# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 4 Experimental Result
### Anomaly Trajectory Recognition Based on Trajectory Rules  
In the test set, vehicle trajectories were selected between adjacent cameras that had undergone re-identification to extract trajectories of the same ID. The trajectories were grouped based on predicting and validating the rear 1/2, 1/3, 1/4, and 1/5 lengths of trajectories from the previous camera with corresponding lengths of the front portion of trajectories from the current camera. The error comparison results are presented in Table 2.  
From the validation error analysis in Table 2, it can be observed that as the prediction distance increases, both the lateral root mean square error and vertical root mean square error continue to grow. However, the vertical root mean square error is larger compared to the lateral root mean square error, attributed to the smaller proportion of vertical distance resolution in pixel coordinates. Overall, the bidirectional LSTM model performs well in predicting vehicle trajectories, with a lateral root mean square error of only 0.56 m and a vertical root mean square error of 1.84 m when half of the trajectory is predicted in both forward and backward directions. The errors between the model's predictions and actual values are relatively small. To further enhance vehicle tracking in the context of this study, the blind spot vehicle trajectory prediction model based on bidirectional LSTM was utilized for trajectory concatenation and prediction between adjacent cameras. This approach aimed to obtain complete vehicle travel trajectories under adjacent cameras. Using the Xi'an Ring Expressway as the application scenario, with a camera spacing of 450 m and an effective trajectory length of approximately 200 m per camera, the predicted trajectory length required for the blind \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Predicted Trajectory Proportion** & **1/2** & **1/3** & **1/4** & **1/5** \\ \hline TransverseRMSE (m) & 0.56 & 0.45 & 0.36 & 0.31 \\ DirectionRMSE (m) & 1.84 & 1.23 & 0.81 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSE Comparison Results for Trajectory Prediction Experiments. Figure 12: Cumulative matching characteristic (CMC) curves of different models. spot was about 250 m, accounting for approximately 38.5% of the concatenated complete trajectory. Before concatenation, noise reduction was applied to the vehicle trajectory points by removing points with significant position data fluctuations. The resulting complete vehicle trajectory after concatenation is illustrated in Figure 13.  
In Figure 13, the trajectory prediction and concatenation results for two vehicles are displayed. From the overall trajectory prediction results, the predicted trajectories align well with the actual driving behavior of the vehicles. However, due to fluctuations in the obtained vehicle tracking boxes, the predicted trajectories also exhibit a certain degree of fluctuation. Nonetheless, the overall trend remains consistent with normal vehicle driving behavior. This indicates that the trajectory prediction model is capable of effectively handling noise and fluctuations, demonstrating favorable predictive performance. In Table 3, we quantitatively evaluate the performance of the method in this paper on wrong driving behaviour recognition. It can be seen that the method of this paper correctly identifies the retrograde behaviour on almost all video data. Similarly, we quantitatively evaluate the performance of our method on speed anomaly recognition. The experimental results are presented in Tables 4 and 5. It demonstrates that the methodology of this paper has superior performance. Figure 13: Schematic diagram of vehicle behavior trajectory. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video** & **Vehicle Trajectory (bar)** & **Correct** & **Missed Detection** & **False Detection** \\ \hline
1 & 146 & 146 & 0 & 0 \\
2 & 90 & 90 & 0 & 0 \\
3 & 117 & 117 & 0 & 0 \\
4 & 125 & 123 & 2 & 0 \\ \hline \hline \end{tabular}
\end{table}