Situation-Aware Drivable Space Estimation for Automated Driving

An automated vehicle (AV) must always have a correct representation of the drivable space to position itself accurately and operate safely. To determine the drivable space, current research focuses on single sources of information, either using pre-computed high-definition maps, or mapping the environment online with sensors such as LiDARs or cameras. However, each of these information sources can fail, some are too costly, and maps could be outdated.
In this work a new method for situation-aware drivable space (SDS) estimation combining multiple information sources is proposed, which is also suitable for AVs equipped with inexpensive sensors. Depending on the situation, semantic information of sensed objects is combined with domain knowledge to estimate the drivability of the space surrounding each object (e.g. traffic light, another vehicle). These estimates are modeled as probabilistic graphs to account for the uncertainty of information sources, and an optimal spatial configuration of their elements is determined via graph-based simultaneous localization and mapping (SLAM).
To investigate the robustness of SDS towards potentially unreliable sensors and maps, it has been tested in a simulation environment and real world data. Results on different use cases (e.g. straight roads, curved roads, and intersections) show considerable robustness towards unreliable inputs, and the recovered drivable space allows for accurate in-lane localization of the AV even in extreme cases where no prior knowledge of the road network is available.