## 1 Introduction  
Roadway crashes have been a primary cause of fatalities worldwide, directly resulting in approximately 1 million deaths and 50 million serious injuries annually [1]. Reducing the injury risk (IR) from crashes, especially the potential severe outcome, has been a major focus of extensive academic research and engineering projects related to roadway safety [2]. Many studies have attempted to model roadway safety by analyzing massive amounts of crash information and parsing injury outcomes with crash reconstruction analysis [3, 4]. The current well-established estimates of crash severity are based on IR functions that are modeled on post-crash variables, such as the change in velocity (\(\Delta\)V) sustained by a vehicle before and after a crash. Although posterior analysis can infer crash mechanisms, real-time assessment of safety maneuvers in the pre-crash phase remains challenging. Recently, innovative risk assessment technologies that use real-time information have become an emerging trend in active roadway safety, driven by the continuous advancements in vehicle safety technology. Accordingly, many researchers have suggested that active safety measures can greatly benefit from credible applications that use identifiable pre-crash information as indicators for potential crashes [5, 6, 7]. This approach could complement the traditional post-crash analysis and enable real-time assessment of safety maneuvers, which is currently difficult to achieve. The pre-crash phase of a vehicle refers to the period from the occurrence of a hazardous situation to the moment when the collision becomes unavoidable. From the perspective of preventive assessment, two criteria must be satisfied to evaluate the latent dynamic risk for this phase: the proximity to a crash and the potential severity [8]. A priori variables, such as pre-impact velocity and closing angle, must be considered because they are crucial in retrieving IR based on pre-crash phase information [9]. Traffic conflict is commonly used to interpret risky threats with real-time operation information. However, the lack of a clear connection between conflict parameters and the prevalent severity scale has resulted in limited research on measuring injury severity within the pre-crash framework [10]. [PERSON] established the explicit relationships between the conflict index and the actual crash severity measured by the maximum abbreviated injury scale (MAIS) to assess the potential risk [11]. This finding demonstrates that the gap between the pre-events and injury severity can be filled by applying in-depth crash datasets that contain adequate variables and critical operation data prior to a crash. Synthetic crash data provide the key elements in tuning models that can describe the complexity of real impacts and represent a valuable tool for the a priori assessment of IR [12]. Several studies have investigated the causal factors of crash severity and their influence on crash characteristics. Based on the statistical techniques, evident findings indicate that factors involving vehicles, operating conditions, occupant attributes, and dynamic collision mechanics are considered to influence the severity of injury [13; 14; 15]. However, the effect of the same variable in different studies is skewed. [PERSON] noted that, unlike several existing studies, seat position had a significant influence on injury outcomes in frontal crashes [16]. [PERSON] indicated that the passengers' gender and age have no particular relationship with injury severity [17], and [PERSON] emphasized the elevated effect of these two factors in assessing exposure to severe injuries [18]. According to potential interpretation, these factors are undoubtedly relevant to the IR, but they do not directly dictate the outcomes in comparison with the impact characteristics [19].