## 4 Modeling and Assessment
### Driver Behavior Modeling  
This paper extracts sets of environmental quantification indicators and driving behavior features, and attempts to establish a connection between them using drivers' environmental understanding and behavior models. After the normalization process mentioned above, different driving environments can be represented by three quantified environmental indicators. Therefore, for various environments, a unified quantification formula can be expressed as:  
\[A=\alpha\times X_{rms-p}+\beta\times D(f)_{lap}+\lambda\times NCC(p,d) \tag{7}\]  
where \(X_{rms-p}\) represents visual brightness, \(D(f)_{lap}\) represents visual clarity, \( NCC(p,d)\) represents visual obstruction. \(\alpha\), \(\beta\), and \(\lambda\) represent corresponding coefficients.  
The behavior of drivers is influenced by environmental factors. One crucial factor is the lighting conditions, which play a significant role in shaping driver behavior. When visibility is excellent due to good lighting conditions, drivers generally exhibit more aggressive behavior on the road. This manifests in their tendency to drive at higher average speeds and brake less frequently. The improved visibility allows drivers to perceive and react to their surroundings more efficiently, potentially leading to a greater sense of confidence and willingness to take risks. Conversely, when faced with poor lighting conditions, such as at night or in inclement weather, drivers tend to adopt a more conservative driving style. The reduced visibility in these situations necessitates caution and careful navigation. Consequently, drivers often drive at lower average speeds and brake more frequently to maintain control and ensure safety.  
Recognizing the correlation between driving behavior and lighting conditions, we introduce the concept of the driver conservation factor (DCR). DCR serves as a quantifiable measure to assess the level of conservativeness or aggressiveness in driver behavior. The model-building process is shown in Figure 7. It is determined by calculating the average driving speed, with a higher average speed corresponding to a larger DCR value. This factor allows us to establish a driver behavior model that incorporates the influence of environmental conditions, particularly lighting, on driver decision-making and actions. By considering the interplay between environmental factors and driver behavior, our proposed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Sets** & **Visual Brightness** & **Visual Clarity** \\ \hline \(v_{max}\) & 0.98915 & 0.98915 \\ \(v_{avg}\) & 0.93976 & 0.97302 \\ \(\theta_{max}\) & 0.92844 & 0.93976 \\ \(\theta_{avg}\) & 0.95742 & 0.92844 \\ \(\delta_{max}\) & 0.96297 & 0.95742 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlation Analysis of Partial Behavioral Feature Sets and Environmental Quantitative Indexes.  
model aims to enhance our understanding of how drivers adapt and respond to varying conditions on the road.  
\[DCR=\varphi*v_{avg}+\mu*\theta_{avg}+\cdots+\epsilon*\delta_{avg} \tag{8}\]  
where \(DCR\) represents the driver conservation factor, \(v_{avg}\) represents speed average, \(\theta_{avg}\) represents throttle average angle, \(\delta_{avg}\). Represents braking average angle. \(\varphi\), \(\mu\), and \(\epsilon\) represent corresponding coefficient.  
The driver behavior characteristics in the driver behavior model can be increased or decreased according to the scene, and at the same time, the coefficients can also be corrected according to the experimental scene and experimental data, which improves the robustness of the model. Verified by experimental data, there is a strong linear correlation between the quantitative value of the environment and the driver's behavior factors, which shows that the model is effective under the premise of the experimental assumption.