Adaptive Vehicle Lighting for Object-Class Glare Control
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Solution Overview
Problem
Conventional vehicle lighting systems lack adaptive capabilities to dynamically adjust light distribution based on detected objects, leading to issues such as blinding and inadequate detection due to static light configurations and imperfect darkened tunnels.
Innovation Solution
A vehicle lighting system with sensors and machine learning capabilities that detect and classify objects, allowing for selective dimming of light sources based on object class and location to create tailored darkened regions, enhancing object detection and reducing glare.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If light sources are maintained at full brightness to ensure adequate illumination, then illumination intensity is improved, but glare and blinding of detected objects worsens
Solution Approach 1:
The lighting system dynamically adjusts light intensity based on real-time object detection and classification. The controller continuously monitors sensor data and modulates light source brightness accordingly, transitioning from static full-brightness operation to adaptive dynamic control that responds to changing environmental conditions and object presence.
Solution Approach 2:
The system applies different illumination strategies to different spatial zones and object types. Rather than uniformly dimming all lights, the controller selectively adjusts specific light sources based on object class, location, and orientation, creating localized illumination patterns that maintain visibility while reducing glare on specific objects.
2Device complexity
If static light configuration is used to simplify system design, then device complexity is reduced, but adaptability to different objects and scenarios worsens
Solution Approach 1:
The lighting system is designed with multi-functional capabilities, where a single integrated system performs object detection, classification, and adaptive light control. The controller executes machine learning models and manages multiple light sources with different functions (high beams, low beams, work lights), creating a universal system that handles various scenarios without requiring separate dedicated systems.
Solution Approach 2:
The controller serves as an intermediary between sensor inputs and light source outputs. It processes sensor data through machine learning models, determines appropriate lighting responses based on object class and context, and translates these decisions into specific light source adjustments, mediating between the detection and illumination functions.
3Object-affected harmful factors
If all light sources are dimmed uniformly to reduce glare, then glare reduction is improved, but object detection capability worsens due to inadequate illumination
Solution Approach 1:
The system applies differentiated dimming strategies to different light sources based on object characteristics. Rather than uniform dimming, the controller selectively adjusts specific light sources depending on object class, location, and orientation, maintaining adequate illumination in certain zones while reducing glare in others.
Solution Approach 2:
The system employs continuous feedback loops where sensor data about detected objects feeds back to the controller, which then adjusts light source intensity accordingly. This closed-loop control ensures that illumination levels are optimized for both detection accuracy and glare reduction, with real-time adjustments based on object presence and characteristics.
Data Source
AI summary
A vehicle light system includes a plurality of light banks connected to a vehicle and configured to project light that radiates away from the vehicle. Each light bank includes a plurality of light sources. An image sensor, such as a camera, is connected to the vehicle and is configured to generate image data corresponding to a scene about the vehicle. A controller is connected to the light banks and the image sensor. The controller is configured to receive the image data, and execute an object detection machine learning model based on the image data to detect an object, determine a location of the object, and classify the detected object. The controller is configured to then select and dim one or more of the plurality of light sources based on the output of the object detection machine learning model. The dimming can vary based on the object class.