MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for
Connected Automated Driving
- URL: http://arxiv.org/abs/2302.13029v1
- Date: Sat, 25 Feb 2023 09:03:05 GMT
- Title: MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for
Connected Automated Driving
- Authors: Yukuan Jia, Ruiqing Mao, Yuxuan Sun, Sheng Zhou, and Zhisheng Niu
- Abstract summary: A new paradigm, Cooperative Perception (CP), comes to the rescue by sharing sensor data from a cooperative vehicle (CoV)
Existing methods rely on the exchange of meta-information, such as visibility maps, to predict the perception gains from nearby vehicles.
We propose a new approach, learning while scheduling, for distributed scheduling of CP.
The proposed MASS algorithm achieves the best average perception gain and improves recall by up to 4.2 percentage points compared to other learning-based algorithms.
- Score: 19.66714697653504
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Timely and reliable environment perception is fundamental to safe and
efficient automated driving. However, the perception of standalone intelligence
inevitably suffers from occlusions. A new paradigm, Cooperative Perception
(CP), comes to the rescue by sharing sensor data from another perspective,
i.e., from a cooperative vehicle (CoV). Due to the limited communication
bandwidth, it is essential to schedule the most beneficial CoV, considering
both the viewpoints and communication quality. Existing methods rely on the
exchange of meta-information, such as visibility maps, to predict the
perception gains from nearby vehicles, which induces extra communication and
processing overhead. In this paper, we propose a new approach, learning while
scheduling, for distributed scheduling of CP. The solution enables CoVs to
predict the perception gains using past observations, leveraging the temporal
continuity of perception gains. Specifically, we design a mobility-aware sensor
scheduling (MASS) algorithm based on the restless multi-armed bandit (RMAB)
theory to maximize the expected average perception gain. An upper bound on the
expected average learning regret is proved, which matches the lower bound of
any online algorithm up to a logarithmic factor. Extensive simulations are
carried out on realistic traffic traces. The results show that the proposed
MASS algorithm achieves the best average perception gain and improves recall by
up to 4.2 percentage points compared to other learning-based algorithms.
Finally, a case study on a trace of LiDAR frames qualitatively demonstrates the
superiority of adaptive exploration, the key element of the MASS algorithm.
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