Lane Line Reconstruction Using Future Scenes for Occlusion Detection
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Solution Overview
Problem
Conventional autonomous control systems for vehicles struggle to detect lane lines that are occluded by objects or located near the vanishing points of the horizon, making it difficult to navigate accurately.
Innovation Solution
A vehicle-mounted camera system with a lane detection system that uses a training module to process images, reconstruct occluded lane lines, and predict their position using machine-learning algorithms, enabling real-time lane detection and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional autonomous control systems use traditional image processing methods to detect lane lines, then the system structure remains simple, but the system cannot accurately detect lane lines that are occluded by objects or located near the vanishing points
Solution Approach 1:
The patent introduces future frames as an intermediary to help detect current lane lines. By using trajectory information from future frames where lane lines are visible, the system can predict and reconstruct occluded lane lines in the current frame, improving detection accuracy without requiring complex real-time processing of occluded regions
Solution Approach 2:
The system performs preliminary detection and trajectory prediction using future frames before the vehicle reaches the occluded region. This allows the lane line reconstruction to be based on pre-computed trajectory information, reducing the complexity of real-time detection in occluded areas
2Measurement precision
If the system uses machine learning algorithms to predict lane lines in occluded regions, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system uses future frames to pre-compute trajectory information and predict lane line positions before the vehicle reaches occluded regions. This preliminary action allows the machine learning model to work with pre-processed trajectory data rather than raw occluded images, reducing processing time while maintaining accuracy
Solution Approach 2:
The patent extracts only the essential trajectory information from future frames (lane line positions and vehicle state) rather than processing complete images. This extraction approach reduces the data volume for machine learning processing while preserving the critical information needed for accurate lane line prediction
3Adaptability or versatility
If the system relies solely on current frame image data to detect lane lines, then the processing is straightforward, but the system cannot anticipate lane curvature near vanishing points
Solution Approach 1:
The patent adds the time dimension by incorporating future frames into the detection process. Instead of analyzing only the spatial information in the current frame, the system uses temporal information from multiple frames to predict lane curvature and trajectory, enabling the vehicle to anticipate lane changes before reaching vanishing points
Solution Approach 2:
The system performs preliminary trajectory analysis using future frames to predict lane curvature in advance. This allows the autonomous control system to prepare for upcoming lane changes and navigate smoothly through curved sections, improving adaptability without requiring complex real-time reactions
Data Source
AI summary
A vehicle capable of autonomous driving includes a lane detection system. The lane detection system is trained to predict lane lines using training images. The training images are automatically processed by a training module of the lane detection system in order to create ground truth data. The ground truth data is used to train the lane detection system to predict lane lines that are occluded in real-time images of roadways. The lane detection system predicts lane lines of a roadway in a real-time image even though the lane lines maybe indiscernible due to objects on the roadway or due to the position of the lane lines being in the horizon.