In recent years, autonomous vehicles (AVs) have been receiving increasing
attention from investors, automakers, and academia due to the envisioned
potentials of AVs in enhancing safety, reducing emissions, and improving
comfort. The crucial task in AV development boils down to perception and
navigation. The research is underway, in both academia and industry, to improve
AV’s perception and navigation and reduce the underlying computation and costs.
This article proposes a model predictive control (MPC)-based local path-planning
method in the Cartesian framework to overcome the long computation time and lack
of smoothness of the Frenet method. A new equation is proposed in the MPC cost
function to improve the safety in path planning. In this regard, an AV is built
based on a 2015 Nissan Leaf S by modifying the drive-by-wire function and
installing environment perception sensors and computation units. The custom-made
AV then collected data in Norman, Oklahoma, and assisted in the performance
evaluation of the two control algorithms in this work. Both straight roads and
curved roads are considered in the evaluation. For the purpose of saving costs
and raising real-world implementation potential, the vision-only solution is
applied in object detection and bird’s-eye-view coordinate data generation. MPC
and Frenet coordinate system approaches are independently employed to generate a
safe and smooth path for the AV using the collected data. The two methods are
compared in terms of smoothness, safety, and computation time. Compared with the
Frenet-based method, the proposed MPC method reduces the computation time by
80%, and the path smoothness is significantly improved.
