Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (hypertarget ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.
A Cooperative Vehicle-Infrastructure System for Road Hazards Detection With Edge Intelligence
IEEE Transactions on Intelligent Transportation Systems ; 24 , 5 ; 5186-5198
2023-05-01
1346937 byte
Article (Journal)
Electronic Resource
English
Cooperative learning method for road infrastructure detection and characterization
European Patent Office | 2015
|Vehicle speed detection device of intelligent vehicle infrastructure cooperative system
European Patent Office | 2021
|Vehicle-road cooperative intelligent roadside detection system
European Patent Office | 2022
|