Towards A General-Purpose Motion Planning for Autonomous Vehicles Using Fluid Dynamics

TL;DR

This paper introduces a fluid dynamics-inspired general-purpose motion planning method for autonomous vehicles, leveraging the lattice Boltzmann method to generate feasible trajectories that consider vehicle dynamics, safety, and efficiency across diverse driving scenarios.

Contribution

It presents a novel, explainable motion planning approach using fluid mechanics principles and the lattice Boltzmann method, addressing multiple challenges in autonomous vehicle trajectory generation.

Findings

Outperforms traditional MPC-based motion planning in simulations

Efficiently generates feasible trajectories considering vehicle dynamics

Applicable across diverse driving scenarios such as highway, merging, and intersections

Abstract

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).