Autonomous Driving

Camera image quality testing in the automotive industry is primarily used to ensure driving safety and enhance the intelligent experience. Its core applications are concentrated in three major scenarios: driver assistance, autonomous driving, and in-cabin/outside monitoring.

1. Core Safeguard for Autonomous Driving (AD) and Advanced Driver-Assistance Systems (ADAS)
This is the most critical application scenario for image quality testing, as it directly determines the decision-making accuracy of autonomous driving systems.

• Verification of Environmental Perception Accuracy: Autonomous driving relies on cameras to identify pedestrians, vehicles, traffic signs, and lane markings. Image quality testing must ensure that cameras

  can still output clear images under extreme conditions such as intense glare, heavy rain, and dense fog, preventing recognition algorithm failures due to blurry image quality.

• Multi-Camera Collaborative Calibration: Vehicles are typically equipped with multiple cameras (front-view, side-view, surround-view, etc.). Testing must verify that the image quality parameters (e.g., resolution

  , color consistency) of each camera are uniform, ensuring seamless stitched surround-view images without discrepancies that could mislead system judgment.

• Dynamic Scenario Adaptability Testing: Testing assesses the stability of camera image quality in dynamic scenarios like high-speed driving and rapid turns, preventing information loss caused by motion blur

  or insufficient frame rates.

2. In-Cabin/Outside Monitoring and Safety Management

Primarily used for recording driving processes, preventing safety risks, and optimizing the cabin experience.

• Dashcam (DVR) Image Quality Compliance: Testing verifies video clarity under various lighting conditions to ensure critical information like license plates and road conditions can be clearly reconstructed in

  the event of an accident, meeting regulatory requirements.

• Driver/Cabin Monitoring System (DMS/OMS) Function Verification: For Driver Monitoring Systems (DMS), testing checks if cameras can clearly capture facial expressions to ensure accurate detection of behaviors

  like drowsy driving or phone use. For Occupant Monitoring Systems (OMS), it verifies if image quality supports functions such as child presence detection and object recognition.

• Support for Anti-Theft and Remote Monitoring: Testing evaluates the image quality of monitoring cameras after the vehicle is turned off, ensuring clear identification of anomalies inside or around the vehicle

  during remote viewing, providing effective visual evidence for anti-theft functions.

3. Manufacturing and Quality Control
Image quality testing is used to screen defective components before vehicles leave the factory, reducing post-sale risks.

• Component Factory Inspection: Batch testing of procured camera modules to verify if parameters like resolution, color reproduction, and distortion rate meet design standards, thereby filtering out substandard products.