# Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation
## 3 Selecting Driving Locations for Multiple Paths
### Selecting a Fixed Number of Driving Locations for Smallest Distance
In Objective II, the number of driving locations is fixed, which means that the cost of maintaining the driving locations is fixed. With the given budget of driving locations, we want to reduce the latency as much as possible. By doing so, we can increase safety. We evaluate the performance of the proposed heuristic algorithms, BS and K-Means, with two baseline algorithms, Random Search and Simulated Annealing. The number of base stations and candidate driving locations are 3000 and 500 as before. We also generate 1000 origin-destination pairs. We vary the number of remote driving locations to select from 10 to 20, i.e., \(k=10,\cdots,20\). Figure 7 shows the distances of the algorithms. As can be seen in Figure 7, the two heuristic algorithms, BS and K-Means, show similar performance over various \(k\). They are much better than Random Search. This means that the proposed heuristic algorithms work well in reducing the distance to the base stations. Furthermore, BS and K-Means perform similar to SA. Since SA is a general optimization algorithm, this result suggests that the proposed algorithms may perform near optimal in terms of reducing the distance to the base stations. Actually, the performance of SA and Random search depends on the number of iterations in running the algorithms. In this simulation, the number of iterations for both Random search and SA is 10,000. The performance of Random search and SA does not improve much after 10,000 iterations. Figure 6: Average distance for different number of drivers. Figure 7: Distances over various number of remote driving locations. ### Result Discussion and Comparative Analysis

The simulation evaluations for the two objectives show that the proposed algorithms are good enough to provide reasonable solutions for the given problems. The proposed algorithms in this paper usually perform better than the existing algorithms, namely, LAF, RDSMP and K-Means. The main reason for this better performance is due to the exploitation of the specific characteristics of the problem itself. First of all, the base stations along the routes are not randomly distributed ones. Thus, covering the origin and destination earlier than the base stations in the middle can provide better performance compared to K-Means. Furthermore, in this paper, we formulate the objectives more clearly so that we can come up with a better solution such as ERDSMP, which explicitly tries to reduce the latency as much as possible. We briefly provide a comparative analysis of these algorithms in Table 3.

## 5 Conclusions

In this paper, we focus on the _interim_ situation towards fully autonomous vehicle technology. Since the current AVs do not fulfill the level 5 of AV technology, we try to add human intervention in controlling AVs. The biggest challenge in such remote driving is the latency between the remote drivers and the vehicles. Thus, we focus on selecting appropriate remote driving locations satisfying given requirements. For that matter, we propose several algorithms to select a set of remote driving locations while satisfying given constraints. To reflect various requirements from the users, we define two objectives in selecting the remote driving locations, each of which focuses on different aspect of remote driving. Through simulations, we show that the performance of the proposed algorithms are better than baseline algorithms and near optimal. We plan to explore these algorithms with real world data of the road network and communication network for the position of base stations and driving locations in the future.