Driving is a very challenging task to automatize despite how naturally and efficiently it may come to experienced human drivers. The complexity stems from the need to (i) understand the surrounding context and forecast how it is likely to evolve, (ii) plan motions so that maneuvers can be performed with a certain level of anticipation despite the uncertainty of the future traffic state, and (iii) act on the throttle and the steering wheel to execute the planned motions accurately. These tasks match the major research topics concerning autonomous driving, namely perception and prediction, motion planning, and control. In this thesis, we study challenges related to motion planning and decision-making for connected automated vehicles (CAVs) in mixed traffic. That is for CAVs that coexist with human drivers, other CAVs, and unconnected automated vehicles (AVs). Even though we intend to formulate the proposed methods so that they are context agnostic, their assessment is carried out in roundabout scenarios. Roundabouts are ideal testing scenarios due to the complexity of the traffic interaction and overall traffic dynamics, the impact that uncertainty has on the coordination performance, as well as the strong influence that dynamic occlusions of the surroundings caused by nearby vehicles have on the decision-making process. We propose a novel approach concerning how an AV's surrounding space is represented and described, which brings benefits to the motion planning module. Unlike the classical planning approach based on object detection and avoidance, we study an alternative strategy based on free space identification and exploitation, which is shown to be a suitable mechanism to account for occlusions and other perception uncertainties. Our planning solutions are model-based and--inspired on the way human drivers seem to make decisions--aim to make safe yet efficient decisions without the need to explicitly explore all possible trajectories that can be followed. Instead, we propose a low-dimensional driving maneuver representation that enables us to characterize the solution-space of the decision-making problem at a high-level. In particular, a novel planning framework is presented in this thesis to address four significant planning aspects. Firstly, a reactive gap-acceptance behavior is formulated, which represents an appropriate baseline behavior despite its simplicity. Afterward, we investigate a decision-making approach for CAVs in fully connected scenarios, whereby CAVs would consider the impact of their decisions on the overall traffic before executing them. Then, we address the challenge of making AVs cooperate with other unconnected vehicles through a so-called implicitly cooperative mechanism. Furthermore, we present a predictive-reactive planning strategy where the challenge of planning motions taking into account longer traffic predictions, and the possibility of them being wrong is tackled. Finally, the suitability of some of the proposed theoretical results is assessed in a more realistic setup, where the methods are applied to real data provided by our industrial partners. This dissertation provides new ideas and methods to address the complexity of motion planning in mixed traffic. Specifically, we tackle the problem through a versatile motion planning framework and a set of pragmatic model-based decision-making strategies, paving the way towards feasible, efficient, and more reliable solutions.