Road Surface Shadow Detection Using Adaptive Headlight Patterns
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Solution Overview
Problem
Autonomous driving systems face challenges in accurately detecting objects at night due to insufficient luminous performance of existing lighting devices, which affects safety and reliability in navigation and obstacle avoidance.
Innovation Solution
A method involving projecting a light pattern on the road surface, acquiring an image of the pattern, and detecting shadows to obtain information about objects, using a processing unit to analyze the image and modify the light pattern for enhanced detection, including the use of machine learning and solid-state lighting devices like LEDs for improved object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing lighting devices are used for night autonomous driving, then the system structure remains simple, but the luminous performance is insufficient leading to poor object detection accuracy
Solution Approach 1:
The lighting device dynamically adjusts its light pattern between a first pattern (first set of luminous intensities) and a second pattern (second set of luminous intensities). This dynamic switching enables the system to optimize illumination for different detection scenarios, improving object detection accuracy without requiring multiple static lighting systems.
Solution Approach 2:
The system changes the luminous intensity parameters of the light pattern by switching between different sets of intensities. This parameter adjustment allows the lighting device to adapt to varying detection requirements, enhancing the contrast and visibility of objects on the road surface while maintaining system simplicity.
2Measurement precision
If a non-uniform light pattern is used to illuminate the road surface, then object detection may be improved in certain areas, but shadow identification becomes more complex requiring additional processing resources
Solution Approach 1:
The system performs preliminary action by projecting a uniform light pattern first to create clearly identifiable shadows. This preliminary uniform illumination simplifies shadow detection and object identification, reducing processing complexity before any subsequent pattern adjustments are made.
Solution Approach 2:
The light pattern is designed with homogeneous (uniform) illumination characteristics in its basic form. This uniformity ensures that shadows cast by objects are consistent and easily distinguishable, simplifying the image processing algorithms needed to detect and analyze objects without requiring complex computational resources.
3Illumination intensity
If additional light modules are added to improve night vision, then luminous performance increases, but the system complexity and cost increase
Solution Approach 1:
The existing lighting device is made multi-functional by enabling it to perform both standard illumination and enhanced object detection functions. By programmatically controlling the luminous intensities of existing light sources, the system achieves improved night vision capabilities without adding separate dedicated detection lighting modules, thus maintaining system simplicity.
Solution Approach 2:
The lighting device serves itself by using its own existing light sources for dual purposes: standard road illumination and active object detection. The system adjusts the operational parameters of its current lighting components to provide enhanced detection capabilities, eliminating the need for additional specialized hardware.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances safety and accuracy in object detection during night autonomous driving by providing detailed features of objects, allowing for appropriate vehicle maneuvers to avoid collisions, without requiring additional light modules or noticeable changes to the user.
Implementation Method 1
projecting a light pattern on the road surface
Implementation Method 2
acquiring an image of the projected light pattern
Data Source
AI summary
A method for detecting an object in a road surface. The method includes projecting a light pattern on the road surface, acquiring an image of the projected light pattern, detecting a shadow in the acquired image and using some features of the shadow to obtain information about features of an object. The invention also provides a method for autonomous driving using this object detection and an automotive lighting device.