Reconfigurable Network for Efficient Inferencing in Autonomous Vehicles

Abstract

We propose a reconfigurable network for efficient inference dedicated to autonomous platforms equipped with multiple perception sensors. The size of the network for steering autonomous platforms grows proportionally to the number of installed sensors eventually preventing the usage of multiple sensors in real-time applications due to an inefficient inference. Our approach hinges on the observation that multiple sensors provide a large stream of data, where only a fraction of the data is relevant for the performed task at any given moment in time. The architecture of the reconfigurable network that we propose contains separate feature extractors, called experts, for each sensor. The decisive block of our model is the gating network, which online decides which sensor provides the data that is most relevant for driving. It then reconfigures the network by activating only the relevant expert corresponding to that sensor and deactivating the remaining ones. As a consequence, the model never extracts features from data that are irrelevant for driving. The gating network takes the data from all inputs and thus to avoid explosion of computation time and memory space it has to be realized as a small and shallow network. We verify our model on the unmanned ground vehicle (UGV) comprising of the 1/6 scale remote control truck equipped with three cameras. We demonstrate that the reconfigurable network correctly chooses experts in real-time allowing the reduction of computations cost for the whole model without deteriorating its performance.