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TOVAC: Tele-operated Vehicle Admission Control and Routing

Jorge Martín-Pérez, Carlos M. Lentisco, Luis Bellido, Ignacio Soto, David Fernández

TL;DR

The paper tackles the challenge of providing ultra-reliable, low-latency tele-operated driving over cellular networks by introducing TOVAC, a channel-aware admission control and routing framework. It builds a capacity graph that encodes per-road maximum admitted vehicles using a probabilistic SINR model and radio-resource accounting, and solves time-expanded multi-commodity flow with a capacity-aware A* route planner to ensure latency and reliability. The approach guarantees the 5 ms latency and 99.999% reliability while keeping utilization low and avoiding unsafe overlaps, as demonstrated in Turin with realistic network data and bandwidth configurations. This enables scalable, safe ToD operations and suggests opportunities for elastic resource sharing and online route adaptation in future networks.

Abstract

Tele-operated Driving (ToD) is a challenging use case for mobile network operators. Video captured by the built-in vehicle cameras must be streamed meeting a latency requirement of 5 ms with a 99.999% reliability. Although 5G offers high bandwidth, ultra-low latencies and high reliability; ToD service requirements are violated due to bad channel conditions. Ignoring the channel state may lead to over-estimate the number of ToD vehicles that can meet the service requirements, hence comprising the vehicle security. To fill this gap, in this letter we propose TOVAC, an algorithm that guarantees ToD service requirements by taking adequate admission control and routing decisions. This is achieved by using a channel-based capacity graph that determines the maximum number of vehicles that can be tele-operated in any road section. We evaluate TOVAC considering cellular deployments from Turin and show that, unlike a state of the art solution, TOVAC guarantees the ToD service requirements.

TOVAC: Tele-operated Vehicle Admission Control and Routing

TL;DR

The paper tackles the challenge of providing ultra-reliable, low-latency tele-operated driving over cellular networks by introducing TOVAC, a channel-aware admission control and routing framework. It builds a capacity graph that encodes per-road maximum admitted vehicles using a probabilistic SINR model and radio-resource accounting, and solves time-expanded multi-commodity flow with a capacity-aware A* route planner to ensure latency and reliability. The approach guarantees the 5 ms latency and 99.999% reliability while keeping utilization low and avoiding unsafe overlaps, as demonstrated in Turin with realistic network data and bandwidth configurations. This enables scalable, safe ToD operations and suggests opportunities for elastic resource sharing and online route adaptation in future networks.

Abstract

Tele-operated Driving (ToD) is a challenging use case for mobile network operators. Video captured by the built-in vehicle cameras must be streamed meeting a latency requirement of 5 ms with a 99.999% reliability. Although 5G offers high bandwidth, ultra-low latencies and high reliability; ToD service requirements are violated due to bad channel conditions. Ignoring the channel state may lead to over-estimate the number of ToD vehicles that can meet the service requirements, hence comprising the vehicle security. To fill this gap, in this letter we propose TOVAC, an algorithm that guarantees ToD service requirements by taking adequate admission control and routing decisions. This is achieved by using a channel-based capacity graph that determines the maximum number of vehicles that can be tele-operated in any road section. We evaluate TOVAC considering cellular deployments from Turin and show that, unlike a state of the art solution, TOVAC guarantees the ToD service requirements.
Paper Structure (8 sections, 1 theorem, 14 equations, 4 figures, 1 table, 1 algorithm)

This paper contains 8 sections, 1 theorem, 14 equations, 4 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. capacity model
  3. Radio resources for remote driving
  4. Cell se
  5. Road capacity graph
  6. Admission Control and Routing
  7. Performance Evaluation
  8. Conclusion

Key Result

Corollary 1

If the cell power follows an exponential distribution $h\sim exp(\mu)$, and the interfering cells power is also exponential and identically distributed $g_i\underset{i.i.d}{\sim} exp(\lambda)$; the satisfies with $\alpha>0$ the path loss exponent, $\sigma^2$ the value of additive and constant noise power, and $d_{r,\nu}$ the distance between cell $r$ and vehicle $\nu$.

Figures (4)

  • Figure 1: Illustration of the edges within capacity graph, each with the maximum number of vehicles admitted $\nu_e^{\max}$. Further roads have smaller capacity (yellow) due to path loss, while a road nearby the cell $r$ (green) has higher capacity. Consequently, TOVAC just admits the first two vehicles and rejects the third (top vehicle), since cell $r$ only guarantees the service requirements for a single vehicle $\nu_e^{\max}=1$ at roads $(u,w),(u,v)$.
  • Figure 2: Heatmaps with the maximum number of remote driving vehicles that can be operated with 99.999 % reliability in Turin.
  • Figure 3: violation ratio (y-axis) experienced among the admitted vehicles (x-axis).
  • Figure 4: violation ratio (y-axis) experienced among the admitted vehicles (x-axis) for different reliability requirements, with 240 MHz bandwidth.

Theorems & Definitions (2)

  • Corollary 1
  • proof