Lane Line Prediction Using Continuous Functions
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining the location of lane lines, especially those that are outside the sensor range, around curves, or occluded by objects, necessitating systems that can infer and predict lane line locations efficiently.
Innovation Solution
A system that uses sensors to detect a first portion of lane lines, determines location points, models lane lines as continuous functions with responsibility metrics, and predicts the location of undetected portions based on these functions, utilizing iterative processing to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to directly detect lane lines, then measurement precision is improved, but the system cannot detect lane lines outside sensor range, around curves, or occluded by objects
Solution Approach 1:
The system performs preliminary detection of visible lane line portions and uses continuous function modeling to predict and infer the locations of occluded, curved, or out-of-range lane lines before they are needed for navigation decisions. This allows the system to proactively determine lane line positions that cannot be directly observed by sensors.
Solution Approach 2:
The patent introduces continuous functions as an intermediary mathematical model that connects detected lane line data points to predicted lane line positions. These continuous functions serve as mediators that infer the locations of occluded or curved lane lines by extrapolating from visible portions, enabling the system to overcome sensor limitations.
2Measurement precision
If more sensors or higher precision sensors are used to improve lane line detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
Instead of using additional physical sensors to directly detect lane lines, the system creates mathematical copies (continuous function models) of the lane lines based on limited sensor data. These computational models replicate the lane line information that would otherwise require additional sensors to detect, thereby reducing hardware complexity while maintaining detection accuracy.
Solution Approach 2:
The patent replaces the mechanical/sensor-based detection approach with a computational/mathematical approach. Instead of relying on more sensors to physically detect lane lines, the system uses continuous function modeling and prediction algorithms to infer lane line positions, substituting computational processing for additional sensing hardware.
3Adaptability or versatility
If complex prediction models are used to predict lane line locations, then adaptability is improved, but processing speed and computational efficiency decrease
Solution Approach 1:
The system transforms the lane line prediction problem by parameterizing the continuous functions with a limited set of coefficients. This parameterization allows the complex prediction task to be performed efficiently by adjusting a small number of parameters rather than performing computationally intensive operations, thereby maintaining high processing speed while achieving accurate predictions.
Data Source
AI summary
A method for predicting lane line locations, the method comprises: detecting, by one or more sensors on a vehicle, image data comprising a first portion of one or more lane lines of a roadway; determining a plurality of location points indicating locations of the one or more lane lines based on the image data; determining one or more continuous functions to respectively represent the one or more lane lines based on the plurality of location points, wherein determining the one or more continuous functions comprises determining a plurality of responsibility metrics indicating respective likelihoods that each continuous function represents a lane line; and predicting a location of a second portion of one or more lane lines on the roadway based on the one or more continuous functions and the plurality of responsibility metrics.