Transportation is an essential part of our livelihood. The daily commute from one place to another is at ease with the development of efficient transport systems. Especially when you reside within the city limits, better infrastructure facilities which promise good transport must have made your life easy. But since every coin has two sides, several security and safety risks are occurring when the system fails. It is to avoid such risks that the governments enforce rules and regulations that the citizens have to follow when stepping out to a public road. These rules will range from pedestrian crossings to driving speed limits on special roads. In worst-case scenarios, small negligence in maintaining road rules might cost a valuable life.

For helping the authorities to bring the road rules under control, AI-based computer vision is playing a major role these days. Monitoring the violation of rules such as maintaining speed limits, pausing at stops and wearing safety measures such as helmets and seatbelt, computer vision technology is playing a huge role in building secured transport facilities. Mentioned following are some of the major use cases of Computer Vision in transport systems.

Safety and driver assistance

A CDC survey states that approximately 1.35 million people fall victim to motor vehicle accidents every year. There are number of factors that become the cause for such incidents. Some are visibility issues, lack of focus, fatigued drivers, and technical issues. In many places across the world, especially in smart cities, there are sensors installed that have data receptors. These sensors placed at highways and other busy junctions have computer vision powered cameras installed in them. These cameras help in controlling the safety methods by monitoring and providing information on the distance among vehicles and pedestrians and static structures in the road. This data is shared with the driver who can then choose a much safer route. The visual data collected by the Computer Vision models can be used by municipalities and other public agencies as well. Visual data receptors also avoid any possibilities of collisions by identifying highway symbols, obstacles and other road details. Computer Vision devices will help to monitor agencies to know the routes with the highest number of travellers. As a result, fuel and vehicle usage can be controlled in public transport.

Traffic Control

Easy mobility is a factor that influences the transport system in a smart city. For instance, if a person has to be taken to the hospital quickly and has to go through some of the important junctions in the city, it might be difficult for an ambulance or an emergency vehicle to pass through. In this scenario, there are Computer Vision powered smart transportation tools along with IoT network devices, that provide traffic monitoring and communication. These models can inform the drivers which lane should be taken for an easy commute to reach the destination faster. For this purpose, data receptors can be mounted on vehicles. Mobile apps that compliment Computer Vision-based smart transportation can also be used. 

There are also Computer Vision and IoT based applications on roads and vehicles. If a vehicle is found to be crossing the speed limit it will notify the driver as well as the traffic police. GPS, GIS and radio-frequency devices are also embedded with Computer Vision algorithms to find vehicle proximity and upcoming traffic density of a particular area.

Driving Autonomous Vehicles

Driverless vehicles have become the mantra for several motor manufacturing companies. Among the plethora of technologies, these vehicles also use Computer Vision for their functioning. Unlike manual driving, these automated driving techniques rectify human errors and give preference to mobility, the safety of the occupants and pedestrians, and fuel-related factors. The smart cameras installed in these vehicles will aid the car to find different obstacles in front of it even if the visibility is low. They depend on Computer Vision for 3D mapping and decision making on the route, driving speed and parking. Two autonomous vehicles in the same path can foresee an accident by using the embedded technologies and the vehicles can choose to pull over. 

AI and Computer Vision are two important technologies in building a good transport system. Since there is an increase in security issues concerning transportation, incorporating AI-based technologies for building infrastructure facilities will be an act of good governance as it values the wellbeing of the citizens.