Development, Analysis, and Real-life Benchmarking of RRT-based Path Planning Algorithms for Automated Valet Parking

One of the major application areas of highly automated vehicles is the problem of Automated Valet Parking (AVP). In this work, we analyze solutions and compare performances of RRT (rapidly exploring random tree) based approaches in the context of the AVP problem, which can also be applied in a more general low-speed autonomy context. We present comparison results using both simulation and real-life experiments on a representative parking use case. The results indicate better suitability of RRTx and RRV for utilization in typical AVP scenarios. The main contributions of this work lie in real-life experimental validation and comparisons of RRT approaches for use in low-speed autonomy.

Automated summary

This work analyzes and compares RRT-based approaches for Automated Valet Parking (AVP) and low-speed autonomy, presenting results from simulation and real-life experiments. The findings suggest that RRTx and RRV are better suited for typical AVP scenarios, contributing to the validation and comparison of RRT methods for low-speed autonomy.