Vehicle Water Splash Detection Using Waveform Analysis
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Solution Overview
Problem
Vehicle water splashing on the windshield can obstruct the driver's view during heavy rain, leading to potential collisions due to sudden steering or braking, and continuous splashing on specific road surfaces affects following vehicles.
Innovation Solution
An apparatus using deep learning algorithms to analyze image data from cameras, determining the dangerousness of water splashing based on waveform characteristics, and implementing vehicle control measures such as warning outputs, path adjustments, and communication with nearby vehicles to avoid hazardous situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning algorithms and camera systems are used to detect water splashing, then driver safety and collision prevention are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary detection of water splashing patterns using camera imaging before the splashing reaches the windshield. By identifying the waveform characteristics and trajectory of water droplets in advance, the system can predict potential hazards and prepare appropriate responses, improving safety without requiring overly complex real-time processing during critical moments
Solution Approach 2:
The patent introduces an intermediary processing layer that captures raw camera images, extracts waveform features, and translates them into actionable safety decisions. This intermediary stage using image processing algorithms acts as a mediator between the simple camera input and complex safety responses, reducing the overall system complexity while maintaining high reliability
2Measurement precision
If real-time image processing and deep learning are implemented, then water splashing detection accuracy is improved, but processing time and computational energy consumption increase
Solution Approach 1:
The system extracts only the essential waveform features from water splashing images rather than processing entire high-resolution images. By isolating and analyzing specific characteristic patterns such as droplet trajectories and splash waveforms, the system achieves high detection accuracy while significantly reducing computational time and energy requirements
Solution Approach 2:
The patent applies partial processing by focusing computational resources only on regions of the image where water splashing is detected or likely to occur. Rather than analyzing the entire field of view with equal intensity, the system selectively processes relevant areas, maintaining accuracy while minimizing processing time
3Reliability
If vehicle control measures such as automatic braking or path adjustment are implemented, then collision avoidance is improved, but risk of sudden maneuvers and loss of vehicle stability increase
Solution Approach 1:
The system applies preliminary countermeasures by detecting water splashing patterns that indicate potential hazards before the vehicle actually encounters them. By identifying waveform characteristics that precede dangerous splashing events, the system can prepare gradual control adjustments in advance, preventing collisions while maintaining vehicle stability through smooth, anticipatory corrections rather than sudden reactive maneuvers
Solution Approach 2:
The patent implements a cushioning approach by providing advance warning and gradual control adjustments before critical situations arise. When water splashing patterns indicate potential danger, the system prepares the vehicle control system with buffered, progressive adjustments rather than immediate full-force interventions, cushioning the transition and maintaining stability while still preventing collisions
Data Source
AI summary
An apparatus for responding to vehicle water splashing includes a processor determining the vehicle water splashing based on image data of a nearby vehicle and determining dangerousness caused by the vehicle water splashing to perform vehicle control and storage storing information determined by the processor and the image data of the nearby vehicle.