4 Ways Computer Vision Will Reshape Urban Transportation
Smart transportation is one of the fundamental components of smart cities. The integration of digital technologies with physical transport infrastructure will transform how people live, work and travel in cities. The use of autonomous vehicles, IoT, big data analytics and many more technologies will enable urban residents to travel safer, cheaper and faster. The mobility and communication networks in urban spaces keep any city running smoothly. Adding elements of smart transportation to these will make cities more efficient, livable and sustainable. Computer vision is expected to play a key role in several smart transportation applications—from self-driving cars and traffic flow analysis, to parking space management and road condition monitoring.
Smart transportation relies on digital systems that process a huge amount of information in the form of images, videos, audio files, text-based information, GPS and GIS data, IoT sensor data and other forms of data. Machine learning and computer vision algorithms are needed to process this raw information and convert it into actionable insights for urban planning bodies to formulate effective policies in smart cities. These technologies are also the driving force behind complicated applications like self-driving cars, intelligent traffic management, smart airport video surveillance and automated parking systems.
According to the World Health Organization (WHO), approximately 1.3 million people die in road crashes every year. Some of the leading causes of traffic accidents are speeding, driving under the influence of alcohol, avoidance of safety gear like helmets and seatbelts, distracted driving and non-adherence to traffic rules. As you can see, human error is the reason for most traffic accidents.
Autonomous vehicles can remove the human element from this equation, thereby drastically reducing the chances of a crash. A self-driving car will constantly collect information from the vast network of sensors and cameras on motor vehicles, roads and traffic signals. Computer vision algorithms will analyze this raw data to optimize road safety and generate insights on collision alerts and pedestrians on the road in real-time. A self-driving car can process data dynamically and detect how close it is to pedestrians, other vehicles, cyclists and potential hazards on the road before making accurate adjustments. Image processing algorithms will also enable autonomous vehicles to pick out moving objects in low-light areas and automatically trigger airbags and automatic brakes in the event of a collision.
Other safety technologies within an autonomous vehicle that will transform road safety are:
These applications rely on computer vision and machine learning algorithms to function correctly. Recently, the Universities of Applied Sciences of Ulm and Heilbronn, Germany, have collaboratively created a self-learning road warning system, which leverages sensor, radar and camera data to identify moving objects and warn drivers to prevent accidents.
Smart transportation doesn’t just involve self-driving cars but also the optimization of road networks. Traffic congestion is the biggest reason for increased travel time in cities. It contributes to higher fuel consumption and air pollution. Intelligent traffic monitoring and management can address such issues by leveraging computer vision to reduce congestion and fuel consumption.
The first step in smart traffic monitoring systems is collecting data through overhead and ground-based cameras, GPS, GIS and radiofrequency devices. This data is fed to computer vision algorithms that will detect vehicles on the road, calculate traffic density and relay their status to a local traffic control center. The real-time road congestion data is analyzed further to reroute vehicles to a less-congested road.
