## 1 Introduction  
As the rapid innovation of microprocessor technology continues, more investment in the research and development of vehicle electronic control technology in the context of automated driving has been conducted [1, 2, 3, 4]. Road fatalities has been one of the major death causes globally [5], and it is projected to become the fifth leading cause of death by 2030 [6]. In the same report, it is noted that the human factor is the main reason for road accidents globally, with [PERSON] et al. [7] suggesting that 93% of road accidents were caused by human error and driver matentiveness. Nevertheless, road accidents can be prevented by assimilating automation components into vehicle driving systems, thus reducing human error by controlling the vehicle in ways similar to skillful and careful drivers [8, 9]. Therefore, vehicle active safety control technologies, such as advanced driver assistance systems (ADAS) that help the driving process automation are playing an important role in modern passenger cars and commercial vehicles. One of the main features of ADAS is the collision avoidance (CA) system. A sufficient CA architecture usually encompasses threat assessment, path planning and path tracking strategies [10]. As road collisions might happen in various scenarios, a successful CA system should have the ability to guide the avoidance navigation of the host vehicle in multi-scenario hazards. For example, collisions at intersections, high-speed highway collisions, CA in crowded urban areas as well as collision mitigation of a priori unknown obstacles [11; 12]. There are at least two common types of CA design control architecture which have been proposed and implemented. For example, a multi-layer CA system [13], which contains both trajectory re-planning layer and tracking layer [14]. This is the usual type of CA implementation and is also known as a guidance and navigation control system [15]. In addition, optimization algorithms based on different mathematical theories have been proposed and applied in the CA system. Threat assessment strategies provide an online evaluation of the potential threat to assist drivers in avoiding or evading collisions [16]. According to [PERSON] et al. [17], threat assessment can be defined as a real-time judgment of a function which calculates the possibility of an unavoidable road accident. A path planning (PP) strategy as part of the full CA architecture can be widely used in the field of road vehicles and mobile robots. It is formulated to acquire a collision-free path while the vehicle independently navigates itself [12]. In the condition of the avoidance of single or multiple static obstacles, many studies have been carried out. Inspired by viscous fluid flowing between two parallel plates or around a cylinder, [PERSON] et al. proposed virtual fluid-flow-model based lane-keeping integrated with a collision avoidance control system to realize the function of both lane-keeping and collision avoidance [18]. The predictive nature and constraint handling capabilities of model predictive control (MPC) make it an attractive framework for leveraging these new technologies, and it is also adopted in CA [15; 19]. A machine learning approach, such as the artificial neural network [20; 21; 22] and collision cone approach [23], depends on the driver behavior and the environment instead of the mapping information alone, making it reliable in the context of dynamic CA. The advantage of this method is, once a solution is found, it will be a shorter path. However, getting stuck in local minima is also an issue that should be addressed [24]. At the same time, its drawback is it requires a large trial to obtain the results, thus the computational time is also an issue [25]. However, the literatures mentioned above mostly focuses on the fixed obstacles, like [19], or moving obstacles in the same direction as the ego vehicle, like [26], and the speed of the ego vehicle and obstacles remains a constant.