Real-Time Multi-Task Environmental Perception System for Traffic Safety Empowered by Edge Artificial Intelligence

• Published in: IEEE transactions on intelligent transportation systems Vol. 25; no. 1; pp. 1 - 15
• Main Authors: Liu, Chenxi, Yang, Hao, Zhu, Meixin, Wang, Feilong, Vaa, Torgeir, Wang, Yinhai
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Cameras; Classification; computer vision; Data transmission; Edge computing; Image edge detection; Image segmentation; Meteorology; Multidimensional methods; Perception; Resilience; Road conditions; Road surface; Roads; Safety; Sensors; Task analysis; Traffic environment; traffic perception; Traffic safety; Visibility; Weather; Weather stations
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3309100

Traffic safety, reliability, and resilience are significantly influenced by environmental factors such as visibility, road surface, and weather conditions. Yet, current monitoring methods, including weather stations and onboard environmental sensors, often fall short due to their high costs, significant latency, and limited dissemination. This paper presents the Edge-based Multi-task Safety-oriented Environmental (Edge-MuSE) sensing system, designed to address these traffic safety challenges associated with environmental factors. Edge-MuSE departs from traditional single-task sensing methods by performing multidimensional traffic environment perception tasks. It estimates key safety-related environmental factors exclusively through camera inputs and incorporates four innovative sensing tasks: visibility estimation, image dehazing, road segmentation, and road surface condition classification. The system is tailored to edge devices to transition computational loads from central servers to distributed nodes, thereby enhancing privacy and reducing latency. Additionally, Edge-MuSE integrates communication functions based on TCP/IP and Wi-Fi protocols, enabling rapid dissemination of sensing results and warning messages to local road users. System structures and data streaming have been optimized to accommodate the constraints of edge devices, ensuring high-efficiency edge computing. Field testing of Edge-MuSE in multiple testbeds in Bellevue (WA, US) and Oslo (Norway) has demonstrated its reliable and precise performance in perception tasks (92.15% accuracy in visibility estimation and 92.25% in road surface condition classification) as well as an impressive processing speed of 21.3 FPS. As such, Edge-MuSE presents a promising solution for enhancing roadway safety, efficiency, and resilience.