Uses of AI in Road Safety

Did you know that around 1.3 million individuals pass away every year due to automobile accidents (source: Worth Health Organization)? According to estimates, another 20 to 50 million people experience non-fatal injuries, many of whom develop a disability. In the wake of such an alarming number, the United Nations General Assembly has fixed a bold objective of cutting the global number of fatalities and injuries from traffic accidents in half by 2030 (A/RES/74/299). For this aggressive target, it is important that the benefits of emerging technologies reach everyone and everywhere. Experts believe that there is an untapped possibility to use Artificial Intelligence in order to bridge the digital and road safety barrier globally. 

You must have already read the headlines about how artificial intelligence and machine learning are benefiting every industrial sector. No wonder AI can be leveraged in the field of road safety as well. With the growing applications of AI, people are trying to step into this field by gaining work-ready skills. A number of training options are now available for individuals willing to switch their careers to AI and machine learning. You can, in fact, enroll in MS in AI online, offered by a reputed university or edtech platform, to learn AI from scratch.

This article helps you understand how artificial intelligence can prove to be advantageous in ensuring better road safety. 

AI for Road Safety

There are several aspects of road safety where AI can offer innovative solutions. Some of them include fatigue indications, enhancing road infrastructure, advanced vehicle collision avoidance systems, black spot accident patterns, forensic post-crash investigations, sleep detectors for motorists, and inspiring creative solutions in the regulatory frameworks. Implementation of AI calls for the ethical use of algorithms and equal access to data, both of which many nations now need help finding solutions for road safety. 

Let’s talk about some of the above applications in detail:

Black spot accident patterns

An AI system can be created that will recognize and detect accident-prone locations and circumstances when driving a car or other vehicle. An advanced driver assistance system can be equipped in a vehicle that also aids in warning drivers about potential threats up ahead. Through data analysis and mobility analysis, the ground-breaking system can continuously watch and monitor dynamic dangers on the road network in order to identify black spots.

 

Forensic post-crash investigations

Given how much time people spend behind the wheel, a digital forensics investigation into the computer of an automobile can access that data for a number of purposes. With more modern vehicles, recovering vehicle data can be essential for accident investigations. In the past, the crash scene and tangible evidence were the main focus of an accident inquiry. However, with AI, data can be just as important in settling an event as tire marks and vehicle damage. 

Road network operations

When it comes to road network operations, AI can be implemented for modeling and prediction of traffic conditions, asset evaluation and upkeep, traffic safety, management of traffic and incidents, and systems for regulating traffic signals, among others. For example, machine learning techniques are now being used to anticipate bus arrival and departure times as well as long and short-term travel times along with traffic density across the network. An ML-powered tool can also be created that uses the raw data generated by traffic cameras to identify various traffic objects and characterize them based on their movements and interactions. 

Sleep detectors for motorists

There are various solutions that might be used to help vehicles detect the driver’s level of attentiveness. Some eyelid tracking systems have issues with poor illumination or when the driver turns their head away. Some systems measure the steering wheel’s movement as a motorist’s input. However, this won’t function in an autonomous car. With the creative use of AI, a novel method for detecting sleepiness by monitoring the electrical activity of the heart has also been developed. 

Advanced vehicle collision avoidance systems

On average, a 40%–60% reduction in vehicle collision frequency and collision-related expenses can be achieved by utilizing AI sensors in the car to identify driver movement, gaze direction/attention, vehicle activity, traffic circumstances, and other contextual data. AI assistance, along with AI assistance in the vehicle, can help minimize distracted driving without the participation of motorists. AI safety technologies are completely compatible with preserving driver privacy and do not require human review or video access at all. Some companies have even tried to fuse important inputs simultaneously, including driver behavior, to provide drivers adequate time to respond and avoid impending crashes in real time rather than after they occur.

Wrap Up

Governments across countries are trying to harness the potential of AI in every field, and Road safety is one of them. Reading the above content, you must now have a clear idea regarding how AI can benefit in ensuring road safety and reduce the number of alarming accidents happening globally. If you haven’t explored AI yet, now is the right time to learn about this powerful technology. You may even take a reputed artificial intelligence and machine learning course to start understanding this subject from scratch. If you gain relevant skills, you will be a part of scarce AI talent, and your demand will be high in today’s competitive job market.