Autonomous Driving

Optical imaging technology is a core component of computer vision systems. By equipping autonomous vehicles with cameras and sensors, vehicles can capture real-time image information about the surrounding environment. Utilizing techniques such as image processing, object detection, and deep learning, autonomous driving systems can identify various targets such as road signs, vehicles, and pedestrians, thereby enabling intelligent driving decisions.

Optical sensors such as LiDAR (Light Detection and Ranging) and cameras are used to achieve high-precision perception of the surrounding environment. LiDAR emits laser beams and measures the time it takes for them to be reflected back to obtain distance information, thereby constructing a three-dimensional map of the surrounding environment. Cameras provide more intuitive environmental information and can assist vehicles in target detection and path planning.

Based on the environmental information obtained from optical sensors, autonomous driving systems can make intelligent driving decisions, including vehicle speed control, lane keeping, obstacle avoidance, etc. By combining optical sensor data with real-time algorithms, autonomous vehicles can achieve efficient and safe autonomous driving behavior.

Autonomous vehicles can utilize image data captured by optical devices such as cameras to update digital maps. This image data can be used to update map information such as roads, buildings, and traffic signs, thereby enhancing the perception and understanding capabilities of autonomous driving systems.

Optical cameras can also be used to monitor the driver’s status, including concentration level and fatigue detection. By continuously monitoring the driver’s status in real-time, autonomous driving systems can alert the driver to take necessary action or automatically take control measures to ensure driving safety.