  Sharing and joint processing of camera feeds and sensor measurements, known
as Cooperative Perception (CP), has emerged as a new technique to achieve
higher perception qualities. CP can enhance the safety of Autonomous Vehicles
(AVs) where their individual visual perception quality is compromised by
adverse weather conditions (haze as foggy weather), low illumination, winding
roads, and crowded traffic. To cover the limitations of former methods, in this
paper, we propose a novel approach to realize an optimized CP under constrained
communications. At the core of our approach is recruiting the best helper from
the available list of front vehicles to augment the visual range and enhance
the Object Detection (OD) accuracy of the ego vehicle. In this two-step
process, we first select the helper vehicles that contribute the most to CP
based on their visual range and lowest motion blur. Next, we implement a radio
block optimization among the candidate vehicles to further improve
communication efficiency. We specifically focus on pedestrian detection as an
exemplary scenario. To validate our approach, we used the CARLA simulator to
create a dataset of annotated videos for different driving scenarios where
pedestrian detection is challenging for an AV with compromised vision. Our
results demonstrate the efficacy of our two-step optimization process in
improving the overall performance of cooperative perception in challenging
scenarios, substantially improving driving safety under adverse conditions.
Finally, we note that the networking assumptions are adopted from LTE Release
14 Mode 4 side-link communication, commonly used for Vehicle-to-Vehicle (V2V)
communication. Nonetheless, our method is flexible and applicable to arbitrary
V2V communications.
