Modelling activation of congestion control for estimating channel load in vehicular networks

In this paper, we present a Markov chain based model that can be used for easy estimation of the Vehicle to Vehicle (V2V) message generation rates in a highway environment based on just the traffic flow rate and the average vehicle speed on the highway. This allows for a faster estimation of the overall channel load than using a simulation environment to do the same. Our model considers the effects of Decentralized Congestion Control based on Transmit Rate Control (DCC-TRC) on Cooperative Awareness Message (CAM) generations. The model is evaluated by comparing the results obtained with the message generation rates achieved for a highway traffic scenario in a simulation environment based on Artery and SUMO. Comparing the results shows that the Cumulative Distributive Function (CDF) of the message generation rates estimated by our model is fairly accurate as they fall within the 95% confidence interval of the CDF obtained from the simulation

Modelling the packet delivery of V2V messages based on the macroscopic traffic parameters

In this paper, we present an analytical model for estimating the Packet Delivery Ratio (PDR) in vehicular communication using IEEE 802.11p protocol for a highway scenario based on the macroscopic traffic parameters. We also consider the effects of Decentralised Congestion Control (DCC) based on Transmit Rate in improving the PDR. The model is validated using a simulation environment based on Artery and its estimation of the PDR is found to be within 1 % deviation of the PDR calculated from the simulation environment. We also show a use case of our model in identifying the correct DCC parameters at which the DCC will be activated based on the macroscopic traffic parameters

Modelling V2V message generation rates in a highway environment

In this paper, we investigate if an analytical model can be used to estimate the load on the vehicular communication channel. We design a queueing model for estimating the probability distribution of Cooperative Awareness Message generation rates in a highway environment. The results are compared with a real world vehicular traffic trace on a highway and also with a vehicular communication simulator using Veins and SUMO for more complicated traffic scenarios involving acceleration and deceleration ramps. Comparing the results shows the probability distribution of the message generation rates predicted by our model to be within the 95% confidence interval of the distribution obtained from the traffic trace and the simulation

Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment

Most of the research in automated driving currently involves using the on-board sensors on the vehicle to collect information regarding surrounding vehicles to maneuver around them. In this paper we discuss how information communicated through vehicular networking can be used for controlling an autonomous vehicle in a multi-lane highway environment. A driving algorithm is designed using deep Q learning, a type of reinforcement learning. In order to train and test driving algorithms, we deploy a simulated traffic system, using SUMO (Simulation of Urban Mobility). The performance of the driving algorithm is tested for perfect knowledge regarding surrounding vehicles. Furthermore, the impact of limited communication range and random packet loss is investigated. Currently the performance of the driving algorithm is far from ideal with the collision ratios being quite high. We propose directions for additional research to improve the performance of the algorithm