Autonomous Driving Parameter Selection for Risk-Adaptive Control
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Solution Overview
Problem
Current autonomous driving technologies face challenges in real-time calculation delays due to complex mathematical modeling, which can lead to safety risks and inefficiencies in adapting to driver preferences and environmental conditions.
Innovation Solution
An autonomous driving control method that determines driving parameter values from preset groups based on driver preferences and environmental risk levels, reducing the need for complex calculations and enhancing safety by directly selecting appropriate parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If real-time calculation through mathematical modeling is performed to adapt to driver preferences and environmental conditions, then adaptability is improved, but calculation delay increases which compromises safety
Solution Approach 1:
The patent pre-calculates and stores multiple groups of driving parameters corresponding to different driver preferences and environmental risk levels before actual driving. During autonomous driving, the system directly selects from these pre-prepared parameter groups based on current conditions, avoiding real-time complex calculations while ensuring both adaptability and safety response time
2Measurement precision
If complex mathematical modeling is performed in real-time, then driving parameter accuracy is improved, but calculation time increases causing delays
Solution Approach 1:
The system performs complex parameter calculations in advance and stores them in a database. During actual autonomous driving, the control unit queries and directly retrieves pre-calculated parameter groups based on current driver preference and environmental conditions, eliminating real-time calculation delays while maintaining parameter accuracy
Solution Approach 2:
Instead of performing original complex calculations in real-time, the system creates and stores copies of pre-calculated driving parameter sets for various conditions. The control unit then selects appropriate copies based on current situation, replacing complex real-time computation with efficient data retrieval
3Device complexity
If preset groups of driving parameter values are used, then device complexity is reduced, but adaptability to individual driver preferences may be limited
Solution Approach 1:
The patent creates a universal parameter selection system that handles multiple driver preferences and environmental conditions through a single standardized framework. The control unit can select from multiple pre-prepared parameter groups based on different conditions, making the system adaptable to various scenarios without increasing structural complexity
Data Source
AI summary
An example autonomous driving control method is provided, including determining a current driving preference mode from a plurality of driving preference modes. A current environment risk level can be determined from a plurality of environment risk levels. A current group of driving parameter values can be selected from a plurality of preset groups of driving parameter values based on the current driving preference mode and the current environment risk level, where the current group of driving parameter values corresponds to the current driving preference mode and the current environment risk level. Each of the plurality of preset groups of driving parameter values can include at least one driving parameter value, and each preset group of driving parameter values can correspond to a driving preference mode of the plurality of driving preference modes and an environment risk level of the plurality of environment risk levels.