Kavli Affiliate: Yi Zhou
| First 5 Authors: Saeed Saadatnejad, Yi Zhou Ju, Alexandre Alahi, ,
| Summary:
Safety is still the main issue of autonomous driving, and in order to be
globally deployed, they need to predict pedestrians’ motions sufficiently in
advance. While there is a lot of research on coarse-grained (human center
prediction) and fine-grained predictions (human body keypoints prediction), we
focus on 3D bounding boxes, which are reasonable estimates of humans without
modeling complex motion details for autonomous vehicles. This gives the
flexibility to predict in longer horizons in real-world settings. We suggest
this new problem and present a simple yet effective model for pedestrians’ 3D
bounding box prediction. This method follows an encoder-decoder architecture
based on recurrent neural networks, and our experiments show its effectiveness
in both the synthetic (JTA) and real-world (NuScenes) datasets. The learned
representation has useful information to enhance the performance of other
tasks, such as action anticipation. Our code is available online:
https://github.com/vita-epfl/bounding-box-prediction
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