Adaptive Feature Fusion for Cooperative Perception using LiDAR Point
Clouds
- URL: http://arxiv.org/abs/2208.00116v1
- Date: Sat, 30 Jul 2022 01:53:05 GMT
- Title: Adaptive Feature Fusion for Cooperative Perception using LiDAR Point
Clouds
- Authors: D. Qiao and F. Zulkernine
- Abstract summary: Cooperative perception allows a Connected Autonomous Vehicle to interact with the other CAVs in the vicinity.
It can compensate for the limitations of the conventional vehicular perception such as blind spots, low resolution, and weather effects.
We evaluate the performance of cooperative perception for both vehicle and pedestrian detection using the CODD dataset.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cooperative perception allows a Connected Autonomous Vehicle (CAV) to
interact with the other CAVs in the vicinity to enhance perception of
surrounding objects to increase safety and reliability. It can compensate for
the limitations of the conventional vehicular perception such as blind spots,
low resolution, and weather effects. An effective feature fusion model for the
intermediate fusion methods of cooperative perception can improve feature
selection and information aggregation to further enhance the perception
accuracy. We propose adaptive feature fusion models with trainable feature
selection modules. One of our proposed models Spatial-wise Adaptive feature
Fusion (S-AdaFusion) outperforms all other state-of-the-art models on the two
subsets of OPV2V dataset: default CARLA towns for vehicle detection and the
Culver City for domain adaptation. In addition, previous studies have only
tested cooperative perception for vehicle detection. A pedestrian, however, is
much more likely to be seriously injured in a traffic accident. We evaluate the
performance of cooperative perception for both vehicle and pedestrian detection
using the CODD dataset. Our architecture achieves higher Average Precision (AP)
than other existing models for both vehicle and pedestrian detection on the
CODD dataset. The experiments demonstrate that cooperative perception also can
improve the pedestrian detection accuracy compared to the conventional
perception process.
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