How artificial in transportation improves our daily life?

Roads, streets and their traffic are the most important elements of modern cities and countries. The welfare of the entire country depends on their efficiency because inefficient traffic planning can lead to a significant increase in the death rate due to accidents, community disconnection, environmental pollution and even various diseases caused by nerves. The transportation industry is a complex system with many external influencing factors, such as human errors and reactions, accidents, economic conditions, and even the time of year or day. Artificial intelligence (AI) uses all these data points to predict the probability of different situations, thus providing conditions for appropriate decisions and planning. Multiple use cases of artificial intelligence in transportation use computer vision, such as object recognition or object tracking. While the most eye-catching applications of AI in transportation are well known, such as self-driving vehicles, self-driving air taxis, or smart highways, numerous other use cases are less spectacular but still very useful. For example, intersections and pedestrian or cyclist paths are visually monitored by artificial intelligence systems to detect traffic accidents and increase safety. In addition, AI in transportation examines traffic patterns for causes of delays or causes of traffic congestion. In this article from Avir AI, we will explore how AI in transportation can improve our daily lives.

Autonomous vehicles

Intelligent driver assistants such as automatic parking, lane detection and adaptive cruise control have become the norm for many new cars in developed countries. Some, like Hyundai’s advanced cruise control, have been widely implemented. However, they do not do the job of a driver completely and a human must be present next to them.

Although self-driving vehicles already exist, fully autonomous implementation is difficult and requires a lot of work (and huge amounts of data). Any confusion in the transmission and instant processing of data in cars can lead to a dangerous and fatal result. Hence, the projects are not yet fully ready to be implemented on every road.

In Tokyo, while self-driving taxis will be allowed on some roads in spring 2023, Japanese auto giants such as Toyota Motor and Nissan Motor have not set a time frame for rolling out cars or services that use artificial intelligence for self-driving. Significant resources have not yet led to the necessary improvements in sensors and software.

In the United States, Waymo began developing self-driving vehicles around 2010, introducing trucks and minivans in a number of states for testing on public roads in 2018, but mass production has yet to begin.

Making self-driving vehicles safe for passengers will obviously take time. As technology continues to advance, self-driving cars will become more reliable and more widespread. Artificial intelligence with the help of sensors, cameras and GPS will have a great impact on public transportation.

AI technology can reduce the rate of human error and monitor compliance with safety regulations to reduce driving risks.

Pedestrian detection

Driving at night is a challenge for many drivers. Since computer systems can automatically identify pedestrians in images and videos, AI-equipped cars can significantly improve the situation. In fact, self-driving systems (or, in the future, self-driving vehicles) could allow drivers to sleep/chat without causing any traffic accidents.

Pedestrian detection is a problem for computer vision and pattern recognition because pedestrians can behave unexpectedly, thus presenting many edge cases in terms of data. As a result, these lead to problems in predicting behavior, which is one of the biggest threats to the success of self-driving cars.

In addition, there are still many other challenges in the training data to be overcome, including different lighting parameters and different poses or clothing displayed by pedestrians. To overcome these problems, artificial intelligence needs a large amount of training data, which takes a lot of time to acquire.

Management of traffic lights

In order to simplify traffic, artificial intelligence should be applied in traffic management to make roads smarter and more environmentally friendly.

Using computer vision machine learning, artificial intelligence processes, controls and optimizes large amounts of data from multiple sensors and cameras installed on roads. Artificial intelligence and big data systems analyze that data to reveal traffic patterns. Relevant insights provide input to intelligent systems to predict traffic or road blockages. Using these inputs, the AI detects and predicts issues that may lead to congestion.

Traffic signaling and intelligent transportation system technology play an important role in road safety. For this, the timing and configuration of the traffic lights is necessary. For example, increasing pedestrian crossing green light intervals improves pedestrian safety and reduces traffic.

Innovative artificial intelligence solutions include intelligent traffic monitoring and control systems to manage speed, provide lane departure warnings and exchange information with city traffic control systems. Today, vehicles interact with each other and road infrastructure. This interaction, called Cooperative Intelligent Transport Systems (C-ITS), means that data from these interactions can be shared with traffic managers. Vehicle-to-vehicle communication channels and vehicle-to-infrastructure communication channels are used for emergency braking warnings, distance sensing, inappropriate driving detection, collision avoidance systems, weather-related skid warnings, and optimal intersection management.

Travel time forecast

Delay is another major problem of transportation, especially air transportation. These delays are costly for both individuals and airlines.

Implementing artificial intelligence shows a way to overcome the costs of flight delays while addressing negative passenger experiences. AI systems can reduce passenger wait times by being able to predict the short-term effects of almost anything, from stormy weather to a certain number of technical issues that might cause flight delays. By processing aircraft data, historical records, and real-time weather information, artificial intelligence uses machine learning to reveal hidden patterns and provide the airline industry (and passengers) with valuable information about the possibilities that might cause delays or cancellations. he does.

However, computer vision systems can also alternately monitor cars, trucks, and buses and predict delays. In addition, the cameras are relatively easy to install and maintain, and the videos provide a clear and intuitive image to people, unlike many other data collection technologies that provide numerical outputs.

In addition, AI refines time-of-arrival (ETA) predictions to closely match real-world results by training machine learning models using historical data combined with real-time signals. For example, Uber created a routing engine that uses real-time traffic measurements and map data. Predicts ETA as the sum of travel times by segment along the best route between two points. Then, the machine learning model predicts the amount of time that should be added to the routing engine’s ETA results to achieve the results observed in the real world.

Monitoring the condition of the roads

Pothole damage costs motorists over $3,000,000,000 annually in the United States alone. Unfortunately, governments fail to identify and fix road problems.

However, computer vision in transportation AI can successfully identify road defects and evaluate surrounding infrastructure by observing changes in road surfaces.

Computer vision algorithms can detect the level of road damage and alert relevant authorities to improve road maintenance.

Algorithms collect image or video data and then process that data to identify cracks and even classify them automatically. In addition, these algorithms will soon implement targeted rehabilitation techniques and automated preventive maintenance without human intervention.

In other words, automatic asphalt pavement hazard detection (PD) improves road maintenance allocation efficiency while increasing road safety, providing immediate updates for faster repair and saving time and money.

For example, EyeVi uses computer vision and machine learning in transportation to solve the problem of road surface damage.

Parking management with computer vision

Finding parking in the busy streets of big cities is very difficult. On the other hand, this work is usually stressful (as well as harmful to the environment) and increases traffic congestion.

Computer vision can rework parking management. First, parking lots must have sensors that measure the distance between cars to monitor each available spot. However, since such a sensor cannot scan license plates, it is time for cameras, parking meters and computer vision to get involved.

Using automatic license plate recognition, artificial intelligence accurately identifies parked cars and also measures how long they have been parked.

The system can then use this data to update the map of all vacant and soon-to-be-available slots in real-time. Drivers can then use the map on their mobile device to quickly find empty parking spaces with low occupancy levels, saving a significant amount of time.

Automatic detection of traffic accidents and law enforcement

Due to the importance of this issue, traffic accident detection is one of the best fields of artificial intelligence transportation research. The main objective is to ensure minimal disruption to traffic flow.

For a long time, video surveillance was the most efficient tool for tracking road networks and intersections. The system provides an instant view of traffic and allows authorities to respond to incidents as quickly as possible.

However, it was humans who watched the videos and their abilities were limited. It is impossible for one person to monitor several cameras at the same time with the same efficiency, and as a result incidents are not detected for critical moments.

This is where automatic incident detection comes into play. The computer vision system looks for incidents, queues and unusual traffic conditions in the video and constantly monitors all the cameras. For example, Motorola Solution offers an AI-powered user interface to ensure that important events don’t go unnoticed. Moreover, this artificial intelligence in the transportation industry can even predict future traffic problems.

For example, MindTitan developed a traffic accident prediction model in collaboration with the Estonian Road Administration. This system uses data such as violations, accidents, weather conditions, location and time of police patrols, etc. Based on these data, the prediction model should predict the risk, severity and main cause of traffic accidents. Therefore, AI can improve road safety and reduce traffic congestion.

Another example is an artificial intelligence project developed in Bellevue (WA), USA. Based on more than 5,000 hours of video footage, the researchers identified accurate predictors of crash locations. The artificial intelligence model identifies traffic hotspots in the city network by processing data from high-quality 360-degree traffic cameras installed at 40 intersections. These cameras provide data on traffic volumes, vehicle speeds, and traffic signs close to the crash.

Another area of AI influence in transportation is law enforcement. Previously, because vehicles traveled at high speeds and the detection of dangerous driving depended on human observation, police were usually involved after an accident occurred. However, artificial intelligence has solved this problem. Smart systems help authorities detect people who are driving, drinking or using mobile phones and alert nearby officers to intercept them and prevent accidents before they happen.

In addition, these smart systems can predict the best place for police patrols. MindTitan is developing an artificial intelligence system for the Estonian Police and Border Guard Board (PBGB) that can predict emergency calls based on a number of factors, from historical data of emergency calls and issues to weather conditions on site. slow nose

Automatic license plate recognition

Automatic license plate recognition uses computer vision systems that analyze video from highway and street cameras to identify the license plate number and simultaneously mark the location, date and time.

Then, a central server processes those images and recognizes the numbers and letters with optical character recognition (OCR).

It is commonly used by the police to help locate vehicles. For example, within seconds, license plate recognition will help determine whether a vehicle was at a crime scene at a particular time.

In addition, this technology can assist with traffic, parking or toll management by identifying travel patterns.

However, automatic license plate recognition is often seen as controversial. Some argue that automatic license plate recognition can reveal private information about a driver’s life. Hence, it is wise to use this technology carefully.

Monitor the driver

According to the government, in 2020, 633 deaths from crashes caused by drowsy driving occurred in the United States alone. The UK government has described driver fatigue as “one of the main areas of driver behavior that needs to be addressed”. In Iran, many road accidents happen due to sleepiness.

Even if personal responsibility is introduced, according to these alarming statistics, it is not enough. Unfortunately, just asking drivers to take extra care won’t do. Many drivers do not like to admit that they are tired or even deny that it affects their ability to drive.

For safer driving and better monitoring, companies are using computer vision and adding video equipment to the car cabin. Using facial recognition and state estimation, this technology pays attention to things like sleepiness and emotional recognition. By alerting the driver and advising them to stop and rest, AI will prevent hundreds of accidents and deaths each year and provide a safer road for every passenger.

The same warning can appear when driver distraction is detected. At the moment of distraction – for example, when a mobile device is being used – the AI system can immediately alert the driver and ask them to focus on the road. The AI system can also detect other distractions, such as chatting with a backseat passenger, that disrupt their concentration without the driver noticing.

Advantages of artificial intelligence in the transportation industry

While the application of AI continues to vary across geographies, the adoption of AI technologies has had a major impact on the entire transportation industry.

As an essential part of smart cities (urban ecosystems that emphasize the use of digital technology and shared knowledge for the benefit of public safety, health, mobility, and productivity), AI can improve urban life in many ways.

The list of benefits is already extensive, however AI is still expanding and there will be benefits to it as well. The following are the most important advantages of artificial intelligence in transportation:

The increased use of artificial intelligence ensures lower labor costs while bringing greater profits.

Artificial intelligence has a great impact on safety and monitoring and predicting traffic accidents. In addition, data analysis in logistics helps to improve transportation planning and increase safety in general.

By using historical data about traffic and other relevant details, traffic management becomes more effective.

Logistics sectors and businesses benefit from the extensive infrastructure of implementing artificial intelligence with real-time traffic detection to optimize the route, minimize waiting time, etc.

And in the end that…

Artificial intelligence in the transportation sector can improve everyday life in many ways, from easier and more convenient traffic management and passenger safety to reducing carbon emissions. AI capabilities allow us to process complex data and automate time-consuming tasks such as continuous traffic flow monitoring. Therefore, drivers can be sure that the traffic is observed with superhuman supervision to prevent problems and increase safety.

In the aviation industry, AI-enabled systems can increase revenue generation through intelligent fleet management by identifying hidden patterns in traffic data based on bad weather and predicting delays.

However, since the transportation sector is a complex area with many influencing factors, it is essential to take a closer look with a team of machine learning experts.

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