# Monovision End-to-End Dual-Lane Overtaking Network without Map Assistance
## 3 Simulation Experiment and Result Analysis
### Comparison with Existing Methods  
To explore the advantages and disadvantages of this approach in comparison to others, we conducted an evaluation according to the following criteria:  
1. The average L2 distance between the ideal and actual positions within the first five seconds of a successful overtaking maneuver. 2. Time spent in the adjacent lane \(M_{i}\);
3. Number of overtaking maneuvers initiated \(M_{o}\);
4. Success percentage of overtaking maneuvers \(M_{s}\). 5. Collision probability \(P_{c}\). 6. Average completion time per kilometer \(T_{c}\). 7. Completion of selected road segments _Route Completion_. 8. Trajectory smoothness and control signal smoothness. Table 2 provides a quantitative comparative analysis of the overtaking task. We replicated several lane-changing overtaking methods with lane-changing capabilities on the Carla platform. The experiments were conducted on a dual-lane highway segment measuring 1 km in length. We collected experimental data from 20 distinct scenarios, eachinvolving lane-changing overtaking tasks. Various factors were considered, including traffic density, vehicle speeds, and vehicle types. **Trajectory fitting analysis. ** The experiments showed that although our method did not discretize the reference point of the path like the traditional method and then carry out control planning towards the target path point, its actual effect was not poor for the overtaking route with a single trajectory. The differences between the ideal trajectory points and the actual trajectory points were calculated within the first five seconds, commencing from the initiation of the overtaking steering maneuver. In our approach, the steering control sequence was solely influenced by the velocity factor, resulting in consistent trajectories for the same velocity sequence. As a result, the disparities between the ideal and actual trajectories were significantly reduced compared to conventional methods. Table 2 reveals that our method demonstrated better performance in terms of trajectory fitting. **Overtaking ability comparison. ** Existing lane-changing research methods generally involve motion planning based on the assumption of ideal perceptual information. Therefore, we compared the performance of our approach under two scenarios: one where the information about other vehicles was known, and another where the information about other vehicles was unknown. When the information about other vehicles was known, relative velocities and positions between vehicles were calculated, and _GDCPs_ were employed to ensure path safety robustly. Simultaneously, in cases where the speeds of other vehicles were known, if the leading vehicle did not cooperate by reducing its speed, our method could abandon the overtaking attempt and ensure a return to the original lane within a certain timeframe. This contributed significantly to reducing the number of overtaking maneuvers initiated (\(M_{o}\)). Our approach followed the principle of emulating human driver behavior, overtaking only when success is certain, resulting in a shorter time taken for overtaking (\(M_{t}\)) in comparison to other methods. In scenarios where perception accuracy is high, the advantages of the vehicular networking series of methods in terms of overtaking success are undeniable. This is because these methods not only acquire the positional information of surrounding vehicles but also gain insight into the intentions of other vehicles, which makes executing overtaking maneuvers more convenient. However, the requirement for all vehicles in the driving environment to be equipped with vehicular networking systems reveals certain drawbacks. Although the success rate of overtaking significantly drops when the perception information is less than ideal, the drop is not as pronounced compared to other perception-aware methods. This provides evidence that an end-to-end approach for achieving lane changes is indeed feasible. **Smoothness analysis.