Platooning strategy is an important component of autonomous driving technology. Autonomous vehicles in platoons are often equipped with a variety of on-board sensors to detect the surrounding environment. The abundant data collected by autonomous vehicles in platoons can be transmitted to the infrastructure through vehicle-to-infrastructure (V2I) communications using the IEEE 802.11 distributed coordination function (DCF) mechanism and then uploaded to the cloud platform through the Internet. The cloud platform extracts useful information and then sends it back to the autonomous vehicles respectively. In this way, autonomous vehicles in platoons can detect emergency conditions and make a decision in time. The characteristics of platoons would cause a fair-access problem in the V2I communications, i.e., vehicles in the platoons moving on different lanes with different velocities would have different resident time within the infrastructure’s coverage and thus successfully send different amounts of data to the infrastructure. In this case, the vehicles with different velocities will receive different amounts of useful information from the cloud. As a result, vehicles with a higher velocity are more likely to suffer from a traffic accident as compared to the vehicles with a lower velocity. Hence, this paper considers the fair-access problem and proposes a fair-access scheme to ensure that vehicles with different velocities successfully transmit the same amount of data by adaptively adjusting the minimum contention window of each vehicle according to its velocity. Moreover, the normalized throughput of the proposed scheme is derived. The validity of the fair-access scheme is demonstrated by simulation.

With the rapid development of cloud computing and big data, traditional Vehicular Ad hoc Networks (VANETs) are evolving into the Internet of Vehicles (IoV). As an important communication technology in IoV, IEEE 802.11p protocols have been studied by many experts and scholars. In IEEE 802.11p, a node’s backoff counter will be frozen when the channel is detected as busy. However, most studies did not consider the possibility of continuous backoff freezing when calculating delay. Thus, in this paper, we focus on the performance analysis of IEEE 802.11p for continuous backoff freezing. Specifically, we establish an analytical model to analyze the broadcast performance in the highway scene where vehicles can obtain traffic density from roadside units through Vehicle to Infrastructure (V2I) communications. We first calculate the relationship between vehicle density and the number of vehicles. Then, we derive the relationship between the number of vehicles and packet delay according to Markov chains. Next, we utilize the probability generating function (PGF) to transform traditional Markov chains into z domain under the situation of non-saturation. Finally, we employ the Mason formula to derive packet delay. As compared with the performance without considering the continuous backoff freezing, the simulation results have demonstrated that our analytical model is more reasonable.