Cursor Recurrent Neural Network for Real-Time Lane Detection
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Solution Overview
Problem
Current computer vision systems for autonomous driving face challenges in accurately detecting lane lines and other road features in real-time, particularly due to the brittleness of low-level features under varying environmental conditions and the need for precise localization, which is hindered by the tradeoff between flexibility and semantic understanding in neural network representations.
Innovation Solution
A cursor recurrent neural network (RNN) is employed to traverse a feature map and iteratively output coordinates of detected lane lines or object contours, eliminating the need for decoders and enabling end-to-end training, while using a grid-based approach to represent lane lines parametrically, allowing for precise localization and robust detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional low-level feature detection methods are used, then the system can operate in real-time, but the detection precision and reliability deteriorate under varying environmental conditions
Solution Approach 1:
The patent transforms the detection approach by changing the representation parameters from low-level pixel features to high-level semantic features encoded in a feature map. The cursor RNN processes these high-level features to predict contour coordinates, maintaining real-time performance while improving detection precision through semantic understanding of road features.
Solution Approach 2:
The patent replaces traditional mechanical feature detection algorithms with a neural network-based cursor RNN system. This substitution enables the system to process high-level semantic features directly, achieving both real-time performance and improved precision through learned representations rather than hand-crafted low-level features.
2Measurement precision
If high-level semantic features are used to improve detection accuracy, then the system complexity increases due to the need for sophisticated neural network architectures
Solution Approach 1:
The patent segments the complex detection task into distinct functional components: a feature map encoder that extracts high-level semantics, a cursor RNN that traverses the feature map to predict contour points, and a coordinate output module. This segmentation manages system complexity by organizing the neural network architecture into modular, specialized sub-components.
Solution Approach 2:
The patent introduces a feature map as an intermediary representation between the input image and the final contour detection output. This intermediate high-level feature representation simplifies the detection task by providing semantic information, reducing the complexity of the subsequent contour prediction process.
3Measurement precision
If parametric representations are used to improve localization precision, then the flexibility of the detection system decreases
Solution Approach 1:
The patent employs a dynamic cursor RNN that adaptively traverses the feature map to predict contour coordinates. Rather than using fixed parametric models, the system dynamically adjusts its prediction process based on the learned features, maintaining flexibility while achieving high localization precision through end-to-end learning.
Solution Approach 2:
The patent performs preliminary encoding of the input image into a high-level feature map that captures semantic information about road features. This preliminary action prepares the data in a form that enables precise localization predictions while maintaining adaptability to various environmental conditions and feature types.
Data Source
AI summary
An approach is provided for object detection. The approach involves receiving a feature map encoding high level features of object contours detected in an image divided into a plurality of grid cells, and further encoding start locations of each detected object contour. The approach also involves selecting a grid cell including a start location of an object contour. The approach further involves determining a precise location of the start location within the grid cell. The approach further involves determining a set of feature values from a set of proximate grid cells. The approach further involves processing the precise location and the set of feature values using a machine learning network to output a displacement vector to indicate a next coordinate of the object contour, and updating a cursor of the machine learning network based on the displacement vector.