Enhanced Infrastructure Enabled Perception System Based on Edge Computing

Perception technology plays a crucial role in vehicle automation, yet traditional approaches solely rely on onboard computing and have inherent limitations in perception range. To improve perception range, edge computing is introduced. Through edge computing, some perception computing tasks can be offloaded from onboard sensors to sensors installed on roadside infrastructures. This infrastructure-enabled perception (IEP) expands perception range beyond what onboard sensors alone can achieve. However, existing IEP systems have limited precision in long-distance perception and require costly sensors for optimal performance. To enhance the performance without significant financial investment, this article proposes an enhanced IEP system. The proposed IEP system adopts a virtual-detector-based perception solution, designing multiple virtual detectors to detect vehicle arrivals. Unlike traditional IEP approaches, the proposed system does not rely on dense data points for estimating vehicle geometry. Instead, it bypasses the geometry-estimation step and only needs sparse data points to detect vehicle arrivals. Consequently, even with sparse data points at long perception distances, our IEP system can achieve high precision in long-distance perception. Due to this enhanced solution, the proposed IEP system has the following features: 1) ensuring wide perception range; 2) enabling cm-level precision perception; 3) maintaining robustness against perception distances, vehicle speeds, and sensor frequencies; 4) compatible with mass-produced and cost-effective sensors; and 5) laying a foundation for infrastructure-enabled cooperative driving. Experimental validation confirms the system’s advanced features and demonstrates its superiority over the state-of-the-art IEP system. It achieves a wide perception range up to 150 m and a low localization error down to 6.500 cm. Further investigation suggests that the IEP system should primarily be deployed on expressways and implemented for speed-related cooperative driving applications, such as speed harmonization.

Recommended citation: J Hu, S Luo, J Lai, C Liu. (2025). " Enhanced Infrastructure Enabled Perception System Based on Edge Computing." IEEE Internet of Things Journal. vol. 12, no. 11, pp. 16493-16510, 1 June1, 2025, doi: 10.1109/JIOT.2025.3532317.
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