A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure

TL;DR

This paper explores the feasibility and challenges of developing scalable roadside object detection systems using various sensors and algorithms to enhance road safety and support Sweden's Vision Zero initiative.

Contribution

It provides a comprehensive pre-study on data processing pipelines for roadside object detection, analyzing target objects, sensor options, and algorithm design for scalable implementation.

Findings

Assessed data sources from road surveys and consumer vehicles.

Identified limitations and scalability issues in current data processing pipelines.

Outlined system requirements for effective roadside object detection.

Abstract

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.