Model-based Decision Making with Imagination for Autonomous Parking
This addresses parking challenges for autonomous driving systems, representing an incremental improvement with specific gains in speed and performance.
The paper tackles autonomous parking by proposing an imaginative algorithm that combines an anticipation model, an improved RRT for trajectory planning, and a path smoother, resulting in a tenfold speed increase and better stability and efficiency compared to traditional RRT in three parking scenarios.
Autonomous parking technology is a key concept within autonomous driving research. This paper will propose an imaginative autonomous parking algorithm to solve issues concerned with parking. The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) for planning a feasible trajectory from a given start point to a parking lot, and a path smoothing module for optimizing the efficiency of parking tasks. Our algorithm is based on a real kinematic vehicle model; which makes it more suitable for algorithm application on real autonomous cars. Furthermore, due to the introduction of the imagination mechanism, the processing speed of our algorithm is ten times faster than that of traditional methods, permitting the realization of real-time planning simultaneously. In order to evaluate the algorithm's effectiveness, we have compared our algorithm with traditional RRT, within three different parking scenarios. Ultimately, results show that our algorithm is more stable than traditional RRT and performs better in terms of efficiency and quality.