18064016. LANE LINE DETECTION METHOD AND RELATED DEVICE simplified abstract (Huawei Technologies Co., Ltd.)
Contents
LANE LINE DETECTION METHOD AND RELATED DEVICE
Organization Name
Inventor(s)
LANE LINE DETECTION METHOD AND RELATED DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18064016 titled 'LANE LINE DETECTION METHOD AND RELATED DEVICE
Simplified Explanation
The patent application discloses methods and devices for detecting lane lines on roads.
- Lane line detection is achieved by fusing features extracted from different layers of a neural network.
- The fusion process generates a second feature map with multiple layers of features, including low-layer and high-layer receptive field features.
- The output predicted lane line set is divided into groups, with each group containing predicted lane lines with optimal prediction intervals.
Potential Applications
This technology can be applied in various fields, including:
- Autonomous vehicles: Lane line detection is crucial for self-driving cars to navigate and stay within their designated lanes.
- Advanced driver assistance systems (ADAS): Lane departure warning systems and lane keeping assist systems can benefit from accurate lane line detection.
- Traffic management: Lane line detection can be used to monitor and analyze traffic flow, optimize traffic signal timings, and detect lane violations.
Problems Solved
The technology addresses the following problems:
- Accurate lane line detection: By fusing features from different layers of a neural network, the system can capture both low-level and high-level features, leading to more precise lane line detection.
- Optimal prediction intervals: Dividing the predicted lane lines into groups with optimal prediction intervals helps improve the accuracy and reliability of the system's predictions.
Benefits
The technology offers several benefits:
- Enhanced lane line detection: By incorporating features from multiple layers, the system can better handle variations in lane line appearance, lighting conditions, and road markings.
- Improved prediction accuracy: Dividing the predicted lane lines into groups with optimal prediction intervals helps reduce false positives and false negatives, resulting in more reliable lane line predictions.
- Versatile applications: The technology can be applied in various domains, including autonomous vehicles, ADAS, and traffic management, contributing to safer and more efficient transportation systems.
Original Abstract Submitted
This disclosure discloses lane line detection methods and devices. In an implementation, features extracted by different layers of the neural network are fused to obtain a fused second feature map, so that the second feature map obtained through fusion processing has a plurality of layers of features. The fused second feature map has a related feature of a low-layer receptive field and a related feature of a high-layer receptive field. Afterwards, an output predicted lane line set is divided into groups, where each predicted lane line in each group has an optimal prediction interval.
