Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach
It provides an architectural approach to trajectory planning for autonomous driving researchers, though the contribution appears incremental as it applies existing MPC methods to a known problem.
The paper proposes a model predictive control-based trajectory generation framework for autonomous vehicles that is general-purpose and can handle system failures, addressing the lack of versatile planners in the field.
Research in the field of automated driving has created promising results in the last years. Some research groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. Yet, what is often missing are general-purpose path- or trajectory planners which are not designed for a specific purpose. In this paper we look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks can contribute to generalized path- and trajectory generation approaches for generating safe trajectories even in cases of system failures.