How do Autonomous Vehicles Predict the Front Vehicle Movement?

Do you know more than 30 sensors are installed in an autonomous car to navigate it safely on the roads? Yes, these sensors capture real-time data and process them to identify the surrounding objects.

But how exactly the vehicle differentiates the differences between the objects?

The vehicle needs to identify the object as a vehicle, a pedestrian, or an obstacle. It is one of the essential features of an unmanned vehicle. Let us see how engineers have developed a sophisticated machine-learning algorithm to make self-driving cars more intelligent.

Why Autonomous Vehicle Detect Front Vehicle Movement?

If you notice the movement of an autonomous car on the road, you will witness that it maintains a safe distance from the surrounding vehicles. The Adaptive Cruise Control system of the car captures the motion speed of the car in front and adjusts the speed. This feature of the ADAS system can avoid collisions and keep passengers safe.

In order to achieve this, the digital car must be able to predict the movement of the front vehicle. It needs to identify the object as a vehicle and then predict its speed. Specialized machine-learning algorithms are used to detect the front vehicle movement.

Different Methods for Detecting Front Vehicle Motion

Just relying on front cameras for detecting front vehicles will not serve the purpose of unmanned vehicles. Since the vehicle moves, the background is not constant, and it becomes extremely challenging for engineers to capture the motion accurately because the traditional target detection method fails.

To solve this challenge following methods are discovered.

1. Feature-Based Method

There are various features on a car like edges, color, headlights, symmetry lines and etc. Since the vehicle is in motion, it is essential to monitor multiple features for predicting the front-vehicle movement. This method's general procedure entails coarsely detecting the front vehicle using a set of predetermined parameters, followed by post-processing the findings using additional features.

2. Motion-Based Method

Under the motion-based method, frame difference and optical flow methods are used to analyze the motion of the vehicle. The frame difference method is applicable only where the background is static. Whereas the optical flow method collects information like velocity, 3D structure, and gray value of the moving targets.

3. Multi-Vision Method

It is one of the methods that can provide the most accurate results. Canny operator and Hough transform methods are first used to detect the road area and to identify the vehicle within the road area; the shadow detection method is used. Finally, the binocular method is applied to get insights into the distance between the self-driving car and the front vehicle.

4. Model-Based Method

In this method, a large amount of data is collected to create 2D/3D models and is stored in memory. In real time, the collected data is compared to the actual image captured through the camera. This method also uses the feature-based detecting method to eliminate false targets. Kalman filter is applied in this method to calculate the distance between the vehicles in the 2D model using the edge-projection method.

Sensors Used in Autonomous Vehicles for Front Vehicle Detection

Multiple sensors are deployed in self-driving cars to accurately predict the motion of vehicles in front. These sensors allow the vehicle to understand its surroundings and also communicate with other vehicles.

The following three types of sensors are used for front-vehicle detection.

Radar Sensors

These sensors use radio waves to estimate the distance between the obstacles and the vehicles. They emit waves and calculate the time taken for them o return after hitting the obstacles. By monitoring the time taken, the system analyzes the velocity and distance between the unmanned vehicle and the obstacle.

Camera Sensors

To get a 360-degree view of the surrounding objects, autonomous vehicles have many built-in cameras. Some vehicles can have up to 12 cameras - 3 for front view, 4 for surround view, 3 for mirror replacement, and 2 cameras to monitor the driver. These cameras create realistic images of the obstacle and classify them using deep learning algorithms.

Lidar Sensors

Lidar system works similarly to Radar sensors, but the only difference is Lidar uses light waves. Lidar sensors not only detect the distance between the objects but also develops a 360-degree map view surrounding the vehicle. It can also create a 3D view of the detected obstacles and hence this system is more advantageous than Radar sensors.

Conclusion

To coordinate with different sensors and systems, many different software and machine learning algorithms are utilized in self-driving vehicles. Car detection is one of the most important features in autonomous vehicles, which still needs a lot of improvement to make it the safest mode of transport.

The self-driving car algorithm is so complicated to accurately navigate the vehicles through heavy traffic and unprecedented weather conditions. Self-driving vehicle manufacturers still need to go a long way, and many innovations are taking place to still enhance their features.

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