# Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation
## 2 Background and Literature Review
A commercial long term evaluation (LTE) network has been used to investigate human behaviors in remote driving, where they use a scaled remote driving prototype and conduct a controlled human study with different network delays [19]. They conclude that performance degradation is caused by network delay _variability_ rather than _magnitude_. Furthermore, they conclude that the current LTE network is infeasible for remote driving. However, in another study, a real-time streaming testbed for a vehicular environment through audio visual techniques has been evaluated on frame latency under several parameter settings with today's LTE and Wi-Fi networks [3]. The results show that the median latencies of LTE and Wi-Fi are around 100 ms and 50 ms, respectively, which may be acceptable for remote driving. In [20], a model car has been tested for remote and virtual driving by using Wi-Fi, and the experimental results confirm the reliability in real-time remote driving. In [5], a static multi-camera with fusion display and 3G communication network has been used for remote driving of vehicles to guarantee the safety of driving. A lightweight passive tool has been introduced for the identification of present LTE networks flaw (\"used bandwidth less than 50 percent of the available bandwidth\"). They suggest a remote control mechanism by using a more LTE friendly transport protocol and applications [21]. In [22], LTE-Advanced features have been highlighted to improve the quality of service (QoS) specifically focusing on network capacity delivery in terms of offloading and congestion control. Refs. [19; 23] broadly investigate those factors that influence human remote driving. Refs. [17; 19] reveal that when delay exceeds 700 ms, driving performances significantly degrade and delay beyond 1 s restricts the effective real-time interactions through tele-operation. Ref. [18] also finds that the efficiency of human remote driving is influenced by small constant delays. Actually, ref [19] shows that a local Internet with distance around 1000 mile can provide a round trip time RTT of 16 ms with 4 Mbps minimum bandwidth. Thus, the current communication network is sufficient enough to support the remote driving technology. In our paper, remote drivers actually control and monitor the vehicles. Similarly, even though an autonomous vehicle can operate by itself, someone need to monitor the vehicles for various purposes. For the remote monitoring, a large amount of data should be transmitted from the vehicle to the operation center. Ref. [24] proposes a compression method of LIDAR data specifically. Since the data set is a time series data, they apply various signal processing methods such as discrete cosine transform (DCT) and differential sampling. This method can be also used in remote driving systems for efficient transmission of the sensor data. Autonomous driving may have many security attacks. Ref. [25] provides a list of attack or security threat mechanisms and proposes counter measures of those problems. Specifically, they focus on localization and navigation technologies. Those issues should be addressed in any autonomous driving systems. There can be various algorithms for autonomous driving. Thus, it is necessary to evaluate those algorithms in a standard platform. Ref. [26] proposes an evaluation platform that jointly considers vehicular communication, road traffic and vehicle dynamics. They combine many existing simulators such as SUMO and CARLA to simulate various road and traffic situations. Furthermore, they investigate the behavior of a remote driving application based on CARLA. This platform may be useful for testing any new algorithms and applications. In remote driving, the network infrastructure between the driving center and the vehicle is crucial for the safety of the driving. Ref. [27] investigates the key features of 5G NR-V2X technologies. Furthermore, they discusses the evolution of the V2X (vehicle-to-everything) technologies to provide better understanding of the communication infrastructure.