Object-Based Lane Boundary Tracking Through Occluded Traffic
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Solution Overview
Problem
Existing lane detection methods fail to accurately track lane boundaries in crowded traffic conditions, particularly when vehicle sensors lose detection for prolonged periods.
Innovation Solution
A method involving object-based tracking of road elements using metadata and movement information, enabling estimation of lane boundaries even when temporarily obscured, and generating polynomial representations of lane boundaries based on tracked segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If curve fitting algorithm is used for lane detection, then lane boundaries can be approximated when sensed, but the algorithm fails when lane boundary is not sensed for prolonged periods in heavy traffic
Solution Approach 1:
The system performs preliminary actions by storing metadata about previously detected road elements (lanes, obstacles, traffic signs) and their spatial relationships before they become obscured. When lane boundaries are temporarily concealed in heavy traffic, the system retrieves and utilizes this pre-stored metadata to maintain continuous tracking, preventing information loss and ensuring reliability without requiring real-time sensing of obscured segments.
2Reliability
If object-based tracking with metadata is implemented, then lane boundaries can be tracked during driving, but the system complexity increases
Solution Approach 1:
The system segments the lane boundary tracking problem into distinct components: detecting road elements (lanes, obstacles, traffic signs), extracting their metadata (position, orientation, shape parameters), and using polynomial functions to represent lane boundaries. This segmentation allows each component to be handled independently with specialized algorithms, managing complexity while ensuring continuous tracking through the use of separate detection and estimation modules that work together.
Data Source
AI summary
A method for monitoring road markings in an environment of a vehicle, the method includes (a) receiving by a processing circuit, information about the environment; (b) identifying, based on the information about the environment, a road marking that is located within the environment; and (c) applying an object-based approach, on the road marking using the identified information.