# Game Theory-Based Interactive Control for Human-Machine Cooperative Driving
## 2 Problem Formulation  
As introduced in Section 1, existing research on the traffic participants that HSCV needs to avoid in safe driving are usually set as static obstacles, or SVs, with fixed driving characteristics, and the motion state remains unchanged. However, there are significant differences in the driving styles of different drivers, and the research methods related to driving style recognition include the Markov model, K-means clustering, Bayesian learning, and other methods [31]. Usually, the three labels of aggressive, moderate, and cautious are used in many studies to distinguish driver styles [30; 33; 34; 35]. Aggressive drivers often pursue traffic efficiency and perform sharp acceleration and steering behaviors. For example, an aggressive driver may suddenly accelerate and seize the road, preventing neighbor vehicles from changing lanes. Aggressive drivers even have the dangerous behavior of suddenly changing lanes and ignoring the existence of neighbor vehicles. However, cautious drivers worry about driving safety and will choose a lower speed and maintain a longer following distance. This article focuses on the impact of the dangerous behavior of SVs on HSCVs and how to safely control HSCVs rather than the classification of driving styles. The driving style of SVs will be designed based on the driving style cost function, in which two critical indicators of driving safety and travel efficiency that reflect personalized driving style will be taken into consideration.  
Lane changing is a typical driving behavior of vehicles which is also prone to lead to traffic accidents, especially in high-speed scenarios. This paper mainly focuses on ensuring the safety control of an HSCV in the lane changing scenario, including the HSCV's avoidance of the sudden lane changes of neighbor vehicles (NVs). The driving environment module in Figure 1 shows common driving scenarios in which the red vehicle driving on road 2 is an HSCV controlled by a human driver and an automation system. The vehicles on either side of the HSCV are represented as NV1 and NV2, respectively. The vehicles in front of the road are represented as leader vehicles (LVs). The HSCV is the subject of this study, and it can also be expressed as an ego vehicle, and all NVs and LVs around the HSCV can be called SVs. For the convenience of expression, superscript symbols \(e\) and \(V_{i}\) are proposed to distinguish the HSCV from SVs, where \(i\) is the index of SVs.  
The overall safety obstacle avoidance control framework of the HSCV is illustrated in Figure 1. Firstly, before the HSCV executes vehicle control commands, its automation control system needs to integrate the control commands of the driver and the control system based on the allocated driving authority. The driver's control commands are simulated by the brain emotional learning circuit model (BELCM), and the driving control authority between the driver and the control system is dynamically adjusted in real time by the driving safety field simulated by the driving environment information. In addition to the driver control input, the HSCV automation control system must also consider the interaction with SVs with different driving styles, which the driving style cost function will simulate. Finally, the non-cooperative game method was adopted to find the optimal solution for the HSCV to achieve safe control and obstacle avoidance.