This paper presents a learning-based model predictive
trajectory planning controller for automated driving in unstructured, dynamic environments with obstacle avoidance. We first
address the problem of lacking prior knowledge in unstructured
environments by introducing a risk map that maps the density
and motion of obstacles and the road to an occupancy risk. Model
predictive control is then used to integrate trajectory planning and
tracking control into one framework to bridge the gap between
planning and control. Meanwhile, we use Gaussian Process (GP)
regression to learn the residual model uncertainty for improving
the model accuracy. An objective function considering both risks
within the feasible region and vehicle dynamics is carefully formulated to obtain collision-free and kinematically-feasible local
trajectories. Field experiments are performed on real unstructured environments with our automated vehicle. Experimental
results demonstrate the effectiveness of the proposed algorithm
for successful obstacle avoidance in various complex unstructured
scenarios.