  Dynamic obstacle avoidance (DOA) is a fundamental challenge for any
autonomous vehicle, independent of whether it operates in sea, air, or land.
This paper proposes a two-step architecture for handling DOA tasks by combining
supervised and reinforcement learning (RL). In the first step, we introduce a
data-driven approach to estimate the collision risk of an obstacle using a
recurrent neural network, which is trained in a supervised fashion and offers
robustness to non-linear obstacle movements. In the second step, we include
these collision risk estimates into the observation space of an RL agent to
increase its situational awareness.~We illustrate the power of our two-step
approach by training different RL agents in a challenging environment that
requires to navigate amid multiple obstacles. The non-linear movements of
obstacles are exemplarily modeled based on stochastic processes and periodic
patterns, although our architecture is suitable for any obstacle dynamics. The
experiments reveal that integrating our collision risk metrics into the
observation space doubles the performance in terms of reward, which is
equivalent to halving the number of collisions in the considered environment.
Furthermore, we show that the architecture's performance improvement is
independent of the applied RL algorithm.
