# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 7 Simulation
### Simulation Scenario  
Two test scenarios are built for functional verification of the proposed method. Both scenarios are established on a 20 km-long road with four lanes. The difference between the two scenarios lies in the varying traffic flow speeds across the individual lanes, as illustrated in Figure 8a,b. In scenario 1, the traffic flow speed decreases gradually from the innermost to the outermost lane. In scenario 2, the innermost lane has the highest traffic flow speed, while the two middle lanes have the lowest. The ego vehicle is located in the outermost lane in both scenarios. For scenario 1, when the ego vehicle changes lanes from the outermost to the innermost lane, each lane change provides a speed advantage. This scenario will be used to test whether the proposed model and the baseline model can generate common lane-change maneuvers. For scenario 2, when the ego vehicle moves from the outermost lane to the middle lanes, it loses the speed advantage; however, moving from the middle lanes to the innermost lane allows for a significant speed advantage. Thisscenario will be used to test whether the model can overcome local disadvantages and produce multiple related lane-change maneuvers.  
Since we have not found a method to directly control the lane-level traffic flow in VTD, the method used here is to place some controlled vehicles at very far positions in each lane to control the traffic flow. After a certain period of time from the start of the simulation, these controlled vehicles decelerate to their target speeds and enter the near range of the ego vehicle, thereby altering the states of traffic flow in each lane. In tests of the two scenarios, after the controlled vehicles decelerate for a period of time, the traffic speeds in the four lanes are as shown in Figure 9a and 9b, respectively. It can be observed from these figures that the traffic situations in each lane have transitioned from their initial states to the predefined states. The parameters for the near-range traffic simulation for the ego vehicle during the tests are shown in Figure 10.  
Figure 8: Expected traffic flow status in scenario 1 (**a**) and scenario 2 (**b**).  
Figure 10: The parameter configuration of VTD’s pull traffic.  
Figure 9: Actual traffic flow status in scenario 1 (**a**) and scenario 2 (**b**) in the simulation.  
Since the change of the traffic flow state takes a certain amount of time, at the beginning of the simulation, the ego vehicle will be restricted to the lane-following-only mode and will not be allowed to change lanes until all controlled vehicles decelerate for enough time, which means that the traffic flow in each lane approaches the predefined scenario. The tests of both of the two scenarios for the proposed model and the baseline model are repeated 5 times. In addition, it should be pointed out that in the simulation, the proposed model only performs a lane change after maintaining the turn signal for 3 s, while the baseline model follows its decision result and performs lane change immediately.  
In the simulation, the outlook region is set to cover a time headway of 30 s, providing sufficient space for the ego vehicle to plan the lane-level driving task which contains maneuvers about entering the innermost lane from the outermost lane. The values of the key parameters of the proposed model are shown in the Table 1. The aggressive parameter set from AV-IDM [29] is adopted for the baseline model and the motion control module in the simulation.