  Single-vehicle accidents are the most common type of fatal accidents in
Sweden, where a car drives off the road and runs into hazardous roadside
objects. Proper installation and maintenance of protective objects, such as
crash cushions and guard rails, may reduce the chance and severity of such
accidents. Moreover, efficient detection and management of hazardous roadside
objects also plays an important role in improving road safety. To better
understand the state-of-the-art and system requirements, in this pre-study, we
investigate the feasibility, implementation, limitations and scaling up of data
processing pipelines for roadside object detection. In particular, we divide
our investigation into three parts: the target of interest, the sensors of
choice and the algorithm design. The data sources we consider in this study
cover two common setups: 1) road surveying fleet - annual scans conducted by
Trafikverket, the Swedish Transport Administration, and 2) consumer vehicle -
data collected using a research vehicle from the laboratory of Resource for
vehicle research at Chalmers (REVERE). The goal of this report is to
investigate how to implement a scalable roadside object detection system
towards safe road infrastructure and Sweden's Vision Zero.
