ISA$^2$: Intelligent Speed Adaptation from Appearance
This work addresses a novel problem for autonomous driving systems by defining a new regression task based on appearance, but it is incremental as it builds on existing deep learning methods without broad SOTA impact.
The paper tackles the problem of predicting the proper vehicle speed from a driving image, introducing a new regression task called Intelligent Speed Adaptation from Appearance (ISA^2), and releases a novel dataset with annotated speeds and two deep learning models, showing the difficulty of the task in experimental validation.
In this work we introduce a new problem named Intelligent Speed Adaptation from Appearance (ISA$^2$). Technically, the goal of an ISA$^2$ model is to predict for a given image of a driving scenario the proper speed of the vehicle. Note this problem is different from predicting the actual speed of the vehicle. It defines a novel regression problem where the appearance information has to be directly mapped to get a prediction for the speed at which the vehicle should go, taking into account the traffic situation. First, we release a novel dataset for the new problem, where multiple driving video sequences, with the annotated adequate speed per frame, are provided. We then introduce two deep learning based ISA$^2$ models, which are trained to perform the final regression of the proper speed given a test image. We end with a thorough experimental validation where the results show the level of difficulty of the proposed task. The dataset and the proposed models will all be made publicly available to encourage much needed further research on this problem.