How AI is Changing Pothole Detection on Roads?

Potholes are a common issue on roads, costing American drivers approximately $3 billion annually in vehicle repairs alone, not including accidents caused by potholes.

In the UK, nearly two million potholes are fixed each year, but the aging infrastructure and increasing road usage exacerbate the problem, leading to a projected rise in pothole occurrences.

Conventional methods for detecting potholes typically involve manual inspections or driver reports, which can be slow and inaccurate. However, artificial intelligence (AI) is now helping to find potholes faster and more accurately.

What Are Potholes?

How Potholes Form?

Potholes form when water seeps into the cracks in the road. When the water freezes, it expands, causing the pavement to crack and break apart. Over time, these cracks become bigger and turn into potholes. Heavy traffic and weather changes can make the problem worse.

Why Potholes are Dangerous?

Potholes can damage cars, leading to expensive repairs. They can also cause accidents if drivers swerve to avoid them or if they lose control after hitting a pothole. Potholes cost a lot of money to fix, but it’s necessary to keep roads safe for everyone.

Old Methods of Finding Potholes

Manual Inspection

In the past, road inspectors would drive around and look for potholes. They would mark the locations of the potholes and report them for repairs. This method is slow, labor-intensive, and sometimes dangerous for the inspectors, especially on busy roads.

Reports from Drivers

Another way to find potholes is by relying on reports from drivers. When drivers see a pothole, they can report it to the local authorities. However, not all drivers report potholes, and it can take a long time for the information to reach the right people.

Special Vehicles

Some cities use special vehicles equipped with cameras to find potholes. These vehicles drive around and take pictures of the roads, which are then analyzed to identify potholes. While this method is more accurate than manual inspection, it is also expensive and can only be used once or twice a year due to high costs.

New AI Methods for Pothole Detection

What is AI?

AI, or artificial intelligence, is a type of technology that can learn and make decisions like a human. It can process large amounts of data quickly and accurately, making it ideal for tasks like pothole detection.

Using Cameras and AI

AI can use cameras mounted on cars, drones/UAV’s or streetlights to find potholes. The cameras take pictures of the road, and the AI analyzes the images to detect potholes in real-time. This system can alert road workers immediately, allowing for quick repairs.

Using LiDAR and AI

LiDAR is a technology that uses lasers to measure distances. It can create detailed maps of the road surface, which are then analyzed by AI to find potholes. Scientists use LiDAR and AI to not only detect potholes but also measure their size and depth. This information helps in planning more effective repairs.

How AI Pothole Detection Works

System Components

An AI pothole detection system includes cameras, LiDAR sensors, and computers. The cameras and sensors collect data, while the computers analyze the data to find potholes.

Collecting Data

Cameras and LiDAR sensors take images and measurements of the road. These devices can be mounted on vehicles that drive around the city, constantly scanning the roads for potholes.

Training the AI

To teach the AI to recognize potholes, it is trained using thousands of images of roads with and without potholes. The AI learns to identify the characteristics of potholes, such as their shape, size, and location on the road.

Real-Time Alerts

Once the AI is trained, it can analyze new data in real-time. When it detects a pothole, it sends an alert to road maintenance crews. This allows for quick repairs, preventing further damage and accidents.

Cost and Efficiency

AI can find potholes faster and cheaper than traditional methods. It reduces the need for manual inspections and allows for continuous monitoring of road conditions.

Real-World Examples

Chicago, Illinois

Chicago has implemented an AI system that utilizes a 9-axis sensor and a forward-facing camera mounted on city vehicles. This system detects potholes and assesses road conditions in real-time.

• Impact: The AI system has reportedly helped identify over 300 potholes during test runs, streamlining the repair process significantly.
• Cost Savings: By prioritizing repairs based on detected severity, the city has saved substantial amounts in maintenance costs, although specific figures are not disclosed.

San Jose, California

San Jose is testing AI-driven technology to identify potholes, graffiti, and other urban issues. The pilot program involves outfitting city vehicles with cameras to collect data.

• Pilot Program: So far, the city has conducted three test runs with one equipped vehicle. The program aims to detect small issues before they escalate.
• Future Plans: The city plans to evaluate the pilot’s outcomes to determine if it will expand the program, potentially setting a model for other municipalities.

Victoria, Canada

Victoria has introduced an AI-driven pothole detection system as part of a pilot program to improve road maintenance.

• Funding and Goals: The initiative is supported by increased funding for road maintenance, aiming to enhance the quality of the city’s roadways.
• Expected Outcomes: While specific statistics on pothole detection rates are not available, the city anticipates significant improvements in road safety and maintenance efficiency.

Calgary, Canada

Calgary has partnered with AI Superior to develop a system for detecting road defects, including potholes.

• Detection Accuracy: The AI system has demonstrated an impressive accuracy rate of approximately 98.3% in identifying road defects.
• Resource Optimization: By automating the detection process, Calgary aims to allocate maintenance resources more effectively, potentially reducing costs associated with manual inspections.

Challenges and the Future

Technical Challenges

AI systems need good quality data to work well. Poor lighting, bad weather, or heavy traffic can affect the accuracy of the data collected by cameras and sensors. Additionally, AI systems can sometimes make mistakes, such as confusing other road damage with potholes.

Rules and Policies

Governments need to create rules and policies for using AI on roads. This includes ensuring data privacy, setting standards for AI accuracy, and planning for the integration of these systems into existing road maintenance programs.

Future Improvements

New technologies will continue to improve AI’s ability to detect potholes. Advances in sensor technology, better AI algorithms, and increased data collection will make these systems even more effective.

Conclusion

AI finds potholes faster and more accurately than traditional methods. It saves money, improves road safety, and allows for more efficient use of resources.

AI will continue to improve and help keep our roads in better condition. As technology advances, we can expect even more efficient and accurate pothole detection systems.

Cities and governments should consider using AI for road maintenance. Investing in these technologies can lead to safer roads, fewer accidents, and lower maintenance costs.