Safe and reliable navigation in crowded unstructured pedestrian areas

• A navigation system has been developed for an autonomous vehicle.
• The autonomous vehicle can navigate along unstructured and crowded environments.
• Two planning levels are used, considering two approaches for the first one.
• The system has been successfully tested in real conditions, results shown.

In this paper, the navigation system of the autonomous vehicle prototype Verdino is introduced. Two navigation levels are considered. In the first level, a trajectory is generated from the current position toward a goal that considers two different approaches. In the first, the minimum cost path is obtained using a classical approach (used for regular navigation). The second approach is a little more complex, relying on a set of precomputed primitives representing the motion model of the vehicle, which are used as part of an ARA⁎ algorithm in order to find the best trajectory. This trajectory consists of both forward and backward motion segments for complex maneuvers. In the second level, a local planner is in charge of computing the commands sent to the vehicle in order to follow the trajectory. A set of tentative local trajectories is computed in the Frenét space and scored using several factors, described in this paper. Some results for the two navigation levels are shown at the end of this document. For the global planner, several examples of the maneuvers obtained are shown and certain related factors are quantified and compared. As for the local planner, a study on the influence of the defined weights on the vehicle׳s final behavior is presented. Also, from these tests several configurations have been chosen and ranked according to two different proposed behaviors. The navigation system shown has been tested both in simulated and in real conditions, and the attached video shows the vehicle׳s real-world performance.

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