Autonomous Driving App Mode Switching for Vehicle Resource Limits
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Solution Overview
Problem
Autonomous vehicles face challenges in dynamically managing resource utilization based on changing driving conditions, such as location, surrounding objects, and passenger preferences, which affects the efficiency and performance of applications during autonomous driving.
Innovation Solution
A method and apparatus that utilize an artificial neural network (ANN) to predict resource utilization by analyzing driving route information, switching applications between different modes based on predicted resource thresholds, and controlling execution modes to optimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle executes applications in a mode requiring more resources to maintain high performance, then service quality is improved, but resource utilization efficiency deteriorates when resources are limited
Solution Approach 1:
The patent dynamically adjusts application execution modes based on real-time resource utilization predictions. The system switches between first mode (higher resource consumption for better performance) and second mode (lower resource consumption for efficiency) according to predicted resource needs, making the system adaptable rather than static
Solution Approach 2:
The system changes execution mode parameters based on resource utilization thresholds. When predicted resource utilization exceeds a threshold, the system transitions from first mode to second mode, and vice versa, thereby optimizing the balance between service quality and resource efficiency
2Use of energy by moving object
If the vehicle dynamically changes application modes to optimize resource usage, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where resource utilization predictions are continuously monitored and fed back to control logic. Based on whether predicted utilization exceeds thresholds, the system automatically adjusts execution modes, creating a closed-loop control system that manages complexity through structured feedback rather than uncontrolled complexity
Solution Approach 2:
The system performs self-adjustment by automatically switching between execution modes based on predicted resource utilization without requiring external intervention. The autonomous resource management reduces operational complexity by enabling the system to self-regulate its resource consumption
Data Source
AI summary
A method for managing a vehicle's resource includes: executing at least one application requiring a resource in a first mode, the at least one application associated with autonomous driving process of the vehicle, obtaining driving route information, obtaining, from a position data generation device disposed at the vehicle, location information providing a current location of the vehicle, predicting resource utilization expected to be required in the first mode by using the driving route information and the location information, and switching from the first mode executing the at least one application to a second mode, wherein the at least one application is executed in the second mode requiring less resources than being executed in the first mode based on the predicted resource utilization exceeding a first threshold.