HCNN Lane Line Type Detection Under Weather and Debris Interference
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Solution Overview
Problem
Existing vehicle detection systems struggle to accurately predict lane lines and types under adverse conditions such as weather and debris interference, which hinders their effectiveness in autonomous driving and parking applications.
Innovation Solution
A heterogeneous convolutional neural network (HCNN) system with multiple sub-networks is employed to capture and process input images from optical sensors, performing object detection, lane line detection, and lane line type prediction, even in obscured conditions, by learning to represent data in various stages and using convolution and pooling layers for feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If typical detection systems use machine vision and optical sensors to predict lane lines, then the system can provide basic navigation assistance, but the prediction accuracy deteriorates under optical interference from weather and debris
Solution Approach 1:
The detection system is segmented into multiple independent detection modules, each specializing in detecting specific features (lane lines, vehicles, pedestrians, obstacles). Each module processes optical signals independently and contributes to the overall prediction, allowing the system to maintain reliability under optical interference by distributing the detection task across multiple specialized components rather than relying on a single vulnerable detection path
Solution Approach 2:
The detection system is designed with multi-functional capabilities to detect and process multiple types of targets simultaneously (lane lines, vehicles, pedestrians, obstacles) using the same optical sensor array. This universal detection framework allows the system to adapt to different detection scenarios and maintain robust performance across varying environmental conditions including weather and debris interference
2Adaptability or versatility
If the detection system processes multiple types of data (objects, lane lines, lane types) simultaneously, then the system provides comprehensive navigation information, but the system complexity increases
Solution Approach 1:
The comprehensive detection system is divided into segmented functional modules, where each module handles a specific detection task (object detection, lane line detection, lane type classification). This modular segmentation allows the system to achieve high adaptability and versatility while managing complexity through organized, independent components that can be developed and maintained separately
Solution Approach 2:
Multiple detection functions (object detection, lane line detection, lane type classification) are merged into a unified heterogeneous convolutional neural network framework. This integration allows the system to process multiple data types simultaneously through shared computational resources and coordinated processing pipelines, achieving versatility without linearly increasing system complexity
Data Source
AI summary
A method of predicting lane line types utilizing a heterogeneous convolutional neural network (HCNN) includes capturing an input image with one or more optical sensors disposed on a host member, passing the input image through the HCNN, the HCNN having at least three distinct sub-networks, the three distinct sub-networks: predicting object locations in the input image with a first sub-network; predicting lane line locations in the input image with a second sub-network; and predicting lane line types for each predicted lane line in the input image with a third sub-network.