Sound-Based Traffic Detection for Adaptive Lighting Control
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Solution Overview
Problem
Current lighting control systems for public and private spaces are inefficient in terms of energy usage due to over-lighting, lack of real-time traffic data, and high costs associated with visual perception-based systems.
Innovation Solution
A predictive and adaptive lighting control system utilizing artificial intelligence based on sound to learn traffic data, adjust lighting settings, and predict events such as vehicle turns or stopping, thereby optimizing energy use and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If visual perception systems (cameras) are used to detect traffic and adjust lighting, then lighting can be adapted to actual traffic conditions, but the system cost increases significantly and the sensing radius is limited
Solution Approach 1:
The patent replaces visual perception systems (cameras) with acoustic sensing systems (microphones). Sound waves propagate differently than light, allowing detection over longer distances and through obstacles. The acoustic field serves as the sensing medium instead of the visual field, fundamentally substituting the detection mechanism while maintaining the ability to detect traffic presence and characteristics
Solution Approach 2:
The lighting device integrates multiple functions: it provides illumination, contains acoustic sensing capabilities (microphone), and includes processing unit for traffic detection. This multi-functionality eliminates the need for separate camera systems, reducing overall system complexity and cost while achieving the same traffic adaptation goal
2Measurement precision
If visual perception systems are used to detect traffic, then lighting can be adjusted to actual conditions, but the system cannot effectively detect stopped vehicles or conversations
Solution Approach 1:
The patent substitutes acoustic sensing for visual sensing. Acoustic waves can detect stationary objects and activities that produce sound (such as conversations or engine idling) without requiring movement or reflection of light. This enables detection of stopped vehicles and human activities that visual systems would miss
Solution Approach 2:
The system changes the detection parameter from visual (optical) to acoustic (sound frequency and intensity). By monitoring sound characteristics rather than light reflection, the system can identify different traffic scenarios including stopped vehicles (engine noise), conversations (human voice frequencies), and moving traffic, providing more comprehensive detection capabilities
3Reliability
If cautionary ratios are applied in lighting design to ensure adequate illumination, then lighting safety is improved, but energy waste increases at unacceptable levels
Solution Approach 1:
The patent implements dynamic lighting control where illumination levels are continuously adjusted based on real-time traffic detection. The lighting system transitions from static over-illumination to dynamic adaptation, modifying light output according to actual traffic presence and intensity. This maintains safety during high-traffic periods while reducing energy consumption during low-traffic periods
Solution Approach 2:
The system incorporates feedback loops where acoustic sensors continuously monitor traffic conditions and feed this information back to the lighting control unit. Based on this feedback, the system automatically adjusts illumination levels to match actual traffic needs, eliminating the need for cautionary over-lighting while maintaining adequate safety margins
Data Source
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AI summary
A lighting system comprising a plurality of controllable lighting devices (15) distributed in an area to be illuminated and an artificial intelligence (20) adapted to learn traffic data and to determine the lighting settings of said plurality of controllable lighting devices (15), wherein said artificial intelligence (20) comprises: - a plurality of intelligent devices (22) arranged in said area, each associated with at least one of said controllable lighting devices (15), wherein each intelligent device (22) comprises sound sensing means (25) adapted to acquire sound data and a local neural network (21a) adapted to learn traffic data based on the acquired sound data, to predict local traffic events and to process local lighting commands, within predetermined lighting setting limits, for the at least one associated controllable lighting device, - at least one general neural network (21b) remote with respect to the plurality of intelligent devices (22), and - sound training means comprising image sensing means (30), sound sensing means (25) and a neural training network (21c) cooperating therewith to associate simultaneously sensed image data and sound data to create sample soundtracks (18) of a same scene to thereby teach the artificial intelligence (20) to recognize and classify traffic sounds, wherein said at least one general neural network (21b) is adapted to receive traffic data from said plurality of intelligent devices (22) and to process said traffic data to modify the lighting setting limits of the plurality of controllable lighting devices (15), wherein said at least one general neural network (21b) comprises transmission means adapted to transmit said modified lighting setting limits to the plurality of intelligent devices (22).