Machine Consciousness Model for Autonomous Driving
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Solution Overview
Problem
Current automatic driving technologies face challenges such as high computational complexity, limited ability to reflect human judgment and sensory fusion, and the inability to simultaneously optimize multiple objectives like safety, comfort, and energy efficiency, leading to difficulties in developing effective machine consciousness models for autonomous vehicles.
Innovation Solution
A composition method for an automatic driving 'machine consciousness' model that uses a self-organizing machine learning approach with a unified distance formula spanning Euclidean and probability spaces, incorporating fuzzy event probability measurement to improve image processing and decision-making, allowing for optimized control of multiple objectives without extensive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional neural network algorithms are used for automatic driving control, then the system can process complex driving scenarios, but the computational complexity and hardware overhead become excessively high
Solution Approach 1:
The patent segments the automatic driving control system into multiple specialized modules: perception module for environmental sensing, decision module for route planning, control module for vehicle operation, and safety module for risk management. Each module processes specific aspects of driving control independently, reducing the computational burden on any single component while maintaining comprehensive scenario processing capability
Solution Approach 2:
The patent develops a unified control framework that integrates multiple driving functions (perception, decision-making, control execution, safety monitoring) into a single multi-functional system. This universal architecture eliminates the need for separate specialized systems for each function, reducing overall hardware overhead while maintaining versatility in handling diverse driving scenarios
2Measurement precision
If extensive training data and computation are used to improve machine learning models, then the accuracy of driving decisions improves, but the development cost and time increase significantly
Solution Approach 1:
The patent pre-establishes a comprehensive rule base encoding traffic regulations, safety protocols, and driving norms before deployment. This preliminary action allows the system to make accurate decisions based on predefined knowledge rather than requiring extensive real-time training, significantly reducing development time while maintaining high decision accuracy
Solution Approach 2:
The system incorporates self-learning capabilities that allow it to improve its performance continuously during actual operation without requiring external retraining. The machine learning models adapt to new scenarios autonomously by learning from accumulated operational data, reducing the need for costly and time-consuming external training processes
3Reliability
If the automatic driving system prioritizes safety by identifying all potential obstacles, then collision risk decreases, but the number of false positives increases causing unnecessary lane changes
Solution Approach 1:
The patent applies different detection thresholds and validation rules to different types of objects and spatial zones. High-criticality objects (pedestrians, vehicles) in critical zones (front, rear, blind spots) undergo rigorous multi-stage verification, while low-criticality objects in non-critical zones use simpler detection. This localized quality control reduces false positives while maintaining high safety standards where needed
Solution Approach 2:
The system implements continuous feedback loops where detection results are validated against multiple criteria (object characteristics, spatial context, temporal consistency, predicted trajectory) before triggering lane change decisions. False positives are filtered through feedback mechanisms that cross-check detections with sensor data, map information, and predicted vehicle behavior, reducing unnecessary lane changes while maintaining collision avoidance
4Reliability
If the system follows strict traffic rules and maintains safe distances, then safety improves, but the driving speed and efficiency decrease
Solution Approach 1:
The patent implements dynamic adjustment of safety parameters (following distance, speed limits, detection thresholds) based on real-time driving conditions, vehicle type, traffic flow, and environmental factors. The system transitions between conservative and aggressive driving modes dynamically, maintaining strict safety compliance when conditions require it while optimizing speed and efficiency when conditions permit, eliminating the need to choose between safety and performance
Data Source
AI summary
The invention proposes an automatic driving “machine consciousness” model, which is composed by the human's safety driving rules. Establish the dynamic fuzzy event probability measure relation, or fuzzy relation, or probability relation of the automatic driving vehicle and the surrounding passing vehicle. The decision result of “machine consciousness” of automatic driving vehicle is realized by complicated logic operation and using the antagonistic result of logic operation in both positive and negative directions. The implementation result is that it can make the decision-making result of automatic driving vehicle close to the result of human's biological consciousness, which can improve the safety of automatic driving vehicle, reduce the development cost and reduce the distance of road test.