Adaptive ODD Calculation for Automated Driving
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Solution Overview
Problem
Existing communication systems, particularly in the context of 5G NR, face challenges in optimizing the use of automated driving systems by reducing the number of operational design domain (ODD) switches, which can lead to inefficiencies and discomfort for drivers.
Innovation Solution
A method is provided where a user equipment (UE) associated with a vehicle calculates a predicted number of ODD switches based on attributes such as navigation maps, traffic data, and weather data. The UE then notifies the driver or deactivates the ODD mode if the predicted number of ODD switches exceeds a threshold value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the automated driving system continuously monitors and switches ODD modes based on real-time conditions, then the system adaptability improves, but the frequency of ODD switches increases causing driver discomfort and system inefficiency
Solution Approach 1:
The system performs preliminary calculation of the predicted number of ODD switches along the planned route before executing the journey. By anticipating future ODD boundary crossings and comparing the total count against a threshold, the system proactively determines whether to maintain or deactivate the automated driving mode, thereby avoiding frequent switches that would cause driver discomfort
2Productivity
If the automated driving system activates and deactivates frequently to maintain optimal performance, then the system productivity improves, but the loss of time due to switching overhead increases
Solution Approach 1:
The system calculates the predicted number of ODD switches in advance using route attributes and ODD boundary information. By performing this prediction before the journey begins, the system avoids real-time switching decisions and their associated time overhead, maintaining continuous operation in the optimal mode throughout the journey
3Measurement precision
If the system uses multiple attributes (navigation maps, traffic data, weather data) for accurate ODD prediction, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system leverages existing multi-functional data infrastructure by integrating navigation maps, traffic data, and weather data through standardized interfaces. The core innovation lies in the prediction algorithm that processes these diverse data types using a unified approach, calculating the predicted number of ODD switches by evaluating route attributes against ODD boundary conditions, thereby achieving high prediction accuracy without proportionally increasing system complexity
Data Source
AI summary
A user equipment (UE) may be associated with a vehicle, for example the UE may be a vehicle or may be a wireless device connectively coupled (e.g., wired or wireless connection) to the vehicle. The UE may receive a set of attributes associated with a route of the UE. The UE may calculate a predicted number of operational design domain (ODD) switches along the route based on the set of attributes. In one aspect, the UE may notify a driver associated with the UE of an ODD condition based on the calculated predicted number of ODD switches being greater or equal than a threshold value. In another aspect, the UE may deactivate an ODD mode associated with the UE based on the calculated predicted number of ODD switches being greater or equal than a threshold value.