Radio-Coverage-Aware Path Planning for Cooperative Autonomous Vehicles
For autonomous vehicle fleets requiring wireless connectivity, this work addresses the practical problem of balancing travel distance with radio coverage quality, though the contribution is incremental.
This paper proposes modifying Dijkstra and A* path planning algorithms for autonomous vehicle fleets to account for radio coverage quality, achieving a mapping error probability below 2% and improved radio coverage with only a limited increase in traveled distance compared to shortest-path methods.
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.