  Connected vehicles, whether equipped with advanced driver-assistance systems
or fully autonomous, are currently constrained to visual information in their
lines-of-sight. A cooperative perception system among vehicles increases their
situational awareness by extending their perception ranges. Existing solutions
imply significant network and computation load, as well as high flow of
not-always-relevant data received by vehicles. To address such issues, and thus
account for the inherently diverse informativeness of the data, we present
Augmented Informative Cooperative Perception (AICP) as the first fast-filtering
system which optimizes the informativeness of shared data at vehicles. AICP
displays the filtered data to the drivers in augmented reality head-up display.
To this end, an informativeness maximization problem is presented for vehicles
to select a subset of data to display to their drivers. Specifically, we
propose (i) a dedicated system design with custom data structure and
light-weight routing protocol for convenient data encapsulation, fast
interpretation and transmission, and (ii) a comprehensive problem formulation
and efficient fitness-based sorting algorithm to select the most valuable data
to display at the application layer. We implement a proof-of-concept prototype
of AICP with a bandwidth-hungry, latency-constrained real-life augmented
reality application. The prototype realizes the informative-optimized
cooperative perception with only 12.6 milliseconds additional latency. Next, we
test the networking performance of AICP at scale and show that AICP effectively
filter out less relevant packets and decreases the channel busy time.
