# Bayesian-Based Traffic Safety Evaluation Study for Driverless Infiltration
## 3 Results
### Risk Assessment Based on Bayesian Modelling  
This article used the Bayesian analysis software GeNIe to provide a detailed description of the modeling data simulation analysis and calculation process. Taking the C11 node as an example, GeNIe was run, a new network1 file was created, a new opportunity node in the graphical view was created and renamed it C1, the node button was double-clicked, and the 'Definition' tab was selected, At this point, the following dialog box popped up, referenced and standardized collected data (initial prior probability), and inputted the status of node C1 and the prior probability value of Y, N, 0.12, 0.88 in sequence. The success of nodes in the network was described by the prior probability distribution of their two results (Y and T), as shown in Figure 5.  
Using the same method, nodes C2, C3, and C4 were established in sequence and entered their respective states and prior probability values. Then, the prediction node C11 was created. To represent that Bayesian prediction depended on the actual occurrence prospect,  
The two states of node C11 were renamed to Y and T. The CPT of each node was entered, taking node C11 as an example, see Figure 7.  
The Update tool was clicked, updating the probability distributions in light of observed evidence. The posterior probability distribution of control risk C11 nodes was simultaneously displayed, as shown in Figure 8.  
According to the above method, other nodes were established and finally formed a complete Bayesian network. After updating the network, the complete probability of risk occurrence was 47%, as shown in Figure 9.  
Figure 5: C1 node Prior probability input. Figure 6: C11 node Bayesian network. The known data was substituted into the joint probability Formula (2) to obtain the full probability of A, as shown in Equation (10). \[\mathrm{P(A)}\ =\sum_{X_{i}=P11,D11,C11,R11,E11}\mathrm{P(}X_{i})\,\mathrm{P(A} \mid X_{i})=0.47 \tag{10}\]  
The calculation results were consistent with the software simulation results, indicating that GeNIe was reasonable for Bayesian network traffic safety risk prediction. In terms of the total risk probability, the probability of occurrence was 47%. According to Table 5, it was judged that the driverless risk was a medium level 3, which is generally controllable and in line with the actual situation of driverless traffic safety. In terms of decision risk, the probability was 39%, the highest share of the five risk factors. It showed that the expert group believes that the decision-making risk occupied the primary position in the whole risk factor, in which the GPS positioning, navigation map, and dynamic planning accounted for 25%, and the AI learning decision-making software accounted for 20%, and the two accounted for nearly half of the decision-making risk. If the perception system was compared to human eyes, then the decision-making system was equivalent to the brain of the driverless driving system, through the perception system to obtain all the peripheral environmental information data transmission to the decision  
Figure 8: Risk assessment results of C11 nodes based on Bayesian network. Figure 7: C11 node CPT input. making system, planning the optimal travel route, such as the encounter of obstacles or traffic congestion. The decision-making system can quickly re-plan the driving route, and reasonably avoid roadblocks or congested sections, to ensure that the vehicle can be in the shortest time to safely reach the destination and safely arrive at the destination in the shortest time. In the case of road risk, the probability was 36%, the second highest of the five risk factors. This indicated that the expert group believed that road risk, although an objective factor, still occupied a secondary position in the overall driverless risk factors. Visibility, due to the influence of harsh environments such as rain, snow, fog, and haze, had the highest share of road environment factors, with a 19% share.