Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
This addresses the challenge of dynamic environment perception for autonomous vehicles, though it appears incremental as it builds on existing segmentation and mapping approaches.
The paper tackles the problem of spatio-temporal segmentation for construction zones in self-driving by introducing a non-parametric memory representation that remembers, reinforces, and forgets past beliefs based on new evidence, enabling online detection of changes to complement static HD maps.
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV). Our representation has three important properties: (i) it remembers what it has seen in the past, (ii) it reinforces and (iii) forgets its past beliefs based on new evidence. Reinforcing is important as the first time we see an element we might be uncertain, e.g, if the element is heavily occluded or at range. Forgetting is desirable, as otherwise false positives will make the self driving vehicle behave erratically. Our process is informed by 3D reasoning, as occlusion is key to distinguishing between the desire to forget and to remember. We show how our method can be used as an online component to complement static world representations such as HD maps by detecting and remembering changes that should be superimposed on top of this static view due to such events.