Autonomous Vehicle Lane Width Prediction via Neural Networks
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Solution Overview
Problem
Existing autonomous vehicle localization methods rely on Global Navigation Satellite System (GNSS), inertial measurement units, and digital maps, which can be computationally expensive, unreliable in areas with poor signal reception, or prone to significant errors, limiting accurate navigation.
Innovation Solution
The use of machine learning models, specifically neural networks, to analyze real-time image data from autonomous vehicles to predict lane widths and identify the number of lanes, reducing reliance on potentially inaccurate location data sources by training models with ground truth localization data and image recognition protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing localization methods (GNSS, inertial measurement unit, digital map) are used, then localization can be achieved, but computational cost is high and reliability deteriorates in areas with poor signal reception
Solution Approach 1:
The patent replaces traditional mechanical/computational localization systems (GNSS receivers, inertial measurement units, digital map processors) with a vision-based machine learning system. The neural network model processes image data from cameras to directly predict lane width and vehicle position, substituting complex computational localization mechanisms with a learned visual recognition system that operates efficiently without heavy computational resources.
Solution Approach 2:
The patent creates a virtual representation of the road geometry by training the neural network on labeled image data containing ground truth localization information. The model learns to copy the spatial relationships and lane structures from training images, enabling it to predict lane width and vehicle position in new scenarios without requiring actual GNSS signals or complex map matching computations.
2Measurement precision
If existing localization methods (GNSS, inertial measurement unit, digital map) are used, then localization can be achieved, but location resolution and accuracy deteriorate in areas with reduced signal reception
Solution Approach 1:
The patent replaces signal-dependent localization hardware (GNSS receivers that fail in tunnels or urban canyons) with a vision-based system that uses image processing to determine position. The neural network analyzes visual features of the road environment to infer precise lane width and vehicle location, achieving measurement precision independent of satellite signal availability.
Solution Approach 2:
The system uses the autonomous vehicle's own camera system and onboard processing capabilities to determine its location, rather than relying on external infrastructure like GNSS satellites. The vehicle serves its own localization needs through self-contained visual perception and machine learning, making the system reliable in environments where external signals are unavailable.
3Measurement precision
If machine learning models are used to predict lane width from image data, then localization accuracy improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network model on large datasets of labeled image data with ground truth localization information before deployment. This offline training phase captures complex road geometry patterns and relationships, so that during actual operation, the model can make accurate predictions through simple forward propagation without requiring complex real-time computations or additional hardware.
Solution Approach 2:
The patent transforms the localization problem from estimating absolute position coordinates to predicting relative geometric parameters (lane width, vehicle position within lane). This parameter transformation simplifies the measurement task by focusing on local road geometry features that are more easily extracted from images, reducing the complexity of the sensing and processing system while maintaining accuracy.
Data Source
AI summary
A method, comprises identifying a set of image data captured by at least one autonomous vehicle when the at least one autonomous vehicle was positioned in a lane of a roadway, and respective ground truth localization data of the at least one autonomous vehicle; determining a plurality of lane width values for the set of image data; labeling the set of image data with the plurality of lane width values, the plurality of lane width values representing a width of a lane in which the at least one autonomous vehicle was positioned; and training using the labeled set of image data, a machine learning model, such that the machine learning model is configured to predict a new lane width value for a new lane as output.