Blind Spot Detection Using Secondary Vehicle Mirror Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicular systems cannot detect blind spots of nearby vehicles, which are influenced by varying vehicle sizes, shapes, and driver positions, leading to potential collisions due to incomplete visibility.
Innovation Solution
A blind spot detection system in a primary vehicle uses cameras, sensors, and machine learning algorithms to identify and classify nearby vehicles, determine their blind spots, and assess whether the driver of the secondary vehicle can see the primary vehicle, providing alerts to avoid blind spots through automated or assisted driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicles have different sizes, shapes, and driver positions, then each vehicle has unique blind spots, but existing systems cannot detect blind spots of other vehicles
Solution Approach 1:
The patent uses camera systems as intermediaries to capture images of secondary vehicles and their side-view mirrors. These camera images serve as the mediator through which the primary vehicle's system can indirectly observe and analyze the secondary vehicle's blind spot conditions without requiring direct communication or complex sensors on the secondary vehicle itself.
Solution Approach 2:
The system creates a visual copy of the secondary vehicle's environment by capturing images through the primary vehicle's cameras. These image copies are then processed to identify the secondary vehicle's blind spots, allowing the system to analyze blind spot conditions without physically being in the secondary vehicle's position.
2Reliability
If the system uses cameras and sensors to detect secondary vehicles and their blind spots, then collision risk is reduced, but device complexity increases
Solution Approach 1:
The camera system serves multiple functions: it detects the presence of secondary vehicles, captures images of their side-view mirrors, identifies blind spot zones, and provides visual data for collision risk assessment. This multi-functional approach reduces the need for separate specialized sensors for each function, thereby managing system complexity while maintaining high reliability.
Solution Approach 2:
The system continuously monitors secondary vehicles and provides real-time feedback about blind spot conditions to the primary vehicle's driver or automated driving system. This feedback loop enables dynamic adjustment of driving behavior to avoid blind spots, enhancing collision avoidance capability through continuous information updating.
3Measurement precision
If the system identifies side-view mirror locations to determine blind spots, then detection accuracy improves, but processing complexity increases
Solution Approach 1:
The system performs preliminary identification of side-view mirror locations in captured images before proceeding to blind spot analysis. By pre-locating the mirrors and establishing reference points, the system simplifies subsequent blind spot zone calculations and reduces the computational complexity of the overall image processing task.
Data Source
AI summary
Example blind spot detection systems and methods are described. In one implementation, a primary vehicle detects a secondary vehicle ahead of the primary vehicle in an adjacent lane of traffic. A method determines dimensions of the secondary vehicle and estimates a vehicle class associated with the secondary vehicle based on the dimensions of the secondary vehicle. The method also identifies a side-view mirror location on the secondary vehicle and determines a blind spot associated with the secondary vehicle based on the vehicle class and the side-view mirror location.