Real-time Uncertainty Estimation Based On Intermediate Layer Variational Inference

Deep neural networks have been the prominent approach for many computer vision tasks, excelling in solving many critical tasks. However, estimating the uncertainty of the network’s predictions has still been an open research question with various approaches, adding an edge to a deep neural network by providing more information about the predictions it is generating. Uncertainty estimation is deemed to be an important enabler for the future of automated driving systems, as its information could be needed for processing the vehicle’s next maneuver based on the uncertainty estimates of its perception module. In this paper, we propose a new approach by adding intermediate multivariate layers within a deep neural network aiming to provide much faster uncertainty estimations than the top two state-of-art approaches, MC Dropout and Deep Ensembles. A thorough comparison between the proposed approach and the two state-of-art approaches is presented to evaluate the new technique, assessing its speed, performance and calibration. Results show that the proposed uncertainty estimation method is significantly faster with the potential for real-time applications whilst exhibiting comparable performance to the state-of-art approaches.