The core tasks of AI image processing in autonomous driving primarily involve enabling vehicles to perceive and understand their surrounding environment through visual data. These tasks are critical for safe navigation, obstacle avoidance, and decision-making. The main AI image processing tasks include:
Object Detection: Identifying and locating objects such as pedestrians, vehicles, traffic signs, traffic lights, and cyclists within the camera's field of view. This helps the autonomous vehicle to understand what is around it.
Example: A self-driving car uses cameras to detect a pedestrian crossing the street ahead and adjusts its speed or stops accordingly.
Semantic Segmentation: Dividing an image into multiple segments and assigning a meaningful label to each pixel, such as road, sidewalk, building, or sky. This allows the vehicle to understand the layout of the environment in detail.
Example: Semantic segmentation helps distinguish between the drivable road and non-drivable areas like grass or sidewalks.
Lane Detection: Recognizing and tracking lane markings on the road to ensure the vehicle stays within its lane. This is essential for lane-keeping and lane-changing maneuvers.
Example: The system detects solid and dashed lane lines and keeps the vehicle centered within the correct lane.
Object Tracking: Continuously monitoring the movement of detected objects over time to predict their future positions. This is vital for understanding the behavior of nearby vehicles or pedestrians.
Example: Tracking the movement of a car approaching from behind to anticipate potential overtaking actions.
Depth Estimation / 3D Perception: Estimating the distance of objects from the vehicle using stereo vision or monocular depth estimation techniques. This helps in understanding the spatial arrangement of the environment.
Example: Estimating how far a stopped vehicle is ahead to determine a safe following distance.
Traffic Sign and Signal Recognition: Detecting and interpreting traffic signs (like stop signs or speed limits) and traffic light states (red, yellow, green) to ensure compliance with traffic rules.
Example: Recognizing a red traffic light and triggering the vehicle to come to a complete stop.
In the context of implementing these AI image processing tasks at scale, cloud-based platforms can provide powerful support for training deep learning models, processing large datasets, and deploying real-time inference services. For instance, Tencent Cloud offers AI and machine learning services such as Tencent Cloud TI-ONE (AI Platform for Training) and TI-EMS (AI Model Management & Serving), which can be used to develop, train, and deploy computer vision models for autonomous driving applications. Additionally, Tencent Cloud CVM (Cloud Virtual Machines) and GPU instances provide the computational power needed for intensive image processing tasks during both training and inference phases.