Lane Stitching Control for Gapped Road Markings
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining lane tracking when lane lines are intermittent or have irregular gaps, disrupting the availability of autonomous features.
Innovation Solution
A system and method for stitching virtual lane markings using sensor data from front-facing cameras, identifying gaps in lane lines, determining points and curves using Bezier curve computations, and generating lane stitching data based on correlation measures to control vehicle components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lane line detection is used for autonomous driving control, then vehicle navigation accuracy is improved, but autonomous driving reliability deteriorates when lane lines are intermittent or have gaps
Solution Approach 1:
The system performs preliminary actions by detecting lane lines before gaps occur and predicting their continuation. The lane line detection module identifies lane lines in advance, and the gap detection module predicts where gaps will occur, allowing the system to prepare virtual lane markings before the actual gap is encountered, maintaining continuous navigation guidance.
Solution Approach 2:
Virtual lane markings serve as an intermediary element between detected lane lines and autonomous driving control. When physical lane lines are interrupted by gaps, the virtual lane markings generated through curve fitting and extension act as a mediator to bridge the discontinuity, providing continuous guidance to the path planning module without relying on incomplete physical markings.
2Duration of action of stationary object
If virtual lane markings are generated to connect gaps, then autonomous driving continuity is improved, but computational complexity increases
Solution Approach 1:
The system uses curve fitting with quadratic or cubic functions to model lane lines, naturally handling curvature and gaps. By representing lane lines as continuous mathematical curves rather than discrete line segments, the system can smoothly extend lines through gaps using parametric equations, reducing the need for complex piecewise calculations while maintaining continuity.
Solution Approach 2:
The system changes parameters by detecting gap characteristics (length, position, orientation) and adjusting curve fitting parameters accordingly. When a gap is detected, the system modifies the extension distance and curvature parameters of the lane line model to bridge the gap appropriately, allowing flexible adaptation to different gap conditions without requiring completely different algorithms.
3Loss of information
If gap detection and virtual stitching is performed, then lane line availability is improved, but processing time increases
Solution Approach 1:
The system segments the lane detection process into distinct modules: lane line detection, gap detection, virtual lane marking generation, and path planning. Each module processes specific aspects independently, allowing parallel computation where possible. The gap detection module only activates when lane line discontinuities are detected, avoiding unnecessary processing during continuous lane conditions.
Solution Approach 2:
The system applies partial action by performing virtual lane marking generation only when and where gaps are detected, rather than processing entire lane sequences uniformly. The extension distance is calculated based on gap size, applying just enough computational effort to bridge the specific discontinuity without over-processing surrounding continuous segments.
Data Source
AI summary
A system and method are provided for controlling a vehicle. In one embodiment, a system includes: a sensor system configured to generate sensor data sensed from an environment of the vehicle; and a control module configured to, by a processor, based on the sensor data, identify a gap in a lane line on a roadway in front of the vehicle, determine at least two points within the gap, determine a curve between the at least two points, compute a correlation measure based on the curve, generate lane stitching data based on data based on the curve and an evaluation of the correlation measure, and controlling one or more components of the vehicle based on the stitching data.