Since driver error is considered a major cause in over 90% of all road crashes [1], driving assistance systems are widely used to improve driving safety, and are an important research direction in the intelligent vehicle field. With the rapid development of sensing, identification and communication technologies, the vehicle can obtain an increasing amount of information in real time, ranging from the vehicle status information to road information and traffic information. Therefore, the function of the driving assistance systems is gradually expanding. The application of the model predictive control theory in automobile control has received wide attention [2–5]. One of the main reasons is that it can predict the future state of the system. For an automobile’s active safety control under extreme operating conditions, the safety control input can be obtained through the coordination of safety, comfort, and other indexes. Depending on the degree of risk, the weights of the indexes will be different. By comparing the safety control input with the driver input, the correctness of the human driver’s action can be evaluated. Based on the evaluation results, the driving assistant system will decide whether to intervene in the movement of the vehicle. Since the safety control input is calculated by predicting the future state of the system, we call this control method the predictive safety control; a general block diagram that reflects the control idea is shown in Figure 1(a). It is an effective control method proposed for the driving assistance system under certain extreme operating conditions, for example, a tire blowout accident. In the following, we will combine the specific extreme operating condition of the tire blowout to explain how this control method can be used to design the driving assistance system. It is noteworthy that the example used in this article is only reflective of one case, and some modules have been simplified. For other cases of active safety problem, the predictive safety control diagram can be used flexibly.
[1]
Tulga Ersal,et al.
A study on model fidelity for model predictive control-based obstacle avoidance in high-speed autonomous ground vehicles
,
2016
.
[2]
D. Mayne,et al.
Receding horizon control of nonlinear systems
,
1990
.
[3]
H. Michalska,et al.
Receding horizon control of nonlinear systems
,
1988,
Proceedings of the 28th IEEE Conference on Decision and Control,.
[4]
Yanjun Huang,et al.
Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints
,
2017,
IEEE Transactions on Vehicular Technology.
[5]
Amir Khajepour,et al.
A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles
,
2017,
IEEE Transactions on Intelligent Transportation Systems.
[6]
Kara M. Kockelman,et al.
Implications of Connected and Automated Vehicles on the Safety and Operations of Roadway Networks: A Final Report
,
2016
.
[7]
H. ChenT,et al.
A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability *
,
1998
.
[8]
Kok Kiong Tan,et al.
Development of a Genetic-Algorithm-Based Nonlinear Model Predictive Control Scheme on Velocity and Steering of Autonomous Vehicles
,
2016,
IEEE Transactions on Industrial Electronics.