When most people consider the merits of machine learning, they typically think about its applications from a capitalist standpoint. There are countless ways that business owners are using machine learning advances to pad their bottom lines.
However, policymakers, nonprofits and other experts are also finding machine learning applications that serve more benevolent purposes. Consumers are also looking for new machine learning tools to help mitigate their daily risks and solve some of their most perplexing challenges.
One of these benefits is reducing the prevalence of car accidents. In addition to helping reduce the risks of accidents, they can also help attorneys like Brian White make better cases by identifying negligent risk factors on the part of the other party in car accident litigation.
Over 3 million people are injured in car accidents in the United States alone each year. Although some accidents are inevitable, the prevalence could be reduced considerably by improving highway planning, helping drivers identify risk factors and better organizing events with high traffic volume.
There are a number of ways that machine learning could help address these concerns.
New machine learning initiatives offer promising opportunities to lower car accidents
The University of Washington announced a new initiative to help reduce car accident injuries and fatalities with the use of machine learning. In 2017, the university forged a partnership with Microsoft and the city of Bellevue.
This initiative relies on CCTV imagery from major roads. These traffic cameras provide footage of collisions and near collisions throughout the United States, Canada and Mexico.
Data from these accidents is used to train machine learning algorithms to identify correlating risk factors with car accidents. The goal is to develop predictive analytics models that will be able to recommend changes to prevent such accidents from occurring in the first place.
Although some machine learning initiatives offer insights directed towards public policy makers and engineers, other data analytics projects are more geared towards educating the drivers themselves. They learn to identify numerous risk factors and alert the driver.
Nauto is one major company that has made major strides with artificial intelligence in the use of traffic safety for drivers. It provides a machine learning tool kit that relies on multiple sensors. This includes a camera that monitors the driver and another that screens the road.
It provides alerts to the driver if it notices any hazards that might be overlooked. It also looks for signs that the driver is distracted or tired, so it can bring these concerns to their attention.
Machine learning algorithms will also be able to aggregate data from third parties on traffic safety risks. They might use state highway data to alert drivers of traffic congestion and accident scenes, which pose a greater threat to traffic safety.
How nuanced can these machine learning risk analyses really be?
We have witnessed an increasing number of data scientists develop machine learning technology that assists with car accident protection and prevention. Some of the research that has received the most publicity centers around broader discussions on this topic. However, the more interesting case studies talk about the more specific applications for machine learning in traffic safety.
Eugenio Zuccarelli, a renowned data scientist talked about his approach in Towards Data Science. He helped develop algorithms that can deal with traffic safety risks in the United Kingdom.
His algorithms used a very nuanced model that relied on extensive inputs from public data and customer responses. It even took variables like customer age and their type of vehicle into consideration. This data is obviously important, because the nature of a vehicle and driver demographics will always be part of any sensible risk scoring metric. However, the average driver doesn’t consider these factors until it is too late. Therefore, a machine learning algorithm that gives them tailored advice will help minimize the threat of an accident.
Zuccarelli shared some comments on the potential applications of his machine learning models in traffic accident prevention:
“By understanding the external factors driving the risk of an accident, the government can prioritise spending by targeting first the major drivers of accidents. For instance, if we find that the light condition is more important than the road condition itself, the Department of Transport can allocate its limited budget prioritising lighting conditions first and then the quality of roads.”
In addition to helping the government, it can also help insurance companies with their actuarial algorithms.
Machine learning is going to play a critical part in reducing car accidents in the foreseeable future
Experts have been concerned about the growing risk of car accidents in densely populated areas. The COVID-19 crisis has made people even more aware of the vulnerabilities that society faces towards public health crisis, which may call for stricter controls for other concerns as well.
We have started to see the potential benefits of using machine learning to mitigate the risks of car accidents already. Drivers and engineers alike are likely to invest in this technology to help keep society safe from the growing threat of traffic fatalities.