How Traffic Barricades Could Help Autonomous Cars Drive City Streets

For human drivers, road construction is probably one of the most annoying things they have to deal with on a regular basis. For autonomous vehicles, this can also be an issue– a severe one. Fortunately, traffic barricades could help autonomous cars drive the city streets, and here’s how.

Traffic Sign Recognition

Autonomous vehicles are capable of recognizing traffic signs as they pass by it. One of the most important ones is the barricades because it serves as a guide to these vehicles of the direction they are allowed to go.

A method that relies on radar sensors, as well as visual information fusion is being proposed, and hopefully, be installed in barricades. This algorithm is composed of 2 parts, such as finding if there are any hindrances along the way and determining whether it’s a traffic tool or not.

Homography is being used to calibrate camera and radar, which allows the radar data to be mapped to the image, and a corresponding image patch can be analyzed from this.

There’s also a method that works on a contour feature known as the chamfer matching which is utilized to figure out if the obstacle in the image patch is a form of the barricade. This approach is being evaluated on driverless vehicles to see how it could work.

Digitization and Simulation

Digitization and simulations of real-world driving are another way that would ensure that a multi-vehicle system on the road is entirely safe in real time. Wherein, driverless cars would continue to evolve the longer they stay on the way through the help of decision-making algorithms.

Together with reinforced learnings that could only be implemented once they can understand and adequately respond to the different environment, such as slowing down when there are barricades or when a traffic light begins to change. Taking things even further, machine learning can be carried out at massive scale by using simulated driving scenarios.

Basically, instead of placing seven different cars using various algorithms, it’s now possible to put them in high fidelity simulations which would ensure that they would all function accordingly. Through this, it would be possible to pinpoint the problem before they even arise, creating a safe and effective testing ground.

Autonomous vehicles are being developed to be able to recognize traffic signs, barricades, and like while on the road– this is very important to ensure safety at all times. Just like what has been mentioned earlier, traffic barricades serve as an essential guide for cars, giving them the idea of where to go– they are embedded with sensors.

The algorithm is often composed of two parts– finding the exact location where the issue is in the image, and recognizing its type. Homography is used in this, as it allows the calibration of camera and radar. That means the radar data can easily be mapped to the image, and the small corresponding image patch can easily be removed.

Likewise, a strategy that depends on the contour feature, known as chamfer matching is also utilized to figure out whether the problem is in the area is a barricade. This approach is being tested on driverless vehicles to ensure that it would work efficiently.