## 4 Performance Evaluation of Millimeter-Wave V2V
### Performance of Millimeter-Wave V2V Communications to Support Safe Overtaking  
To estimate the amount of the minimum required sensor data for safe overtaking, we performed a simulation. The required sensor data rate was derived with \(d_{\text{be}}\) and \(d_{\text{oe}}\). Figure 17a shows the process flow of the simulation. Firstly, the output of the LiDAR sensors on the blocking and ego vehicle was simulated. When the ego vehicle used cooperative perception, it could use not only its sensor data, but also the sensor data of the blocking vehicle for the recognition process. In the recognition process, we defined that if the recognition score described in Equation (8) was more than 0.9, the ego vehicle recognized the oncoming vehicle. The recognition score was calculated under the prepared LiDAR sensor resolution sets of \((r_{\phi},\ r_{\theta})\). If the ego vehicle failed to recognize the oncoming vehicle, it believed that there were no vehicles on the oncoming lane. Since the ego vehicle did not know exactly whether there were vehicles on the oncoming lane, this misunderstanding led to a collision. If the ego vehicle recognized the oncoming vehicle, then it additionally checked two factors. One was ensuring a comfortable braking distance for preventing a collision. The other was ensuring the driving path for overtaking. In this  
Figure 15: The 0.01% outage capacity with height diversity under \(h_{\text{t1}},\ h_{\text{r1}}=0.38\) m, \(h_{\text{r2}}=0.48\) m, and \(h_{\text{r2}}=0.58\) m and without diversity at 60 GHz. simulation, the required time for overtaking was set to 5 s. If the ego vehicle did not pass either, it would stay on the lane. If it passed, it could overtake the blocking vehicle. Figures 18-20 show the simulation result of \(d_{\text{be}}=5,~{}10,~{}15\) m under the parameters of Table 1. The horizontal axis denotes the velocity of the ego vehicle, and the vertical axis denotes the sensor data rate required for the safe overtaking and the outage capacity of each carrier frequency. The black solid and dotted lines with markers show the required sensor data rate with and without cooperative perception. Since the calculation time grew drastically at more than 8 Gbps, extrapolation was used. The green, red, and blue solid lines show the realized 0.01% outage data rate for each \(d_{\text{be}}\) at 5, 30, and 60 GHz. Figure 21 shows the result of \(d_{\text{be}}=10\) m using non-linear spacing LiDAR sensors. Figure 16: The 0.01% outage capacity with height diversity for 5, 30, and 60 GHz. Figure 17: Description of the whole process in the simulation. Figure 19: Required data rate and 0.01% outage data rate realized by the 5, 30 and 60 GHz bands under \(d_{\text{be}}=10\) m.  
Figure 18: Required data rate and 0.01% outage data rate realized by the 5, 30 and 60 GHz bands under \(d_{\text{be}}=5\) m.  
Considering the realized data rate and the required sensor data rate using cooperative perception, the maximum velocity for safe overtaking under \(d_{\text{be}}=5,~{}10,~{}15\) m at 60 GHz was 66, 64, 67 km/h and at 30 GHz was 51, 49, 54 km/h. Since the realized data rate at 5 GHz was too small, the maximum velocity for safe overtaking was less than 20 km/h in all cases. In all \(d_{\text{be}}\) cases, cooperative perception using 60 GHz constantly ensured around 65 km/h for safe overtaking. In the case of using non-linear spacing LiDAR sensors, when 5, 30, and 60 GHz were used for cooperative perception, the ego vehicle could safely overtake 0, 9, and 7 km/h faster than using linear spacing LiDAR sensors. Although non-linear spacing LiDAR sensors improved the overtaking, the effect of providing sparse information about near objects should be noticed, especially in other driving scenarios.