# Characterization of Driver Dynamic Visual Perception Under Different Road Linearity Conditions
## 1 Introduction  
The visual system, as the main sensory system through which drivers acquire road information during driving, plays a decisive role in ensuring driving safety [1, 2, 3, 4, 5]. Especially in complex roadway environments such as turns, climbing and downhill sections, and steep curves, drivers face diverse and challenging visual tasks [6]. Different road features have a profound impact on drivers' visual perception, attention allocation, information processing, and decision-making abilities [7], which in turn affect drivers' driving behaviors and the incidence of traffic accidents [8].  
On straight roadways, drivers are usually able to acquire road information at longer distances with relative ease, thus enabling them to detect potential traffic risks earlier. However, when a vehicle enters a turning or curved roadway, the driver's visual distanceis significantly shortened and the complexity of the visual task increases [9]. Studies have shown that the design of a turning roadway has a significant impact on the driver's visual task; especially when the curve is sharp or the traffic density is high, the driver needs to pay more attention to the curvature of the roadway and obstacle identification [10]. This visual compression not only affects drivers' recognition of traffic obstacles but may also give them a tendency to overdrive or underestimate hazards [9, 11]. Climbing and downhill segments are equally important in driver visual tasks. Climbing segments usually result in limited visual range for drivers, and the dynamics of a vehicle traveling on an incline require a high level of reaction speed and judgment. In climbing sections, drivers usually underestimate the traffic obstacles ahead, especially in poor road conditions or large gradients, which increases the risk of traffic accidents [12, 13]. In contrast, downhill sections pose a greater challenge to drivers' visual attention and reaction speed. During downhill driving, due to gravity, drivers need to remain alert to ensure safe speeds and monitor the road surface condition and traffic conditions in real time. It has been pointed out that the steepness of a slope affects the driver's visual assessment of the road environment [14], especially in terms of significant delays in vehicle braking and obstacle-avoidance responses. With the continuous development of advanced driver assistance systems (ADASs) and autonomous driving technologies [15], the assurance of traffic safety has gradually shifted towards automation [16]. Existing driver assistance technologies, such as automatic emergency braking and adaptive cruise control, alleviate the visual pressure of drivers in complex environments to a certain extent [17]. However, these systems still have certain limitations when facing highly complex roadways [18, 19]. It is still difficult for current autonomous driving systems to fully simulate the driver's visual adaptation and judgment process in specific roadway environments [20], and thus the visual ability of human drivers remains one of the key factors in ensuring traffic safety.  
In order to gain a deeper understanding of drivers' visual characteristics in different roadway environments, this study will combine eye tracking, driving simulation experiments, and real road tests to explore drivers' visual tasks and decision-making patterns in a variety of roadway environments [21], such as straight ahead, turning, climbing, downhill, and sharp curves and steep slopes [22, 23]. This study aims to reveal the influence mechanism of different road conditions on drivers' visual behavior [24] and then provide theoretical basis and data support for road design, traffic safety management, and further development of intelligent driving technology. Through the systematic analysis of drivers' visual responses in complex road environments [25], this study helps to promote the optimization of traffic safety and the innovative development of intelligent transportation systems.