Autonomous Vehicle Ethical Decision Training Using GAN Location Intelligence
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Solution Overview
Problem
Autonomous vehicles face challenges in making ethical decisions, particularly in complex scenarios, due to the lack of human-like empathetic and ethical decision-making capabilities, which hinders the development of fully autonomous operation.
Innovation Solution
A system utilizing a Generative Adversarial Network (GAN) to generate synthetic ethically complex driving scenarios, combined with a deep learning model (ETHNET) trained using these scenarios, enables autonomous vehicles to make ethical driving decisions by simulating varied and dynamic real-world situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles follow strict driving rules, then safety and reliability are improved, but adaptability to complex ethical scenarios deteriorates
Solution Approach 1:
The system pre-trains the deep learning model using synthetic ethically complex driving scenarios generated by GANs before deployment. This preliminary training enables the vehicle to learn ethical decision-making patterns in advance, allowing it to adapt to complex scenarios without compromising safety during actual operation.
Solution Approach 2:
The patent introduces an intermediary deep learning model (ETHNET) that mediates between strict driving rules and complex ethical scenarios. The model processes sensor data and outputs ethical driving decisions, acting as a bridge that translates rule-based constraints into context-aware ethical judgments for L5 autonomous vehicles.
2Measurement precision
If real ethically complex driving scenarios are used for training, then decision-making accuracy is improved, but data availability and training efficiency deteriorate
Solution Approach 1:
The system uses GANs to generate synthetic copies of ethically complex driving scenarios that replicate real-world ethical dilemmas. These synthetic scenarios serve as training data, providing abundant examples of ethical situations without requiring collection of rare real-world incidents, thus improving training efficiency while maintaining decision-making accuracy.
Solution Approach 2:
The GANs pre-generate a comprehensive dataset of synthetic ethically complex scenarios before model training begins. This preliminary data preparation ensures sufficient training data availability and allows the deep learning model to be thoroughly trained on diverse ethical situations without waiting for real-world data collection.
3Extent of automation
If fully autonomous operation is implemented, then human interaction is eliminated, but ethical decision-making capability deteriorates
Solution Approach 1:
The patent replaces human ethical reasoning (mechanical system) with an artificial intelligence-based deep learning model. The ETHNET processes sensor inputs and generates ethical driving decisions through neural network computations, enabling L5 autonomous vehicles to make ethical judgments without human drivers while maintaining high adaptability to complex scenarios.
Solution Approach 2:
The system performs preliminary training of the deep learning model using synthetic ethical scenarios before deployment. This pre-training embeds ethical reasoning capabilities into the autonomous vehicle's AI system, enabling it to make human-like ethical decisions independently without requiring human interaction or intervention during operation.
Data Source
AI summary
System and methods enable vehicles to make ethical/empathetic driving decisions by using deep learning aided location intelligence. The systems and methods identify moral islands/complex driving scenarios where a complex ethical decision is required. A Generative Adversarial Network (GAN) is used to generate synthetic training data to capture varied ethically complex driving situations. Embodiments train a deep learning model (ETHNET) that is configured to output one or more driving decisions to be taken when a vehicle comes across an ethically complex driving situations in the real world.