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Vehicle autonomy has the potential to bring many social benefits, such as improved traffic safety and increased productivity. Modern autonomous vehicles are able to sense their local environment, recognise relevant objects, and make driving decisions that obey traffic rules. Nevertheless, many situations encountered during daily driving continue to be challenging for autonomous vehicles, holding back the commercial deployment of autonomous driving technology. In particular, motion planning in environments that involve interactions with human drivers requires the design of algorithms that can reason about the uncertain motion of other vehicles while relying on noisy and incomplete sensor measurements. Given the stochasticity in human driving behaviour and sensor limitations, effective handling of uncertainty is of paramount importance for ensuring system safety and robustness. This thesis makes several contributions towards enabling self-driving vehicles to reason about the uncertain behaviour of other drivers and utilise this reasoning capability for planning. As our use case, we focus on the complex task of merging into moving traffic where uncertainty can emanate from the behaviour of other drivers and imperfect sensor measurements. We exploit the power of deep neural networks in learning complex correlations from data for developing driver behaviour models. We use these models for planning on two levels of abstraction: high-level, discrete decisions that help the autonomous vehicle reach its destination safely and in a timely manner, and low-level continuous actions that directly influence the vehicle’s dynamics. For high-level …