Deep Neural Path Navigation for Vehicle Orientation and Lateral Position
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Solution Overview
Problem
Current autonomous vehicle navigation systems are often inaccurate and inefficient, lacking awareness of their environment, which hinders their ability to accurately determine vehicle orientation and lateral position relative to a path.
Innovation Solution
The use of deep neural networks (DNNs) to process image data from various sources, including cameras, LIDAR, and radar, to determine vehicle orientation and lateral position, and subsequently control the vehicle's steering mechanisms in real-time, incorporating additional DNNs for object and obstacle detection to enhance safety and navigation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used to process image data for autonomous navigation, then navigation accuracy and environmental awareness are improved, but computational complexity and processing time increase
Solution Approach 1:
The deep neural network is divided into multiple processing stages: image data reception, feature extraction, orientation determination, lateral position determination, and control signal generation. This segmentation allows complex processing to be broken down into manageable steps, improving both accuracy and computational efficiency.
Solution Approach 2:
The system performs preliminary processing of image data by extracting relevant features before feeding them to the neural network for final decision-making. This preliminary action reduces the computational burden on the main processing unit while maintaining high navigation accuracy.
2Reliability
If multiple sensors and DNNs are integrated for real-time processing, then environmental awareness and safety are improved, but system complexity increases
Solution Approach 1:
Multiple sensors (cameras, LIDAR, radar) are merged into a unified processing system that feeds data to the deep neural network. This integration allows the system to process information from multiple sources simultaneously, improving safety and environmental awareness while managing complexity through unified architecture.
Solution Approach 2:
The deep neural network is designed to perform multiple functions: detecting paths, determining vehicle orientation, calculating lateral position, and generating control signals. This multi-functionality reduces the need for separate specialized systems, thereby managing overall system complexity while maintaining high reliability.
3Measurement precision
If real-time image processing is performed to determine orientation and lateral position, then navigation precision is improved, but processing time and computational load increase
Solution Approach 1:
The system processes only the most critical features of the image data that are necessary for determining orientation and lateral position, rather than analyzing every detail. This partial processing approach maintains high position determination accuracy while significantly reducing processing time and computational load.
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.