Description

These are deep learning algorithms that analyse visual data and classify objects in the environment. CNNs are commonly used in object detection and recognition tasks. They can learn to recognise complex patterns and shapes, making them effective in identifying objects in real-world environments.

Used to separate the environment into different regions, such as roads, sidewalks, and buildings. This helps the vehicle to understand the layout of the environment and identify objects more accurately. It works by assigning each pixel in an image to a particular class, which can help the vehicle navigate the environment more effectively.

Used to estimate the motion of objects in the environment. This information predicts the trajectory of objects, such as other vehicles or pedestrians. It works by analysing the changes in brightness between video frames to determine the direction and speed of motion. Optical flow is helpful in detecting unexpected changes in movement including sudden stops or changes in direction.

Uses two cameras to create a 3D representation of the environment. This enables the vehicle to estimate the distance to objects and plan its movements accordingly. It works by analysing the differences between the images captured by two cameras, to calculate the distance to objects in the scene. Stereo vision is useful in detecting obstacles and estimating the size and position of objects in the environment.

Used to identify the lanes on the road and ensures that the vehicle stays within them. This is typically achieved through image processing techniques, such as edge detection and Hough transforms. It works by analysing the edges in an image to identify the boundaries of the lanes. Lane detection is useful in keeping the vehicle in the correct lane and avoiding collisions with other vehicles.

Used to identify objects in the environment, such as vehicles, pedestrians, and bicycles. This is typically achieved through machine learning algorithms, such as CNNs. Object detection algorithms can locate objects in an image or video and determine their class, position, and size. They are used in a variety of tasks including collision avoidance and pedestrian detection.

Used to identify objects in the environment based on their appearance. This is typically achieved through machine learning algorithms, such as CNNs. Image recognition algorithms can classify objects in an image or video and determine their class. They are useful in detecting and identifying objects in the environment like road signs and traffic lights.

Used to track the movement of objects in the environment, such as other vehicles and pedestrians. This is typically achieved through a combination of prediction and filtering techniques to track the object’s movement. Object tracking algorithms can estimate the object’s position and velocity over time and predict its future position. They are useful in a variety of tasks, such as collision avoidance and pedestrian detection.