Blind Spot Warning Zoning for Right-Side Hazard Detection
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Solution Overview
Problem
Large vehicles have a significant blind spot on the right side, leading to increased risk of traffic accidents due to inadequate hazard warnings for drivers.
Innovation Solution
A method to determine the blind spot of a vehicle, identify traffic participants within it, divide the blind spot into multiple warning regions with varying hazard levels, and provide targeted warnings based on the hazard level of the specific region where the participant is located, using LiDAR and a pre-trained recognition model to enhance driving safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the blind spot is divided into multiple warning regions with different hazard levels, then the precision and relevance of warnings are improved, but the device complexity increases
Solution Approach 1:
The blind spot monitoring region is divided into multiple warning regions (first warning region, second warning region, third warning region) with different hazard levels. Each region corresponds to a specific hazard level (first, second, third hazard levels respectively), allowing the system to provide differentiated warnings based on the traffic participant's location. This segmentation enables precise warning delivery without requiring complex additional hardware.
Solution Approach 2:
Different warning regions are assigned different hazard levels and corresponding warning strategies. The first warning region (closest to the vehicle) has the highest hazard level and triggers immediate warnings, while the third warning region (farthest) has the lowest hazard level. This local differentiation of warning intensity based on spatial position improves warning relevance without increasing device complexity.
2Reliability
If differentiated warnings are provided for different hazard levels, then driving safety is improved, but the information processing complexity increases
Solution Approach 1:
The system pre-establishes the correspondence between warning regions and hazard levels before actual operation. The processing unit is configured with predetermined rules that map each warning region to a specific hazard level and warning strategy. When a traffic participant is detected, the system simply needs to determine which pre-defined region the participant occupies and execute the corresponding pre-planned warning action, significantly reducing real-time information processing complexity.
Solution Approach 2:
The system continuously monitors the position of traffic participants within the blind spot and dynamically adjusts warnings based on the detected hazard level. When a participant moves between warning regions, the system provides feedback by changing the warning intensity or type accordingly, enhancing driving safety through adaptive response while maintaining manageable processing complexity through region-based classification.
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
Accurately provides differentiated warnings based on the hazard level of the blind spot regions, improving driving safety by enhancing the precision and relevance of warnings for different traffic participants and scenarios.
Implementation Method 1
using LiDAR and a pre-trained recognition model to enhance driving safety
Data Source
AI summary
This application provides a method and an electronic device for blind spot warning. The method includes: determining the blind spot of a vehicle during travel; identifying a traffic participant in the blind spot; dividing the blind spot to obtain multiple warning regions, where the multiple warning regions have different hazard levels; determining the warning region in which the traffic participant is located among the multiple warning regions; and performing a warning corresponding to the hazard level of the warning region where the traffic participant is located.