# Strategy and Evaluation of Vehicle Collision Avoidance Control via Hardware-in-the-Loop Platform
## 1 Introduction  
Recently, steering assistance technologies of the vehicle have been on the rise because of safety and legal requirements. Automakers are now urgently developing and announcing relevant safety functions, such as autonomous emergency braking (AEB), to highlight steering assistance. Basically, AEB is a kind of technology for collision avoidance systems (CASs). AEB can be treated as a one-dimensional CAS. However, a general CAS should cover not only one-dimensional collision avoidance, but also two-dimensional collision avoidance. Consequently, the CAS is a necessary function for steering assistance. According to [PERSON] et al. [1], the rear-end collision is 32% of all the car accidents in urban cities, especially in crowded traffic. Additionally, their results showed that by merely adding the AEB function, rear-end collisions can be effectively reduced by 38%. It is known that AEB is a one-dimensional approach for the CAS. However, in some city driving scenarios, certain highway driving is still required. Therefore, the two-dimensional CAS sparks the interest of this study. Generally, the presented CAS includes both one-dimensional and two-dimensional collision avoidance technology. The one-dimensional approach is similar to the AEB and the known adaptive cruise control (ACC). In addition, the two-dimensional approach is the basis of automatic steering. The CAS approaches are known as the advanced driver assistance systems (ADAS). They can decrease the fatigue of the driver and the possibility of accidents [2]. For instance, when the ADAS prompt is considered as the distance up to the maximum, the driver does not pay any attention to it. If the distance is less than the minimum, the system will initiate one-dimensional braking automatically to avoid rear-end collisions. However, if there are unexpected obstacles or accidents that occur whiledriving, the system intervenes to avoid the collision. Therefore, the intelligence for driving intervention based on ADAS is important [3].  
Basically, the embedded system needs to intervene with control measures in a timely manner and thus utilizes two judgment principles: (a) avoid all collisions and (b) never perform a faulty intervention [4; 5]. If the system causes a disturbance between the driver and the driving safety system of ADAS, it can cause an accident. Hence, the driving strategy prompts the driver to pay attention to the distance between vehicles when it is up to the maximum. Likewise, if it is less than the minimum, the system will intervene to avoid the collision by braking. The concept is similar to that employed in some intelligent control theories. This paper proposes a fuzzy-based control algorithm to maintain the braking force and its related safety. For evaluation, testing related strategies under real conditions leads to significant efforts and costs [6; 7]. In this study, a hardware-in-the-loop (HIL) platform is used for evaluations. The inputs from the operator are real; however, the vehicle dynamics are virtual. This platform setup facilitates cost-effectiveness for full-scale evaluations. In this paper, these tools are employed to evaluate the control strategy and its corresponding performances.