With around 1.5 million cases reported annually, the number of lives lost in traffic collisions remains high, according to the World Health Organization’s Global Status Report on Road Safety 2015. The report found that traffic collisions cost most countries around three percent of their GDP.
Adding to these concerns is the fact that only 38% of drivers in the UAE see a direct link between distracted driving and road collisions, according to a YouGov survey commissioned by Road Safety UAE and QIC Insured. Distracted driving is what happens when a driver chooses to text, talk on the phone, smoke, eat or drink, apply makeup or self-groom, reach for objects in the car while driving or adjust entertainment and navigation systems while driving, in addition to driving while intoxicated.
One of the challenges faced by governments and traffic safety administrators around the world is to reduce the frequency of collisions and resulting fatalities by discouraging the practice of distracted driving. The advent of new traffic management technologies will continue to be critical to governments and law enforcement in the effort to improve road safety and traffic management.
Speed detection with automated issuance of fines is a practical and effective measure that has been used for years to discourage driving over the speed limit.
Taking it a step further, the implementation of artificial intelligence and machine learning technologies combined with LiDAR sensors and high accuracy cameras in internet-enabled police vehicles allows for autonomous detection of vehicles that are in violation of specific road rules. Traffic violations taking place in the vicinity are depicted in a 3-D grid on a compact computer screen in the police vehicle.
This technology is capable of measuring distances between cars with accuracy and precision, allowing the police officer operating the technology to focus on the road without having to manually detect violations.
With concern over distracted driving and its impact on public safety mounting in the UAE and the rest of the world, effective, automated methods that utilize advanced technology can play a critical role in reducing incidences of distracted driving. Detection of distracted driving is made possible by training a machine to identify specific behaviors classified as distracted and programming that machine to generate alerts each time an incidence is detected. The alerts – generated in real-time for law enforcement – enable equipped officers in the area to act immediately to halt the distracted driving and prevent further danger.
An in-vehicle system that autonomously detects drivers and alerts the officer is an ideal use of artificial intelligence and allows the officers to keep their attention on their own driving while machines manage detection and alert reporting. The same solution can be replicated in stationary road-side devices.
Automatic License Plate Recognition (ALPR), another feature of this technology, provides real-time information on vehicles on the road by legally accessing traffic records and identifying high risk vehicles, wanted vehicles or wanted registered owners. When the in-vehicle system identifies the license plate number of a car, it connects with the police records in real-time to determine a variety of data such as the vehicle’s registration status, the registered owner’s traffic history, whether the car has been reported stolen and other critical information.
Data generated by in-vehicle and road-side monitoring systems offer valuable insights for traffic administrators and law enforcement. Data analytics will enable us to identify trends such as high-risk times on the roads, locations of frequent collisions and car types and age groups commonly involved in collisions. Data scientists and engineers will also be to use this information to improve roads and inform traffic regulations for future city planning.