BSL: Sustainable Collaborative Inference in Intelligent Transportation Systems

As the recent rise of intelligent transportation systems (ITS), the sensing capability of vehicles has become crucial in realizing sophisticated intelligent transportation services. Collaborative sensing, an important approach to extend the sensing coverage of individual vehicles, has become an essential component of connected vehicle systems. However, due to challenges such as privacy concerns, frequent communication interruptions, customized models, and limited available data, the application of collaborative sensing in current ITS systems is still limited. In this paper, we propose BSL, a novel multi-exit split learning-based collaborative inference system. The key innovation of BSL is the introduction of multi-exit to the split network, enabling network training and collaborative inference between distributed device nodes and the cloud in a split manner. Specifically, BSL allows the device node to dynamically collaborate with the cloud by introducing the edge mode and collaboration mode, ensuring that intelligent services provided to the device will be sustained even if the communication is interrupted, which is crucial in ITS systems. We have implemented the system and evaluated it with public dataset on different embedded devices. The results demonstrate the promising performance of BSL.