Event-Based Lane Line Detection With Optical Flow Search Regions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing lane line detection technologies face high computation burden and speed limitations, necessitating a more efficient and robust method for rapid detection.
Innovation Solution
Utilizing a Dynamic Vision Sensor (DVS) to generate optical flow images from event data, determining search regions, and fitting lane lines without edge detection, reducing computation burden and improving robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane line detection methods are used, then the system is simple to implement, but the detection precision deteriorates in complex road environments
Solution Approach 1:
The patent segments the lane detection task into multiple specialized modules: lane line detection unit, road region determination unit, and label generation unit. Each module handles a specific aspect of the detection process, allowing the system to achieve high precision in complex environments while maintaining manageable system complexity through functional decomposition.
Solution Approach 2:
The patent introduces an intermediary road region determination unit that processes images to identify road regions before lane line detection. This intermediary step filters out non-road elements and focuses the detection algorithm on relevant areas, improving precision without requiring a complete redesign of the entire detection system.
2Measurement precision
If more detection algorithms are applied to improve accuracy, then detection precision improves, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary road region determination before executing the full lane line detection algorithm. By pre-identifying road regions and filtering the image data beforehand, the system reduces the computational burden of subsequent detection operations, achieving high precision with lower overall resource consumption.
Solution Approach 2:
The patent applies detection algorithms selectively to identified road regions rather than processing the entire image. This partial action approach concentrates computational resources on areas where lane lines are likely to exist, improving detection precision while reducing overall computational resource consumption.
3Reliability
If the detection system processes all image data, then comprehensive detection is achieved, but processing time increases
Solution Approach 1:
The patent segments the image processing task by first identifying road regions and then applying lane line detection only to those regions. This segmentation maintains detection reliability by ensuring thorough analysis of relevant areas while reducing processing time by excluding non-road areas from detailed processing.
Solution Approach 2:
The patent performs preliminary road region identification to determine where lane lines are likely to exist before executing the full detection algorithm. This preliminary action filters out irrelevant areas, allowing the system to maintain comprehensive detection of actual lane lines while significantly reducing overall processing time.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure provides a method for detecting a lane line, a vehicle and a computing device. The method includes: generating an optical flow image in accordance with a series of event data from a dynamic vision sensor coupled to a vehicle, each event being triggered by movement of an object in a scenario relative to the dynamic vision sensor; determining an initial search region including a start point of the lane line in accordance with the optical flow image; determining a center of gravity of the initial search region; determining a new search region through an offsetting operation on the center of gravity; determining a center of gravity of the new search region; repeating the steps of determining a new search region and determining a center of gravity of the new search region iteratively to acquire centers of gravity of a plurality of search regions; and determining the lane line in accordance with the centers of gravity of the plurality of search regions.