Recurrent Neural Network Lane Detection for Road Widening Zones
Solution Overview
Problem
Existing advanced driver-assistance systems (ADAS) struggle to accurately detect road lane changes due to road widening or narrowing, leading to potential vehicle misalignment and safety issues.
Innovation Solution
A method utilizing a recurrent neural network (RNN) to detect singular zones on a road where the number of traffic lanes increases or decreases, by processing vehicle position data relative to lane separation lines and computing detection parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional guidance systems detect lane position based on line markings, then the system can provide basic lane centering assistance, but the system makes mistakes when road widening or narrowing occurs
Solution Approach 1:
The patent changes the detection parameter from simple line position to singular zone detection using recurrent neural networks. The system detects variations in lane width patterns over time to identify singular zones (road widening/narrowing) and adjusts guidance behavior accordingly, improving reliability without proportionally increasing complexity
Solution Approach 2:
The patent replaces traditional mechanical/optical line detection methods with a neural network-based detection system. The recurrent neural network processes sequential position data to detect singular zones, substituting conventional detection mechanics with intelligent pattern recognition
2Measurement precision
If the system detects road widening by analyzing line position variations, then it can adjust vehicle path, but the system may still follow incorrect paths due to poor line detection
Solution Approach 1:
The patent implements feedback by continuously monitoring lane width variations and using recurrent neural networks to analyze patterns over time. The system compares detected line positions with expected patterns to identify singular zones, providing feedback that improves detection precision and guides the vehicle correctly through road widening or narrowing sections
Solution Approach 2:
The patent performs preliminary detection of singular zones before the vehicle reaches problematic areas. The recurrent neural network analyzes upcoming road geometry changes in advance, allowing the system to prepare appropriate guidance adjustments before the vehicle enters the singular zone, improving both precision and reliability
3Productivity
If the LCA system operates continuously without deactivation, then it provides constant assistance, but it may cause vehicle misalignment in singular zones
Solution Approach 1:
The patent makes the LCA system dynamic by adjusting its behavior based on detected singular zones. The system maintains continuous operation but modifies its control parameters when singular zones are detected, allowing it to adapt to road geometry changes while maintaining high availability and positioning accuracy throughout the driving process
Data Source
AI summary
A method is for detecting the route taken by a motor vehicle in a particular zone within which the number of lanes increases or decreases. The method includes recording position data characterizing the position of the motor vehicle in relation to the lane separation lines marked on the road and calculating, via a recurrent neural network, at least one parameter for the detection of the particular zone, the recorded position data being supplied as input to the recurrent neural network