Sanral mulls use of machine learning to improve road safety

Read time 3min 10sec

The South African National Roads Agency Limited (Sanral) is probing how machine learning (ML) can be harnessed to improve road safety, reduce congestion and inform infrastructure development.

Sanral notes its innovation arm, the Technical Innovation Hub (TIH), is driving this project.

Established by Sanral in 2019, TIH is described as a think tank, which aims to be at the forefront of harnessing technology to inform, improve and expedite road safety across the South African road network.

In a statement, Ruan Van Breda, mechatronic engineer at TIH, explains ML can be used to detect and segment objects within a camera frame – each frame of a video is analysed as a still image.

“These objects can then be classified based on pre-trained image classifiers. Within the road environment, this allows one to detect and classify different type of vehicles, pedestrians, different types of animals, cyclists, etc.”

Van Breda believes the possibilities are infinite, adding there’s already ample data available for these classification types.

These genres can be further expanded through the creation of custom data sets and training classifiers, to be able to distinguish, for example, between slow-moving traffic and a road traffic crash, he says. This can also be used to create new classification classes based on unique experiences, or the requirements of the road authority; for example, fire or protest detection, foreign objects such as rocks, and tyre detection.

The information can then be used to activate the appropriate response through the Road Incident Management System, remedy the situation and inform road users – in real-time, he states.

“One can also look at how these different objects interact with one another; for example, to detect unusual vehicle behaviour, like a vehicle stopping on the freeway. One is furthermore able to infer information about the interaction between multiple elements such as cars and pedestrians.

"If a vehicle is detected moving to the side of the road and coming to a standstill and pedestrians are detected moving towards the vehicle and enter the vehicle, this can be classified as an informal pick-up. As more and more data is collected, these trends can either aid road authorities with infrastructure planning such as drop-off / pick-up points or aid law enforcement to stop illegal pick-ups if it is considered a safety risk.”

Sanral says even though technology of this nature does present some “significant” risks, all efforts are being made to understand how to effectively use the technology while maintaining strict compliance with legislation as it relates to the privacy of road users.

Some of the ways to mitigate these potential privacy risks are to use strict security and access controls. Furthermore, data can be anonymous at the point of capture.

“While this technology is still in the exploratory phase in South Africa, it already has tongues wagging in countries like China, where they use machine learning to incorporate facial recognition for law enforcement. They are able to identify the driver of a vehicle and instantly issue fines, if that driver does not have a valid driver’s licence.

“Fines can also be issued automatically for individuals who jaywalk or gain access to restricted areas. As with any technological advances, there are pros and cons, and in a complex society like South Africa, for now, let’s look and learn,” concludes Van Breda.

See also