Curve-Fitted Landmark Detection for Low-Latency Autonomous Driving
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Solution Overview
Problem
Conventional approaches for landmark detection in autonomous driving require extensive post-processing, increasing latency and compute requirements, making them less suitable for real-time deployment due to the need for angular or directional information and pixel-level correlations for curved landmarks.
Innovation Solution
The use of deep neural networks (DNNs) to regress control points for curve fitting, combined with curve fitting operations like Bezier curve fitting, to identify landmark locations and geometries, allowing for less compute-intensive post-processing techniques such as clustering and temporal smoothing, enabling real-time precision landmark detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional DNN approaches are used to detect lane lines by classifying each pixel, then comprehensive landmark detection is achieved, but extensive post-processing is required increasing latency and compute requirements
Solution Approach 1:
The patent extracts only the essential control points that define landmark geometry rather than processing all pixels. By using curve fitting to identify key control points from the DNN output, the system obtains sufficient landmark information with minimal post-processing, directly reducing latency while maintaining detection precision.
Solution Approach 2:
The patent segments the continuous pixel data into discrete control points through curve fitting operations. By fitting curves (e.g., polynomial or B-spline curves) to the pixel classifications, the system extracts a small set of control points that represent the entire landmark, dramatically reducing the data volume requiring post-processing.
2Measurement precision
If conventional DNN approaches classify each pixel to identify lane lines, then accurate landmark positions are obtained, but compute requirements increase making real-time deployment difficult
Solution Approach 1:
The patent extracts only the control points from the full pixel classification output using curve fitting. This extraction approach maintains position accuracy by preserving the geometric essence of landmarks while discarding redundant pixel-level information, thereby reducing compute requirements for subsequent processing.
Solution Approach 2:
Instead of using pixel classifications to directly determine landmark properties, the patent inverts the approach by fitting curves to the pixel data and then deriving landmark properties from the control points. This inversion reduces computational burden while maintaining accuracy.
3Shape
If pixel-level correlations are determined for curved landmarks, then accurate curve representation is achieved, but additional angular or directional information is required increasing system complexity
Solution Approach 1:
The patent explicitly models curved landmarks using curve fitting techniques (polynomial curves, B-spline curves). By representing landmarks as parametric curves with control points, the system naturally captures curvature and shape information without requiring separate angular or directional computations, simplifying the overall system complexity.
Solution Approach 2:
The patent transforms the representation of curved landmarks from pixel-level correlations to control point parameters. By changing the parameterization from individual pixel relationships to curve control point coordinates, the system achieves accurate curve representation with reduced computational complexity.
Data Source
AI summary
In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.