  Safety on roads is of uttermost importance, especially in the context of
autonomous vehicles. A critical need is to detect and communicate disruptive
incidents early and effectively. In this paper we propose a system based on an
off-the-shelf deep neural network architecture that is able to detect and
recognize types of unsigned (non-placarded, such as traffic signs), physical
(visible in images) road incidents. We develop a taxonomy for unsigned physical
incidents to provide a means of organizing and grouping related incidents.
After selecting eight target types of incidents, we collect a dataset of twelve
thousand images gathered from publicly-available web sources. We subsequently
fine-tune a convolutional neural network to recognize the eight types of road
incidents. The proposed model is able to recognize incidents with a high level
of accuracy (higher than 90%). We further show that while our system
generalizes well across spatial context by training a classifier on
geostratified data in the United Kingdom (with an accuracy of over 90%), the
translation to visually less similar environments requires spatially
distributed data collection.
  Note: this is a pre-print version of work accepted in IEEE Transactions on
Intelligent Vehicles (T-IV;in press). The paper is currently in production, and
the DOI link will be added soon.
