therefore, a vehicle automated steering controller based on a model predictive control (MPC) approach is proposed in this article. First, considering tire nonlinear characteristics, the state and control matrices are modified, then the time-varying vehicle speed is considered and a linear parameter varying lateral model is established through utilizing a polytope with finite vertices to describe vehicle longitudinal velocity. Then, the MPC-based vehicle path tracking controller, which is robust against parameter uncertainties, is designed; the proposed controller can be solved via a set of linear matrix inequalities (LMI), which are derived from Lyapunov asymptotic stability and the minimization of the worst case infinite horizon quadratic objective function. The proposed control system is evaluated by both cosimulations of MATLAB/Simulink & CarSim and real-bus tests. Results show the effectiveness of the proposed controller, and it can ensure the control accuracy and strong robustness.

Model-Predictive-Control-Based Path Tracking Controller of Autonomous Vehicle Considering Parametric Uncertainties and Velocity-Varying

Vehicle control systems such as collision avoidance, adaptive cruise control and automated lane-keeping systems as well as ABS and stability control systems can benefit significantly from being made “road-adaptive”. The estimation of tire-road friction coefficient at the wheels allows the control algorithm in such systems to adapt to external driving conditions. This paper develops a new tire-road friction coefficient estimation algorithm based on measurements related to the lateral dynamics of the vehicle. A lateral tire force model parameterized as a function of slip angle, friction coefficient, normal force and cornering stiffness is used. A real-time parameter identification algorithm that utilizes measurements from a differential GPS system and a gyroscope is used to identify the tire-road friction coefficient and cornering stiffness parameters of the tire. The advantage of the developed algorithm is that it does not require large longitudinal slip in order to provide reliable friction estimates. Simulation studies indicate that a parameter convergence rate of one second can be obtained. Experiments conducted on both dry and slippery road indicate that the algorithm can work very effectively in identifying a slippery road. Two other new approaches to real-time tire road friction identification system are also discussed in the paper.Copyright © 2002 by ASME

GPS-Based Real-Time Identification of Tire-Road Friction Coefficient

This paper presents a new control scheme for longitudinal collision avoidance (CA) systems to improve the safety of four-in-wheel-motor-driven electric vehicles (FIWMD-EVs). There are two major contributions in the design of longitudinal CA systems. The first contribution is a new safety distance model to make vehicle adapt to different driving roads with an adhesive coefficient between tire and road and to conform to drivers' characteristics with a driving intention parameter. The second contribution is a new braking force distribution strategy based on constrained regenerative braking strength continuity (CRBSC). By optimizing the braking force distribution curve of hydraulic proportional-adjustable valve, the safety-brake range could be linearized to simplify the calculation of braking force distribution on the premise of ensuring braking safety. Furthermore, it is the constraint conditions that could solve the coexistence problem of positive and negative braking forces based on regenerative braking strength continuity (RBSC) to conform to actual requirements. The feasibility, effectiveness, and practicality of the proposed safety distance model and braking force distribution strategy are, respectively, verified by computer simulation experiments. Rapid control prototyping (RCP) and hardware-in-the-loop (HIL) simulation experiments using dSPACE are carried out to demonstrate the effectiveness in the control scheme, simplicity in structure, and flexibility in implementation for the proposed longitudinal CA system.

Longitudinal Collision Avoidance Control of Electric Vehicles Based on a New Safety Distance Model and Constrained-Regenerative-Braking-Strength-Continuity Braking Force Distribution Strategy

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

https://www.mdpi.com/1424-8220/19/19/4289/pdf

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Regenerative braking was the process of converting the kinetic energy and potential energy, which were stored in the vehicle body when vehicle braked or went downhill, into electrical energy and storing it into battery. The problem on how to distribute braking forces of front wheel and rear wheel for electric vehicles with four-wheel drive was more complex than that for electric vehicles with front-wheel drive or rear-wheel drive. In this work, the frictional braking forces distribution curve of front wheel and rear wheel is determined by optimizing the braking force distribution curve of hydraulic proportional-adjustable valve, and then the safety brake range is obtained correspondingly. A new braking force distribution strategy based on regenerative braking strength continuity is proposed to solve the braking force distribution problem for electric vehicles with four-wheel drive. Highway fuel economy test (HWFET) driving condition is used to provide the speed signals, the braking force equations of front wheel and rear wheel are expressed with linear equations. The feasibility, effectiveness, and practicality of the new braking force distribution strategy based on regenerative braking strength continuity are verified by regenerative braking strength simulation curve and braking force distribution simulation curves of front wheel and rear wheel. The proposed strategy is simple in structure, easy to be implemented and worthy being spread.

A new braking force distribution strategy for electric vehicle based on regenerative braking strength continuity

The real-time knowledge of tire-road friction conditions is indispensable for many vehicle control systems, particularly active safety control systems such as adaptive cruise control (ACC), anti-lock braking systems (ABS), electronic stability program (ESP), and acceleration slip regulation (ASR). However, the tire-road friction coefficient is not measured directly. Therefore, several different identification approaches are proposed to obtain the information on the tire-road friction coefficient indirectly. This paper introduces tire force system and the developing status on identification of tire-road friction conditions involving the approach based on sensor equipment and the approach utilize the measurement of the vehicle motion, and some classical identification algorithms used in this field. At last, the existing problems and the future development direction of identification of tire-road friction conditions are pointed out and discussed.

Development of identification of tire-road friction conditions

A new strategy for the electric vehicle (EV) with individual four-wheel drive acquiring maximum acceleration while avoiding tire skid is proposed in this paper. The vehicle longitudinal dynamic model based on the LuGre friction dynamic was built. A sliding mode observer (SMO), which is based on the vehicle dynamic model, was proposed to estimate the slip of tires and vehicle speed under unknown road conditions by measuring electromagnetic torques and the angular velocity of four motors. Due to the slip estimation, the vehicle velocity sensor is not needed. The maximum torque of each motor can be determined in real time, thus the vehicle can acquire maximum acceleration without slipping. Finally, the control strategy proposed in this paper is verified by simulation of an electric vehicle model under different road conditions.

http://ieeexplore.ieee.org/iel7/7043694/7052676/07053452.pdf

Leilei Hu

Papers

Longitudinal Collision Avoidance Control of Electric Vehicles Based on a New Safety Distance Model and Constrained-Regenerative-Braking-Strength-Continuity Braking Force Distribution Strategy

A new braking force distribution strategy for electric vehicle based on regenerative braking strength continuity

Development of identification of tire-road friction conditions

A sliding mode observer of road condition estimation for four-wheel-independent-drive electric vehicles