Autonomous Driving Parameter Selection for Low-Latency Risk Adaptation
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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 different driving habits and environments.
Innovation Solution
An autonomous driving control method that determines driving preference modes and environment risk levels to select predefined groups of driving parameter values, reducing the need for complex calculations and enhancing safety by directly selecting from preset parameter values based on driver preferences and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time calculation through mathematical modeling is performed to adapt to different driving habits and environments, then driving parameter accuracy is improved, but calculation delay increases causing safety risks
Solution Approach 1:
The patent pre-calculates and stores multiple groups of driving parameters corresponding to different driving preference modes and environment risk levels in advance. During actual autonomous driving, the system only needs to select from these pre-prepared parameter groups based on current conditions, eliminating the need for complex real-time mathematical modeling and calculations, thus resolving the contradiction between parameter accuracy and calculation delay
Solution Approach 2:
The patent dynamically adjusts driving parameters by first determining the current driving preference mode and environment risk level, then selecting the corresponding pre-stored parameter group. This dynamic selection mechanism allows the system to adapt to different driving scenarios in real-time without performing complex calculations, achieving both accuracy and speed
2Adaptability or versatility
If complex mathematical modeling is performed in real-time to calculate driving parameters, then adaptability to different driving preferences is improved, but system complexity increases
Solution Approach 1:
The patent performs the complex mathematical modeling and parameter calculation work in advance, storing the results in a database. The control system only needs to perform simple lookup and selection operations during actual driving, significantly reducing system complexity while maintaining full adaptability to different driving preferences and environmental conditions
Solution Approach 2:
The patent creates multiple copies of driving parameter sets corresponding to different driving preference modes and environment risk levels. Instead of performing complex calculations in real-time, the system copies and applies the appropriate pre-calculated parameter set, simplifying the control system while maintaining adaptability
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.