Vehicle Neural Network Processing via Inverse Reinforcement Learning
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Solution Overview
Problem
Current autonomous and semi-autonomous vehicle systems face challenges in accurately determining vehicle trajectories and actions in complex traffic environments due to incomplete or missing data, leading to potential collisions or inefficient navigation.
Innovation Solution
A deep neural network (DNN) trained using an inverse reinforcement learning (IRL) system with a variational auto-encoder (VAE) processes vehicle sensor data to determine vehicle actions, including speed and lane changes, by learning from expert policies and latent reward functions, enabling the vehicle to operate safely and efficiently on roadways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle control systems are used, then the system structure is simple, but the system cannot accurately determine vehicle trajectories in complex traffic environments due to incomplete data
Solution Approach 1:
The patent introduces a deep neural network as an intermediary component between sensor data and vehicle control decisions. The DNN processes incomplete sensor data and infers missing information about traffic environment and vehicle trajectories, enabling accurate trajectory determination without requiring complete data from complex sensor arrays.
Solution Approach 2:
The patent replaces traditional mechanical sensor-based trajectory detection systems with an information-processing approach using deep neural networks. Instead of relying on physical sensors to capture complete environmental data, the system uses computational models to infer trajectories from available sensor data, substituting mechanical detection with intelligent inference.
2Reliability
If more sensors and complex processing systems are added to improve trajectory accuracy, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The deep neural network serves as an intermediary that enhances the reliability of vehicle operation by processing and interpreting sensor data more effectively. Rather than adding more sensors, the DNN intermediary extracts reliable trajectory information from existing sensor data, improving safety without increasing sensor count or system complexity.
3Productivity
If traditional control methods are used, then the system is easy to operate, but the vehicle cannot efficiently navigate complex traffic environments
Solution Approach 1:
The deep neural network enables the vehicle system to self-service by autonomously processing sensor data, determining trajectories, and making navigation decisions without human intervention. The DNN self-learns from data and automatically adapts to complex traffic environments, improving navigation efficiency while maintaining operational simplicity for the user.
Data Source
AI summary
A computing system can be programmed to determine a vehicle action based on vehicle sensor data input to a deep neural network (DNN) trained using an inverse reinforcement learning (IRL) system that includes a variational auto-encoder (VAE). The computing system can be further programmed to operate a vehicle based on the vehicle action.