## Appendix A  
1. Case 1 of Tx1: Tx1 is located to the left of the center axis of the PD  
As shown in Figure 2, the lateral displacement \(L_{h_{1}}\) of the vehicle cannot change instantaneously and substantially due to the limitations of \(L_{w}\), \(v_{w}\) and the vehicle speed. Therefore, the uniform distribution can be used to model \(L_{h_{1}}\), i.e., \(L_{h_{1}}\sim u\Big{(}0,\frac{L_{w}}{2}\Big{)}\). In addition, since \(D_{LOS,1}^{V_{1}-PD}=\sqrt{L_{h_{1}}^{\ 2}+I_{V_{1}-PD}\ ^{2}}\), the distribution of \(D_{LOS,1}^{V_{1}-PD}\) can be obtained according to the variable transformation method:  
\[D_{LOS,1,case1}^{V_{1}-PD}=X=\sqrt{L_{h_{1}}^{\ 2}+I_{V_{1}-PD}\ ^{2}}\sim f_{X}(x)= \frac{2x}{L_{w}\sqrt{x^{2}-I_{V_{1}-PD}\ ^{2}}}, \tag{1}\]  
where \(x_{1}=I_{V_{1}-PD}\leq x\leq x_{2}=\sqrt{I_{V_{1}-PD}\ ^{2}+\frac{L_{w} ^{2}}{4}}\). 2. Case 2 of Tx1: Tx1 is located to the right of the center axis of the PD  
In this case, since \(L_{h_{1}}\sim u\Big{(}0,\frac{L_{w}}{2}-v_{w}\Big{)}\), the following can also be obtained:  
\[D_{LOS,1,case2}^{V_{1}-PD}=Y=\sqrt{L_{h_{1}}^{\ 2}+I_{V_{1}-PD}\ ^{2}}\sim f_{Y}(y)= \frac{2y}{(L_{w}-2v_{w})\sqrt{y^{2}-I_{V_{1}-PD}\ ^{2}}} \tag{2}\]  
where \(y_{1}=I_{V_{1}-PD}\leq y\leq y_{2}=\sqrt{I_{V_{1}-PD}\ ^{2}+\Big{(}\frac{L_{w}}{2}-v_{w} \Big{)}^{2}}\). According to (9), (1) and (2), the average path loss of the LOS link from Tx1 is as follows:  
\[h_{LOS,1}^{PL\ \alpha\alpha}=\sum_{case}\left\{\int\left[\frac{D_{R}\ ^{2}(I_{V_{1}-PD})^{2 \varepsilon}p_{case}}{\xi^{2}(D_{LOS,1,case}^{V_{1}-PD})^{2+2/\varepsilon}}e^ {-\varepsilon_{E}D_{LOS,1}^{V_{1}-PD}(\frac{D_{R}}{\xi D_{LOS,1,case}^{V_{1} -PD}})^{\varepsilon/2}}\right]f_{D_{LOS,1}^{V_{1}-PD}(D_{LOS,1,case}^{V_{1} -PD})}dD_{LOS,1,case}^{V_{1}-PD}\right\}, \tag{3}\]  
where \(case\in\{case1,case2\}\) and \(p_{case}\) denote the probability that Tx1 lies to the left or to the right of the center axis of PD. Converting the summation notation in (3) to the form of the sum of two terms and integrating them separately, the following can be obtained:\[ \begin{split} I&=\frac{{D_{R}}^{2}(I_{V_{1}-PD})^{2/ \epsilon}}{\xi^{2}(L_{w}-v_{w})}\Bigg{[}\int_{x_{1}}^{x_{2}}\frac{1}{x^{1+2/ \epsilon}\sqrt{x^{2}-I_{V_{1}-PD}}^{2}}e^{-\epsilon x}(\frac{{D_{R}}}{\xi x})^ {{}^{\prime\prime 2}}dx+\int_{y_{1}}^{y_{2}}\frac{1}{y^{1+2/\epsilon}\sqrt{x^{2}-I_{V _{1}-PD}}^{2}}e^{-\epsilon xy}(\frac{{D_{R}}}{\xi y})^{{}^{\prime\prime 2}}dy \Bigg{]}\\ &\triangleq\frac{{D_{R}}^{2}(I_{V_{1}-PD})^{2/\epsilon}}{\xi^{2}( L_{w}-v_{w})}(I_{1}+I_{2}).\end{split}  \tag{10}\]  
Since the value of \(\epsilon\) is small enough, assuming \(\left(\frac{{D_{R}}}{\xi x}\right)^{\epsilon/2}\approx\frac{{D_{R}}}{\xi x}\) and \(\left(\frac{{D_{R}}}{\xi y}\right)^{\epsilon/2}\approx\frac{{D_{R}}}{\xi y}\), it follows that the following is true:  
\[ \begin{split} I_{1}&\approx e^{-\frac{\epsilon_{E} }{\xi}{D_{R}}}\int_{x_{1}}^{x_{2}}\frac{1}{x^{1+2/\epsilon}\sqrt{x^{2}-I_{V_{1 }-PD}}^{2}}dx\\ &=e^{-\frac{\epsilon_{E}}{\xi}{D_{R}}}\cdot\frac{I_{V_{1}-PD}^{- (1+2/\epsilon)}\Bigg{(}\sqrt{\pi}Gamma\Big{[}\frac{1}{2}+\frac{1}{\epsilon} \Big{]}-Beta\Bigg{[}\frac{4I_{V_{1}-PD}}{4I_{V_{1}-PD}}^{2}+{L_{w}}^{2}}, \frac{1}{2}+\frac{1}{\epsilon},\frac{1}{2}\Bigg{]}Gamma\Big{[}1+\frac{1}{ \epsilon}\Big{]}\Bigg{)}}{2 Gamma\Big{[}1+\frac{1}{\epsilon}\Big{]}},\end{split}  \tag{11}\]  
and  
\[ \begin{split} I_{2}&\approx e^{-\frac{\epsilon_{E} }{\xi}{D_{R}}}\int_{y_{1}}^{y_{2}}\frac{1}{y^{1+2/\epsilon}\sqrt{x^{2}-I_{V_{1 }-PD}}^{2}}dx\\ &=e^{-\frac{\epsilon_{E}}{\xi}{D_{R}}}\cdot\frac{I_{V_{1}-PD}^{- (1+2/\epsilon)}\Bigg{(}\sqrt{\pi}Gamma\Big{[}\frac{1}{2}+\frac{1}{\epsilon} \Big{]}-Beta\Bigg{[}\frac{4I_{V_{1}-PD}}{4I_{V_{1}-PD}}^{2}+({L_{w}-2v_{w}})^ {2}},\frac{1}{2}+\frac{1}{\epsilon},\frac{1}{2}\Bigg{]}Gamma\Big{[}1+\frac{1}{ \epsilon}\Big{]}\Bigg{)}}{2 Gamma\Big{[}1+\frac{1}{\epsilon}\Big{]}}.\end{split}