Autonomous Vehicle Control via Text-Based Sensor Interpretation
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Solution Overview
Problem
Precise prediction of the movements of other road users in autonomous driving is complex, requiring advanced sensors, data processing, and machine learning, but existing methods often rely on raw sensor data for trajectory prediction, which can be inefficient and prone to errors.
Innovation Solution
A computer-implemented method using a machine learning algorithm, particularly a neural network with a transformer architecture, processes sensor data as text to generate control commands for autonomous vehicles, incorporating HD maps and integrating camera and radar data for enhanced prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trajectory prediction methods using raw sensor data are used, then the system is simpler to implement, but the prediction accuracy and reliability deteriorate
Solution Approach 1:
The patent introduces text as an intermediary representation layer between raw sensor data and prediction algorithms. Sensor data from cameras, radar, and other sensors are converted into natural language text descriptions that capture spatial relationships, object identities, and environmental context. This text intermediary enables sophisticated reasoning and prediction while maintaining system modularity and interpretability.
Solution Approach 2:
The patent replaces traditional mechanical/mathematical data processing systems with language-based processing. Instead of using conventional computer vision and sensor fusion algorithms that directly process numerical sensor data, the system uses large language models to interpret text representations of sensor data, leveraging linguistic reasoning capabilities for trajectory prediction and control decision-making.
2Reliability
If advanced machine learning algorithms are used for trajectory prediction, then the prediction reliability improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary conversion of sensor data into text representations before feeding them to large language models. This pre-processing step structures the sensor information in a format that leverages the linguistic reasoning capabilities of LLMs, enabling more efficient and reliable prediction while reducing the computational burden compared to directly processing raw sensor data through complex machine learning models.
3Measurement precision
If more sensor data and HD maps are integrated, then the navigation accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent merges multiple data sources including camera images, radar data, and HD map information into a unified natural language text description. This integration approach consolidates diverse sensor inputs and contextual information into a coherent textual representation that captures the complete environmental state, simplifying subsequent processing while maintaining high navigation accuracy.
Data Source
AI summary
A computer-implemented method for generating a control command for an autonomous vehicle. The method includes: capturing sensor data by at least one sensor of the vehicle; generating an input text from the sensor data; interpreting the input text using a machine learning algorithm; and generating a control command for the vehicle from the interpreted input text.