Performance Modelling of V2V based Collective Perceptions in Connected and Autonomous Vehicles

With the introduction of Connected and Autonomous Vehicles (CAVs), it is possible to extend the limited horizon of vehicles on the road by collective perceptions, where vehicles periodically share their sensory information with others using Vehicle-2-Vehicle (V2V) communications. This technique relies on a certain number of participants to have a measurable advantage. Nevertheless, the spread of CAVs will take a considerable period of time, it is critical to understand the benefits and limits of V2V based collective perceptions in different market stages. In this work, we characterise the effective Field of View (eFoV) of a vehicle as the perception range using local sensors only, and the collective Field of View (cFoV) as the region learn from the network. Applying analytic and simulation studies in highway scenarios, we show that the eFoV drops quickly with the increase in traffic density due to blockage effects of surrounding vehicles, and it is insufficient to overcome this problem by increasing the sensing range of local sensors. On the other hand, vehicles can gain around 16 folds more information about the road environment by leveraging collective perceptions with only 10% CAV penetration rate. When the penetration rate reaches to around 30%, collective perceptions can provide 95% coverage over the road environments. Our analyses also show that apart from the benefits, employing collective perceptions could result in heavy broadcast redundancy, hence wasting the already scarce network resources. This observation suggests that the sharing of sensory information should be controlled appropriately to avoid overloading the communication networks.