The current road transport system has problems with both safety and efficiency. Future intelligent transportation systems (ITS) are envisioned to alleviate these problems. In particular, cooperative ITS, where vehicles are connected to each other and the cloud, will allow vehicles to collaborate and share both sensor and control information. This will significantly expand the possibilities of optimizing traffic flow and increasing safety. However, as both communication and sensing are unreliable, a key challenge in cooperative ITS is how to accommodate for communication and sensing impairments. This requires an understanding of what the limitations of communication and sensing systems are, and how their uncertainties affect the control and coordination task. The contribution of this thesis lies on the intersection of the fields of communication, sensing, and control, and can be summarized as follows.

First of all, through the use of stochastic geometry, we analyze the impact of interference in vehicular networks, and propose a general procedure to analytically determine key performance metrics such as packet reception probabilities and throughput. Along with this procedure, we provide a model repository that can be used to adapt to both rural and urban propagation characteristics, and different medium access control protocols. The procedure can be used to gain fundamental insights about the performance of vehicular communication systems in a variety of scenarios of practical relevance.

Secondly, when it comes to sensing uncertainties, we use Fisher information theory to provide bounds on the achievable performance of cooperative positioning solutions. We thereby characterize how the composition of the vehicle fleet, and the penetration rate of vehicles with extensive sensing capabilities affects positioning and mapping performance. While the analysis is generally applicable, we present simulation results from a multi-lane freeway scenario, which indicate that introducing a small fraction of cooperating vehicles with high-end sensors significantly improves the positioning quality of the entire fleet, but may not be enough to meet the stringent demands posed by safety-critical applications.

Finally, we study how communication and sensing uncertainties impact cooperative intersection coordination. We show that the requirements on control, communication and sensing are stringent if they are treated separately and that they could be relaxed if the individual systems are made aware of each other. This awareness is explored in two ways: we provide a communication system analysis for a centralized intersection coordination scheme using stochastic geometry, which can be used to provide guidelines on how to design the communication system to guarantee a control-dependent communication quality. We also propose a collision aware resource allocation strategy, which proactively reduces channel congestion by only assigning communication resources to vehicles that are in critical configurations, i.e., when there is a risk for future collisions.

This thesis, through the use of several mathematical tools, thus sheds new insights into the communication, sensing and control performance of cooperative ITS.