Real-Time HDR Camera Pipeline for Autonomous Vehicle Hazard Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating unpredictable environments due to limitations such as visibility, traction, and interference from sudden darkness, indiscernible roadway markers, and intense lighting conditions, leading to potential accidents.
Innovation Solution
Implementing a real-time high-dynamic range (HDR) camera system that processes pixel values from multiple image sensors to provide a wide dynamic range view, merging images at different light levels and integrating with control systems to enable rapid detection and response to environmental features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cameras are used in autonomous vehicles, then the system structure is simple, but the vehicle cannot detect features in challenging lighting conditions such as intense sunlight or darkness
Solution Approach 1:
The camera system is segmented into multiple image sensors, each optimized for different light levels. One sensor captures images in normal lighting conditions while another sensor captures images in low-light conditions. This segmentation allows the system to maintain high detection reliability across varying lighting conditions without requiring a single complex sensor to handle all scenarios.
Solution Approach 2:
The patent merges images from multiple sensors with different exposure settings to create a composite HDR image. The processing system combines the normal light level image and the low light level image into a single high-dynamic-range image that preserves detail in both bright and dark areas, thereby improving detection reliability while managing system complexity through intelligent image fusion.
2Illumination intensity
If multiple image sensors are used to capture images at different light levels, then the dynamic range is extended, but the processing complexity increases
Solution Approach 1:
The system performs preliminary actions by capturing images at multiple light levels simultaneously using multiple sensors before the merging process. This allows the processing system to work with pre-captured data from different exposure conditions, extending the effective dynamic range without requiring complex real-time processing during image capture.
Solution Approach 2:
The patent creates multiple copies of the same scene captured at different light levels using separate image sensors. These copies are then processed and merged to produce a final HDR image. This copying approach allows the system to extend dynamic range by having redundant information from different exposure conditions, while the processing complexity is managed through systematic merging of these copies.
3Speed
If real-time HDR video is processed, then the vehicle can respond to hazards quickly, but the processing time and computational load increase
Solution Approach 1:
The system maintains continuous operation by processing HDR video in real-time through a pipeline that continuously merges images from multiple sensors. The processing system operates continuously to maintain real-time HDR video output, ensuring the vehicle can respond to hazards quickly without interruption. This continuous processing minimizes loss of time by keeping the useful action of hazard detection and response ongoing without batch processing delays.
Data Source
AI summary
The invention provides an autonomous vehicle with a video camera that merges images taken a different light levels by replacing saturated parts of an image with corresponding parts of a lower-light image to stream a video with a dynamic range that extends to include very low-light and very intensely lit parts of a scene. The high dynamic range (HDR) camera streams the HDR video to a HDR system in real time—as the vehicle operates. As pixel values are provided by the camera's image sensors, those values are streamed directly through a pipeline processing operation and on to the HDR system without any requirement to wait and collect entire images, or frames, before using the video information.