2 research outputs found
Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks
The recent advancements in communication and computational systems has led to
significant improvement of situational awareness in connected and autonomous
vehicles. Computationally efficient neural networks and high speed wireless
vehicular networks have been some of the main contributors to this improvement.
However, scalability and reliability issues caused by inherent limitations of
sensory and communication systems are still challenging problems. In this
paper, we aim to mitigate the effects of these limitations by introducing the
concept of feature sharing for cooperative object detection (FS-COD). In our
proposed approach, a better understanding of the environment is achieved by
sharing partially processed data between cooperative vehicles while maintaining
a balance between computation and communication load. This approach is
different from current methods of map sharing, or sharing of raw data which are
not scalable. The performance of the proposed approach is verified through
experiments on Volony dataset. It is shown that the proposed approach has
significant performance superiority over the conventional single-vehicle object
detection approaches.Comment: 7 pages, 6 figure
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
Collaborative object localization aims to collaboratively estimate locations
of objects observed from multiple views or perspectives, which is a critical
ability for multi-agent systems such as connected vehicles. To enable
collaborative localization, several model-based state estimation and
learning-based localization methods have been developed. Given their
encouraging performance, model-based state estimation often lacks the ability
to model the complex relationships among multiple objects, while learning-based
methods are typically not able to fuse the observations from an arbitrary
number of views and cannot well model uncertainty. In this paper, we introduce
a novel spatiotemporal graph filter approach that integrates graph learning and
model-based estimation to perform multi-view sensor fusion for collaborative
object localization. Our approach models complex object relationships using a
new spatiotemporal graph representation and fuses multi-view observations in a
Bayesian fashion to improve location estimation under uncertainty. We evaluate
our approach in the applications of connected autonomous driving and multiple
pedestrian localization. Experimental results show that our approach
outperforms previous techniques and achieves the state-of-the-art performance
on collaboration localization.Comment: Revise several typos and change the Fig2 to be more illustrativ