  Developing safe automated vehicles that can be proactive, safe, and
comfortable in mixed traffic requires improved planning methods that are
risk-averse and that account for predictions of the motion of other road users.
To consider these criteria, in this article, we propose a non-linear
model-predictive trajectory generator scheme, which couples the longitudinal
and lateral motion of the vehicle to steer the vehicle with minimal risk, while
progressing towards the goal state. The proposed method takes into account the
infrastructure, surrounding objects, and predictions of the objects' state
through artificial potential-based risk fields included in the cost function of
the model-predictive control (MPC) problem. This trajectory generator enables
anticipatory maneuvers, i.e., mitigating risk far before any safety-critical
intervention would be necessary. The method is proven in several case studies
representing both highways- and urban situations. The results show the safe and
efficient implementation of the MPC trajectory generator while proving its
real-time applicability.
