Multi-Vehicle Path Planning using Shared Data

Abstract

Vehicle navigation is a problem without a real solution. Every action taken can be disturbed by an accident, making the suggested path not optimal. As a result, the only moment it is possible to state that the selected road was optimal is after the vehicle has completed it and all possible disturbing events have occurred. Due to the high impact of the navigation system on traffic flow, this research suggests sharing information between vehicles to make decisions as a team, giving way for those who benefit most. It also allows them to renavigate vehicles if they face traffic jams or congestion on the road. The newly developed algorithm proved to be useful, overperforming other common path-finding strategies by 10-15% and providing results 5% close to the optimal path ll possible disturbing events have occurred. Traffic flows after the developed algorithm also provided a lower traffic jam rate compared to other algorithms. Algorithms were tested using the simulation developed for this research.