Radio-Coverage-Aware Path Planning for Cooperative Autonomous Vehicles

TL;DR

This paper enhances autonomous vehicle path planning by integrating radio coverage considerations into algorithms like Dijkstra and A*, improving connectivity while maintaining near-optimal travel distances.

Contribution

It introduces radio-aware cost functions for path planning algorithms, enabling better wireless coverage for autonomous vehicles in dynamic environments.

Findings

Radio-aware path planning extends coverage with minimal increase in travel distance.

Proposed algorithms achieve mapping error below 2%.

Simulations validate improved coverage and efficiency.

Abstract

Fleets of autonomous vehicles (AV) often are at the core of intelligent transportation scenarios for smart cities, and may require a wireless Internet connection to offload computer vision tasks to data centers located either in the edge or the cloud section of the network. Cooperation among AVs is successful when the environment is unknown, or changes dynamically, so as to improve coverage and trip time, and minimize the traveled distance. The AVs, while mapping the environment with range-based sensors, move across the wireless coverage areas, with consequences on the experienced access bit rate, latency, and handover rate. In this paper, we propose to modify the cost of common path planning algorithms such as Dijkstra and A*, so that the best path solution takes into account not only the traveled distance, but also the radio coverage experience. To this aim, several radio-related cost-weighting functions are introduced and tested, to assess the performance of the proposed approaches with extensive simulations. The proposed mapping algorithm can achieve a mapping error probability below $2\%$, while the proposed path-planning algorithms extend the radio coverage of the AVs, with only a limited increase in traveled distance with respect to shortest-path existing methods, such as conventional Dijkstra and A* algorithms.