How Computer Vision Can Create Intelligent Transportation Systems
Key word: intelligent transport
Bustling cities need the necessary transportation networks to keep them running smoothly. AI-powered smart transportation and computer vision make it easy for smart cities to achieve this goal.
There are three attributes that every smart city – or any place of human settlement for that matter – must possess in abundance: livability, maneuverability and sustainability. Frameworks and amenities that enable residents to lead comfortable, clean, healthy, and safe lives increase the livability quotient of a smart city. Additionally, communication and mobility networks that make it easier for residents to get to and from work, expand employment opportunities, and simplify the creation and growth of businesses improve the workability aspect of these cities. And finally, the sustainability of a smart city depends on how well it uses technology to reduce energy consumption, pollution and accidents. As you can see, the role of transport networks and technologies is huge in deciding how livable, functional and sustainable smart cities can be. Intelligent transport holds the key to making vehicle networks in smart cities highly connected and safe for this purpose.
Already, computer vision can be used in countless different applications in smart cities. One of the key application areas is intelligent transport. AI, IoT and computer vision bring their unique capabilities to transform transportation networks and vehicles in smart cities in myriad ways.
Computer vision for accident prevention
According to the CDC, about 1.35 million people are victims of road accidents each year, with about 3,700 daily deaths in road accidents. A large portion of these fatalities each year are pedestrians, cyclists and motorcyclists. Such accidents occur due to a number of factors such as poor visibility, driver fatigue, lack of concentration, and technical failure, among other reasons. Intelligent transportation involves an IoT network that includes data-receiving sensors installed on highways and busy streets as well as motor vehicles. These sensors, along with CCTV cameras powered by computer vision, can provide timely information to vehicle drivers about the proximity of their vehicles to pedestrians, static structures, or other vehicles at any time. In a hyper-connected smart city, this information is first captured autonomously by smart cameras. Then it can be sent to connected vehicles. Once a driver receives the data in their vehicle’s infotainment system, they can slow down or take an alternate route. Computer vision system algorithms can predict potential accident systems in advance to reduce preventable accidents.
Today, dynamic image capture and processing are important parts of vehicles. Smart city vehicles need computer vision tools for occupant and pedestrian safety. Additionally, visual data collected and analyzed by computer vision applications can be used by municipalities and other public agencies to create smart community initiatives.
As with any computer vision or AI-based technology, intelligent transportation applications keep getting better with time. Thus, visual data receivers improve the identification of highway symbols and features, lane markings, obstacles, and other road-related details. Additionally, these systems are becoming significantly better at detecting objects and potential collisions and relaying information to vehicle drivers and pedestrians, via smartphone apps or wearable technology.
Safety technologies such as intuitive emergency braking, lane centering, blind spot safety monitoring, collision warning systems, adaptive cruise control, intelligent speed adaptation systems , night vision systems, pedestrian and traffic sign recognition systems rely heavily on digital image capture and cognitive processing of information facilitated by computer vision systems. In today’s era of big data, computer vision enables the analysis of raw information on the fly through algorithms performing millions of probability-based calculations and generating valuable insights and predictions to optimize road safety.
Smart transport also reinforces the sustainability aspect of smart cities. For example, machine learning devices in public transport vehicles allow transport agencies to know the routes with the highest number of travelers. This information allows agencies to reduce the number of vehicles in areas where the number of travelers is generally low. Accordingly, the use of fuel and vehicles can be controlled. Smart cities such as Amsterdam actively prioritizes sustainability and citizen safety when drawing up urban plans. Besides safety and sustainability, computer vision can boost mobility in smart cities through intelligent traffic management.
Computer vision for traffic management
As mentioned earlier, intelligent transport is not only related to tools and applications embedded in vehicles. The concept also involves the optimization of road networks in a smart city. Fluid mobility is an essential aspect to assess the livability, maneuverability and sustainability of any smart city. For example, if there is a medical emergency and someone needs to be rushed to hospital, having a mobile transport network can save lives in such a situation. Similar examples of useful mobility to enhance the sustainability and manageability of smart cities can also be seen in everyday life. So how exactly does computer vision help traffic management and, by extension, mobility in smart cities?
Traffic automation mechanisms that cannot be automated usually tend to contain multiple errors. Thus, in the era of rapidly increasing daily commuters, shortened or narrow roads, and globalization, traffic monitoring becomes a matter of concern for local administrators in any region. Smart cities feature some of the busiest highways and an incredibly high number of vehicles, raising the level of concern.
Intelligent transport tools based on computer vision, as well as IoT network devices, facilitate autonomous traffic monitoring and communication. Smart traffic lights, smart parking, and traffic guidance systems use computer vision and IoT to help drivers know available lanes that can get them to their destination in less time. Vehicle-mounted data receivers and connected mobile applications complement intelligent transportation applications based on computer vision for this purpose.
Smart roads and highways are also part of a city’s intelligent transportation network. These concepts feature IoT and AI-based applications in roads and vehicles to control the speed limit of vehicles on all types of roads. These apps continuously and autonomously monitor traffic flow and send alerts to drivers and traffic police personnel if vehicles exceed the speed limit. This feature of smart transportation is particularly useful for law enforcement authorities as it prevents them from being overwhelmed by the high volume and speed of traffic in smart cities.
Geospatial traffic guidance systems, which are also part of intelligent transportation networks powered by computer vision, use GPS, GIS, and radio frequency devices for detailed traffic monitoring. These tools work in unison to provide 3D spatial and geographic information to traffic controllers and drivers regarding vehicle proximity, traffic density, upcoming obstacle alerts, and traffic flows on specific routes. .
One of the examples of city-based intelligent traffic management is found in the city of Darmstadt, Germany.
Computer vision for autonomous driving
During the last years, nearly every major automaker has entered the driverless market. The number of autonomous vehicles is expected to increase over the next few years. These vehicles will represent the next generation of smart transportation in smart cities. Moreover, these vehicles also have a plethora of technologies, including computer vision applications, which will positively influence their mobility, occupant and pedestrian safety, and fuel efficiency factors.
Autonomous vehicles rely on multiple smart cameras for object recognition – for example, the precise identification of pedestrians, traffic lights or other vehicles, even in moderate to low visibility conditions – in order to regulate safety devices such as airbags and automatic brakes. Moreover, these vehicles also rely on computer vision for 3D mapping for better decision making regarding route selection, driving speed and parking. This translates into even fewer accidents in connected smart cities.
On top of that, computer vision, IoT and AI are also making autonomous vehicles more “connected”, meaning they can autonomously “communicate” with other smart vehicles and with devices. and intelligent transport applications. So, for example, two self-driving vehicles in a narrow lane can predict a crash due to a vehicle accelerating towards them from the opposite side. Using IoT and computer vision, the two vehicles can either pull to safety or change lanes in perfect synchronization to avoid a collision and the resulting build-up of vehicles. This is arguably the most basic example of what smart connected vehicles can do.
Smart transportation simply wouldn’t exist without computer vision, AI, IoT, blockchain, and a few other smart city technologies. Computer vision, in particular, is instrumental in analyzing dynamically captured data and using it in vehicles and transportation regulation devices. In this way, it can be said with certainty that computer vision is playing its part in making smart cities more livable, sustainable and functional.