P. Zhou et al., "AICP: Augmented Informative Cooperative Perception," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 22505-22518, Nov. 2022, doi: 10.1109/TITS.2022.3155175.
AICP : augmented informative cooperative perception
|Author:||Zhou, Pengyuan1; Kortoçi, Pranvera2; Yau, Yui-Pan3;|
1Research Center for Data to Cyberspace, University of Science and Technology of China, Hefei, China
2Department of Computer Science, University of Helsinki, Helsinki, Finland
3Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong
4School of Computer Science and Technology, Anhui University of Technology, Ma’anshan, China
5Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Hong Kong
6Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
7Center of Ubiquitous Computing, University of Oulu, Oulu, Finland
8Computational Media and Arts Thrust, The Hong Kong University of Science and Technology, Hong Kong
|Online Access:||PDF Full Text (PDF, 12.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022120569400
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-12-05
Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, require human driver supervision and are currently constrained to visual information in their line-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception range. Existing solutions focus on improving perspective transformation and fast information collection. However, such solutions fail to filter out large amounts of less relevant data and thus impose significant network and computation load. Moreover, presenting all this less relevant data can overwhelm the driver and thus actually hinder them. To address such issues, we present Augmented Informative Cooperative Perception (AICP), the first fast-filtering system which optimizes the informativeness of shared data at vehicles to improve the fused presentation. 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 lightweight 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 adds only 12.6 milliseconds of latency to a current informativeness-unaware system. Next, we test the networking performance of AICP at scale and show that AICP effectively filters out less relevant packets and decreases the channel busy time.
IEEE transactions on intelligent transportation systems
|Pages:||22505 - 22518|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Academy of Finland 5G Edge computing enhanced Augmented Reality (5GEAR) Project under Grant 319669, in part by the Federated probabilistic modelling for heterogeneous programmable IoT systems (FIT) Project under Grant 325570, and in part by the National Key Research and Development Program under Grant SQ2021YFC330008.
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