Deep Neural Path Navigation for Vehicle Pose and Lateral Control
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Solution Overview
Problem
Current autonomous vehicle navigation systems are often inaccurate and inefficient, lacking awareness of their environment, which hinders precise path navigation and safety.
Innovation Solution
The implementation of deep neural networks (DNNs) for analyzing image data to determine a vehicle's orientation and lateral position relative to a path, enabling real-time control and obstacle detection, thereby improving navigation accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used to analyze image data for autonomous navigation, then navigation accuracy and environmental awareness are improved, but device complexity and computational requirements increase
Solution Approach 1:
The deep neural network is pre-trained with large datasets of image data and corresponding navigation information before deployment. This preliminary training enables the network to perform accurate real-time inference without requiring complex runtime processing, as the learning and pattern recognition work is completed in advance during the training phase.
Solution Approach 2:
Traditional rule-based navigation algorithms and manual programming approaches are replaced with a data-driven deep neural network system. The mechanical process of programming explicit navigation rules is substituted with a learning-based system that automatically extracts navigation patterns from image data, reducing the need for manual system configuration and improving adaptability.
2Speed
If deep neural networks process image data in real-time, then responsiveness and control accuracy are improved, but energy consumption increases
Solution Approach 1:
The deep neural network architecture is segmented into multiple layers with specialized functions (convolutional layers for feature extraction, pooling layers for dimensionality reduction, fully connected layers for classification). This segmentation allows computationally intensive operations to be distributed and optimized, enabling real-time processing while managing energy consumption through efficient resource utilization at each layer.
Solution Approach 2:
The system dynamically adjusts processing parameters such as image resolution, network depth, and processing frequency based on navigation requirements and computational resources available. By changing these parameters, the system can balance real-time processing performance with energy consumption, using higher processing power only when necessary for safe navigation.
3Measurement precision
If deep neural networks determine both orientation and lateral position, then path navigation precision is improved, but measurement complexity increases
Solution Approach 1:
The determination of orientation and lateral position is merged into a single integrated deep neural network processing pipeline. Instead of using separate systems for each measurement, the network simultaneously extracts both pieces of navigation information from the same image data through shared feature extraction layers, reducing measurement complexity while maintaining high precision for both parameters.
Solution Approach 2:
The deep neural network is designed as a multi-functional system that performs multiple navigation tasks including orientation determination, lateral position calculation, and path tracking. This universal approach allows a single system to handle diverse measurement requirements, reducing overall system complexity compared to using specialized separate systems for each function.
Data Source
AI summary
A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.