Advances in vehicular communication technologies have the potential to facilitate autonomous and cooperative driving in the near future. Connectivity technologies such as Vehicle-to-Cloud (V2C) and Vehicle-to-Vehicle (V2V) communication enhance the vehicles’ awareness and enable cooperation. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans.

This thesis presents motion planning algorithms, control strategies and estimation techniques that incorporate the information received via connectivity technologies such as V2C and V2V communication to improve the safety and performance of a single autonomous vehicle or multiple coordinated autonomous vehicles.

Starting with a single vehicle case, the design of estimation, control and planning strategies which utilize V2C connectivity are presented. It is shown the vehicle localization performance is improved by exploiting prior knowledge of a road grade map built using V2C communication. Also, the design of a hierarchical control system including high-level long-term planner and low-level real-time control is presented that employs the traffic condition and road grade data using V2C connectivity.

For the two-vehicle case, the design of a leader-follower coordination using V2V communication is proposed. A safe adaptive cruise control system is presented in which the ego (follower) vehicle receives the predicted trajectory of the front (leader) vehicle. Safety is achieved by designing a robust control invariant set. The set computation is suitable for online applications and is less conservative compared to the state of the art.

For the case of multiple vehicles, the design of safe and efficient coordination algorithms which benefit from V2V communication technologies is presented. The coordination strategies are classified in the two categories of centralized and distributed. The proposed methods are general and applicable to various multi-robot settings. By using the theory of Model Predictive Control (MPC) and strong duality, provably safe algorithms are presented for collaborative navigation of multiple heterogeneous autonomous vehicles with different (arbitrary) polytopic shapes and different dynamical models in tight environments. The optimization-based centralized coordination strategies are presented for formation, reconfiguration and autonomous navigation of CAVs, traveling on public roads. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment.

In the last part of the thesis, a distributed coordination algorithm is presented that exploits the problem structure to decompose the large optimization problem into smaller local sub-problems solved in parallel. Using this approach, the vehicles cooperate (while communicating their intentions to the neighbors) and compute collision-free paths in a distributed way to navigate in tight environments in real-time.