  This paper presents a continuous-time optimal control framework for the
generation of reference trajectories in driving scenarios with uncertainty. A
previous work presented a discrete-time stochastic generator for autonomous
vehicles; those results are extended to continuous time to ensure the
robustness of the generator in a real-time setting. We show that the stochastic
model in continuous time can capture the uncertainty of information by
producing better results, limiting the risk of violating the problem's
constraints compared to a discrete approach. Dynamic solvers provide faster
computation and the continuous-time model is more robust to a wider variety of
driving scenarios than the discrete-time model, as it can handle further time
horizons, which allows trajectory planning outside the framework of urban
driving scenarios.
