Vehicle Maneuver Assistance Using Neural Networks and Driver Models
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Solution Overview
Problem
Existing driver assistance systems in vehicles lack reliable methods to accurately analyze and assess driving situations, leading to potential errors in decision-making during maneuvers.
Innovation Solution
A method utilizing a predefined neural network and driver model to determine the feasibility of a driving maneuver, trained with bidirectional and recurrent neural networks and past driving data, to provide reliable hit accuracy for assistance or autonomous execution of maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional driver assistance systems are used to analyze driving situations, then the system structure is simple, but the decision-making accuracy and reliability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical decision-making systems with neural network-based artificial intelligence systems. The neural network processes driving situation data and generates maneuver recommendations, substituting conventional rule-based or sensor-driven mechanical systems with a more sophisticated computational model that achieves higher decision-making accuracy and reliability.
2Extent of automation
If driver assistance systems make autonomous decisions, then the level of automation is high, but the reliability of maneuver execution is insufficient
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously receives information about the actual execution of driving maneuvers and compares it with the predicted outcomes. This feedback loop allows the system to learn from past decisions and improve future maneuver execution reliability, ensuring that autonomous decisions become progressively more reliable through iterative optimization.
3Measurement precision
If the neural network is trained with comprehensive driving data, then the prediction accuracy is high, but the data processing time is long
Solution Approach 1:
The patent performs preliminary training of the neural network using comprehensive driving data before actual vehicle operation. The neural network is pre-trained with large datasets containing various driving scenarios, maneuvers, and outcomes, so that during actual use, the system can make rapid predictions without requiring real-time processing of extensive training data, thus reducing operational decision-making time while maintaining high accuracy.
Data Source
AI summary
In a method for driving maneuver assistance of a vehicle, a predefined neural network is provided, which is designed to determine whether a predefined driving maneuver is probably possible. A predefined driver model is provided, which is designed to predict a probable future behavior of a vehicle. A current driving situation of the vehicle is determined. Depending on the determined driving situation, the driver model and the neural network, it is determined whether a predefined driving maneuver is possible. Depending on the determination as to whether the driving maneuver is possible, a driver assistance function for the driving maneuver is carried out and/or the driving maneuver is carried out autonomously.