Packet Inter-Reception Time Conditional Density Estimation Based on Surrounding Traffic Distribution

• Published in: IEEE open journal of intelligent transportation systems Vol. 1; pp. 51 - 62
• Main Authors: Jornod, Guillaume, Assaad, Ahmad El, Kurner, Thomas
• Format: Journal Article
• Language: English
• Published: New York IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Communications systems; cooperative systems; Data models; Density; Environment models; Estimation; intelligent vehicles; Messages; Multilayers; Platooning; prediction algorithms; Prediction models; Predictive models; Probability distribution functions; Quality of service; Roads; Trucks; Vehicular ad hoc networks
• ISSN: 2687-7813; 2687-7813
• DOI: 10.1109/OJITS.2020.2995304

Cooperation is an enabler for autonomous vehicles. A promising application of cooperative driving is high-density platooning, where trucks drive with low inter-vehicle distances. It aims at increasing the road and fuel efficiency whilst guaranteeing safety. The safe and efficient coordination of the control requires the regular and reliable exchange of V2V messages. The performance of the vehicular application has been shown to be strongly affected by the variation of the performances of the communications system. To be able to adapt their functional settings to these variations, vehicles need the ability to predict it. We present a prediction model for the packet inter-reception time platoon messages in an IEEE 802.11p network. This performance indicator is the subject of extensive research as it captures the irregularity of input for the control loop. The prediction model uses conditional density estimation based on the exponential distribution. We fit this model using a multi-layer perceptron regressor based on features representing the surrounding communication environment. The presented results are based on data collected during a full scale platooning simulations using ns-3 and SUMO. We compare different environment abstraction models and show the potential of on-line learning.