Ning, Minghao (University of Waterloo), Cui, Yaodong (University of Waterloo), Yang, Yufeng (University of Waterloo), HUANG, SHUCHENG (University of Waterloo), Liu, Zhenan (University of Waterloo), Ahmad, Alghooneh (University of Waterloo), Hashemi, Ehsan (University of Alberta), Khajepour, Amir (University of Waterloo)

Enhancing Indoor Mobility with Connected Sensor Nodes: A Real-Time, Delay-Aware Cooperative Perception Approach

Scheduled for presentation during the Invited Session "Trustworthy Diagnosis and Prognosis in Connected, Cooperative and Automated Mobility" (WeBT10), Wednesday, September 25, 2024, 15:50−16:10, Salon 18

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

This paper presents a novel real-time, delay-aware cooperative perception system designed for intelligent mobility platforms operating in dynamic indoor environments. The system contains a network of multi-modal sensor nodes and a central node that collectively provide perception services to mobility platforms. The proposed Hierarchical Clustering Considering the Scanning Pattern and Ground Contacting Feature based Lidar Camera Fusion improve intra-node perception for crowded environment. The system also features delay-aware global perception to synchronize and aggregate data across nodes. To validate our approach, we introduced the Indoor Pedestrian Tracking dataset, compiled from data captured by two indoor sensor nodes. Our experiments, compared to baselines, demonstrate significant improvements in detection accuracy and robustness against delays. The dataset is available in the repositoryfootnote{url{https://github.com/NingMingHao/MVSLab-IndoorCooperativePerception}}.