An Edge Computing Approach for Autonomous Vehicle Platooning
Abstract
One of the recent advancements in Internet of Things (IoT) is connected vehicles (CV), where each vehicle can connect to the things or vehicles nearby using wireless networks such as Dedicated Short Range Communication (DSRC). This gives rise to many vehicular applications such as platooning. A group of vehicles can negotiate and drive jointly close to each other in a cooperative manner to form a platoon. Using connected and automated driving systems, platooning can aid in cutting total fuel costs, reducing CO$$_{2}$$2 emissions, improving efficiency, decreasing traffic congestion, increasing safety, and providing comfort for the drivers. The existing work on platooning assumes sensors besides DSRC sensors which may not be reliable in extremely poor weather conditions. In this work, we propose DSRC only based platoon negotiation where each vehicle is an edge node. A vehicle that is interested in platooning can broadcast a DSRC message and interesting vehicles can establish communication to negotiate their route. Then, in a series of transactions over the DSRC channel they can agree upon the leader position as well as the follower position(s). We employ the relative position estimation technique we proposed in prior work. The host vehicle (HV) computes the relative angle with the remote vehicle (RV) to accurately estimate its relative position based on Basic Safety Messages (BSMs) only. In this paper, we propose a new platoon negotiation algorithm based only on DSRC communication messages that works well in any weather condition. To test this, we extend CARLA, an autonomous driving simulator to support IoT connectivity as well as platooning. We implemented DSRC agent class for connectivity that can be instantiated for every vehicle in the simulation. The proposed edge node negotiation algorithms include platoon-ready, pre-negotiation, negotiation resolver, and platoon member algorithms. We experimented with a two-vehicle case scenario in the platoon negotiation phase and validated the algorithms in simulation.