Abstract
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.
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