  General-purpose motion planners for automated/autonomous vehicles promise to
handle the task of motion planning (including tactical decision-making and
trajectory generation) for various automated driving functions (ADF) in a
diverse range of operational design domains (ODDs). The challenges of designing
a general-purpose motion planner arise from several factors: a) A plethora of
scenarios with different semantic information in each driving scene should be
addressed, b) a strong coupling between long-term decision-making and
short-term trajectory generation shall be taken into account, c) the
nonholonomic constraints of the vehicle dynamics must be considered, and d) the
motion planner must be computationally efficient to run in real-time. The
existing methods in the literature are either limited to specific scenarios
(logic-based) or are data-driven (learning-based) and therefore lack
explainability, which is important for safety-critical automated driving
systems (ADS). This paper proposes a novel general-purpose motion planning
solution for ADS inspired by the theory of fluid mechanics. A computationally
efficient technique, i.e., the lattice Boltzmann method, is then adopted to
generate a spatiotemporal vector field, which in accordance with the
nonholonomic dynamic model of the Ego vehicle is employed to generate feasible
candidate trajectories. The trajectory optimising ride quality, efficiency and
safety is finally selected to calculate the imminent control signals, i.e.,
throttle/brake and steering angle. The performance of the proposed approach is
evaluated by simulations in highway driving, on-ramp merging, and intersection
crossing scenarios, and it is found to outperform traditional motion planning
solutions based on model predictive control (MPC).
