Transportation is a key factor in modern society. Statistically, it’s also one of the main causes of death and injury, with over a million people dying in traffic accidents every year. Therefore, the need for safety improvement is paramount for this entire industry, especially with increasing urbanization and the number of vehicles on the road. Implementing Computer Vision in transportation safety can be the life-saving solution that people have been looking for.

In this article, we will discuss the changing face of transport safety resulting from Computer Vision models and applications. We will also talk about the benefits and challenges of using this technology and examine its future influenced by the increased 5G coverage.

How Is Computer Vision Used in Transportation?

Generally, Computer Vision refers to a computer’s ability to interpret and understand visual information in a way similar to the human brain. This powerful technology is widely applied in the transportation industry. It’s used in tools ranging from traffic monitoring and vehicle recognition to accident detection systems.

Multiple Computer Vision projects are mainly focused on overcoming safety challenges that abound within transportation systems. This technology has the power to reduce accidents, injuries, and even fatalities. 

At the moment, Computer Vision models are most widely applied in:

• Accident Detection
  The system can quickly detect accidents and alert emergency services to reach the site much faster.
• Speed Limit Enforcement
  Computer Vision can be applied in automated speed detection systems, which capture vehicle speeds and help enforce traffic laws.
• Object Detection
  AI object detection technology can identify obstacles on the road, such as debris or broken-down vehicles, to prevent collisions and create safer driving.
• Driver Behavior Monitoring
  In-vehicle cameras enable the analysis of driver actions, helping identify risk-prone behaviors for implementing timely corrective measures.

Promoting transportation safety with Computer Vision solutions helps address these critical safety challenges in building a safer ecosystem. Moreover, applying Computer Vision to enhance pedestrian safety can detect crosswalks and track pedestrians. This system can alert the driver when a pedestrian is near, reducing the chances of an accident, especially at busy junctions. It can also be integrated with smart traffic signals to optimize traffic flow, reducing congestion and the risk of rear-end collisions.

In addition to all this, using Computer Vision in public transport systems will go a long way in increasing passenger safety. For example, surveillance cameras with enhanced Computer Vision models can track abnormal behavior or imminent danger in real-time. They can alert the transportation authorities to take immediate action against the incident. This will provide better safety to commuters, making more people take up public conveyance. As these technologies continue to develop, people will be able to build safer and more efficient transportation networks.

The impact of computer vision and imaging in intelligent transportation systems can be game-changing. With this technology, authorities and transportation service providers can gain valuable insight into operational efficiencies and safety enhancements.

For instance, Computer Vision projects collect real-time traffic data, which allows for making dynamic adjustments in the flow of services and resources. Data-driven approaches can prioritize measures to enhance safety based on conditions and trends in the data, which constitutes proactive safety management.

This is also made possible by integrating Machine Learning algorithms that enable Computer Vision systems to learn continuously from their surroundings. Therefore, they can improve their performance over time. This continuous improvement will translate to more effective safety interventions and ensure transportation systems get safer with every passing day.

Furthermore, Computer Vision models can do much to assist in monitoring driver and vehicle performance. With cameras and sensors, transportation systems can easily detect patterns of speeding, aggressive driving, or distracted behavior. This data can be used to set up special driver training programs to reduce accident rates and improve the overall safety of road travel. 

Also, this technology can help detect any malfunction in the car, like brake failure or deflated tires, and may trigger on-time maintenance warnings that could potentially avoid an accident. More on using AI-powered technologies for predictive maintenance in this article.

Besides monitoring vehicles and drivers, computer vision can also play a critical role in pedestrian safety. Advanced systems can spot pedestrians crossing the road in high-traffic areas and alert the driver to their presence, reducing the likelihood of collisions. Integrating such systems with traffic lights and signage will make the environment more responsive to the needs of all road users, from cyclists and pedestrians to cars and public transport.

How Can Computer Vision Be Used to Improve Driver Safety?

Another vital area of concern where computer vision in transportation can contribute significantly is pedestrian safety. Computer Vision systems can be installed at crosswalks to alert drivers when pedestrians are present. Such systems would employ real-time image processing and provide motion detection around and within the crosswalk. Therefore, they make pedestrians more visible and reduce the risk of accidents.

Computer Vision projects like this include surveillance cameras and can also help city planners understand pedestrian traffic patterns. It’s necessary to strategically install crosswalks or other countermeasures in those places, preventing various kinds of hazards from happening in advance.

Additionally, Computer Vision models could enable smart traffic lights to automatically adjust their cycle timing in correspondence with real-time pedestrian activity. Analyzing foot traffic, the signal can extend crossings to accommodate high-volume times so people can cross safely and quickly. This dynamic adjustment enhances safety and helps with traffic flow since cars are less likely to be stopped unnecessarily.

Besides real-time monitoring, accident analysis is another area where applying Computer Vision in transportation safety pays dividends. Data from various intersections helps authorities find some common factors leading to or surrounding incidents involving pedestrians. The information gathered thereafter can suggest installing signs at specific places, improving lighting, or even redesigning road layouts for better pedestrian safety. This is where data-driven approaches can provide valuable insights for urban planning and policy-making to build a culture of safety in urban areas.

Traffic congestion is a barrier to safety and efficiency on roads and transportation systems. Enhancing transportation safety with Computer Vision technologies starts with optimizing traffic flow and minimizing congestion.

This technology allows for real-time traffic analysis and can provide data on vehicle counts, speeds, and congestion points. This information enables traffic management systems to dynamically adjust signal timings, redirect traffic, and even deploy personnel where and when necessary in critical areas.

Computer Vision models enhanced by Predictive Analytics look at historical trends and predict future traffic. This gives planners an edge in proactively implementing measures to prevent predicted congestion, ensuring safety and efficiency in transportation networks.

Besides these, the integration of Computer Vision in AI and transportation systems can use data from sensor- and camera-equipped vehicles to give the traffic management system a better insight into the road conditions and drivers’ behavior. The interconnectedness of devices allows for easier traffic flow in that vehicles can communicate with the traffic lights to optimize routes and reduce stops to make driving easier.

Beyond this, Computer Vision products can also go a long way in improving the safety of pedestrians and cyclists. The systems can view crosswalks and bike lanes and signal whether pedestrians or cyclists are in them to traffic lights, adjusting the length of the signal to allow safe passage. 

How Will the Integration of 5G Impact the Deployment of Computer Vision in Transportation?

Global implementation of 5G technology will revolutionize the uses of Computer Vision in transportation. In a nutshell, high speed, low latency, and massive connectivity come with boosting real-time data transmission, a crucial factor in AI and transportation systems.

For example, Computer Vision-enabled vehicles can communicate in real-time and share information about road conditions, obstacles, and flow with each other. This form of collaboration will create an intelligent transportation network where decisions are made quickly and accurately for the safety of all users.

With 5G, increased bandwidth allows the processing of volumes of data at higher rates. That is important for Computer Vision models analyzing high-definition video feeds. Higher capacity will also enable more accurate object detection and classification, which again is helpful in safety outcomes.

Moreover, with 5G integrated into the process, one can easily see how Computer Vision products will enable an ADAS, relying on real-time data, to function more effectively. The system can use this technology to monitor driver behavior, detect signs of tiredness, and even predict an accident before it happens. Such systems can run with unprecedented accuracy by harnessing the power of 5G and thus offer proactive safety measures to decrease the possibility of collisions significantly.

The impact of 5G on Computer Vision in transportation goes beyond single vehicles and extends into smart infrastructure. Incorporating traffic lights, road signs, and cameras into one cohesive network feeds data to a central platform. This will optimize traffic, reduce congestion, and improve general urban mobility. For instance, Computer Vision models adjust traffic lights in real-time according to the prevailing flow conditions, ensuring smooth passage through an intersection with minimal waiting time. This increases the efficiency of transport systems and reduces emissions, lowering the carbon footprint.

When implementing Computer Vision and imaging in intelligent transportation systems, developers usually rely on the following techniques:

• Image Segmentation
  This technique divides an image into multiple segments, allowing systems to identify and focus on relevant objects like vehicles, pedestrians, and signs. By isolating these elements, transportation safety apps can better analyze the environment and make real-time decisions.
• Object Recognition
  This technique is used to improve overall situational awareness by identifying not just standard vehicles but also bicycles, motorcycles, and even animals that represent risks.
• Object Character Recognition
  OCR is used to read texts from images, enabling the recognition of road signs and signals. Identifying speed limits, warning signs, and direction indicators enables such ADAS to warn the driver accordingly, ensuring that they obey traffic laws.
• Facial Recognition
  Driver systems use this technology to monitor attentiveness and drowsiness for in-cabin safety. It analyzes facial expressions and eye movements to detect signs of fatigue or distraction and sends alerts to keep the driver focused on the road.
• Neural Networks
  Embedding NNs allows for a much finer comprehension of complex driving environments, which is especially helpful in an urban environment with obstacles and pedestrians.
• LiDAR and Radar
  Applications that couple visual data with distance measurements create a 3D representation of the environment, allowing for superior obstacle detection and navigation. This multifaceted approach helps not only prevent accidents but also contributes significantly to the development of autonomous vehicles, where understanding surroundings for safe operation becomes the primary issue.

What Are the Challenges of Implementing Computer Vision in Real-World Transport Systems?

While the potential offered by implementing Computer Vision in transportation safety is high, realizing it in real-world systems presents many challenges. The biggest among them are:

• Data Quality
  Computer Vision models need good-quality images and videos to process accurately. Real-world scenarios often include variations in light conditions, weather, and occlusions that may affect data quality and negatively affect system performance.
• Integration into Existing Infrastructure
  Public transportation often relies on legacy systems, which may not be technically compatible with technologically advanced Computer Vision products. Smooth integration requires huge investments and proper planning.
• Ethical and Privacy Concerns
  These always arise when discussing surveillance-based systems. Striking a balance between safety benefits and individual privacy rights is a complex issue that should be considered carefully and regulated.

Is Computer Vision in Transportation Worth Investing In for Safety Systems?

Investment in transportation safety with Computer Vision technology is a prudent step for businesses that aim to improve safety, efficiency, and customer satisfaction. Deploying computer Vision models has evident advantages, from reducing accident rates to optimizing traffic management.

This is especially true as technology, particularly 5G, is still developing, and the potential for Computer Vision in transportation is increasing along with its evolution. Analyzing and acting upon real-time data can revolutionize the global transport system as a whole.

While challenging, this transition to integrate Computer Vision technologies allows transportation companies to lead in safety and innovation. By prioritizing these advancements, companies can protect not only their stakeholders but also contribute to the betterment of global transportation systems.

Computer Vision technology has become increasingly important in helping navigate the future of transportation safety. At the forefront of this transformative era, Alltegrio is happy and ready to provide your transportation company with custom AI and Computer Vision solutions. Fill out the contact form below, and let’s start your journey toward an AI-powered business revolution together.