Object Interaction Neural Network for Autonomous Vehicle Sensor Processing
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting object interactions using traditional heuristic-based approaches, which can lead to failures in identifying dynamic objects and making effective driving decisions.
Innovation Solution
A fully-learned neural network system that processes raw sensor data to predict object interactions without human-programmed logic, providing main object information and interaction confidence scores, enabling better autonomous driving decisions by identifying interacting objects and their locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional heuristic-based approaches are used to predict object interactions, then the system complexity is reduced and ease of operation is improved, but the reliability of object interaction prediction deteriorates
Solution Approach 1:
The patent replaces traditional heuristic-based mechanical logic systems with a neural network system that learns object interaction patterns from data. The neural network substitutes rule-based processing with learned representations, enabling more accurate prediction of object interactions while handling the complexity internally through trained models rather than explicit programming.
Solution Approach 2:
The patent transforms the approach by changing from fixed heuristic parameters to learned parameters through neural network training. The system adjusts weights and biases based on training data to optimize object interaction prediction, allowing the model to adapt to different scenarios and improve reliability through data-driven parameter optimization.
2Measurement precision
If traditional heuristic-based approaches are used for object detection, then the device complexity is reduced, but the measurement precision of interacting objects deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on extensive datasets before deployment. The model learns accurate object interaction patterns, locations, and characteristics in advance through supervised training, enabling precise measurements during actual autonomous vehicle operation without requiring complex real-time processing.
Solution Approach 2:
The patent replaces traditional measurement and detection algorithms with a neural network-based system that learns precise object localization and interaction detection from training data. The neural network substitutes explicit geometric and physical models with learned feature representations, achieving higher measurement precision for interacting objects.
3Adaptability or versatility
If human-programmed logic is used to combine sensor data, then the ease of operation is improved and device complexity is reduced, but the adaptability to different driving scenarios deteriorates
Solution Approach 1:
The patent applies dynamics by making the system adaptive through neural network learning rather than static human-programmed logic. The model can dynamically adjust its predictions based on learned patterns from diverse driving scenarios, enabling versatility across different conditions while the complexity is managed through the trained model's ability to generalize.
Solution Approach 2:
The patent enables adaptability by allowing the neural network parameters to be trained on diverse driving scenarios and then applied to new situations. The system changes from fixed logic to flexible, data-driven parameter optimization that can handle varied conditions through learned representations rather than explicit rules for each scenario.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating object interaction predictions using a neural network. One of the methods includes obtaining a sensor input derived from data generated by one or more sensors that characterizes a scene. The sensor input is provided to an object interaction neural network. The object interaction neural network is configured to process the sensor input to generate a plurality of object interaction outputs. Each respective object interaction output includes main object information and interacting object information. The respective object interaction outputs corresponding to the plurality of regions in the sensor input are received as output of the object interaction neural network.