Vehicle Sensor Power Mode Switching Based on Predicted Stop Time
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Solution Overview
Problem
Vehicle sensors continue to draw power from the power supply even when data is not needed, leading to increased power consumption and potential depletion of the battery's state of charge, especially when the vehicle is stopped, and transitioning between power modes is inefficient if done too quickly.
Innovation Solution
A vehicle computer estimates the stop time based on operation data from infrastructure elements and transitions available sensors to a low power mode when the stop time exceeds a threshold, reducing power consumption and verifying sensor operation using a deep neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors continue to operate in standard power mode during vehicle stops, then sensor data availability is maintained, but power consumption increases and battery state of charge decreases
Solution Approach 1:
The system dynamically transitions sensors between standard power mode and low power mode based on vehicle motion state. When the vehicle is stopped, sensors switch to low power mode to conserve energy. When the vehicle moves, sensors return to standard power mode to ensure data availability for vehicle operation.
Solution Approach 2:
The system changes the power mode parameter of sensors based on vehicle stop duration predictions. By estimating stop time from infrastructure element operation data, the system adjusts sensor power consumption parameters to match actual operational needs, reducing energy waste during extended stops.
2Use of energy by moving object
If sensors are transitioned to low power mode during stops, then power consumption is reduced, but sensor operation reliability may be compromised if transition timing is incorrect
Solution Approach 1:
The system performs preliminary estimation of stop duration using operation data from infrastructure elements before transitioning sensors to low power mode. This advance assessment ensures that sensors are only put into low power mode when stops are predicted to exceed the threshold, preventing premature transitions that could compromise operational reliability.
Solution Approach 2:
The system uses feedback from infrastructure element operation data to continuously monitor and adjust sensor power mode transitions. By comparing actual stop durations against predicted durations, the system refines its transition timing to maintain reliability while maximizing energy savings.
3Use of energy by moving object
If frequent transitions between power modes are performed, then power consumption is optimized, but transition efficiency decreases due to rapid mode switching
Solution Approach 1:
The system estimates stop duration in advance using infrastructure element operation data before initiating power mode transitions. This preliminary assessment filters out brief stops that would not benefit from mode transitions, reducing the frequency of unnecessary transitions and improving overall transition efficiency.
Solution Approach 2:
The system autonomously determines optimal transition timing based on predicted stop duration thresholds, eliminating the need for manual intervention or frequent monitoring. This self-service approach stabilizes transition patterns and reduces inefficient switching by maintaining consistent transition criteria.
Data Source
AI summary
Upon determining that a vehicle is operating within an area, a stop time for the vehicle in the area is estimated based on operation data from an infrastructure element in the area. A vehicle sensor that is available to transition to a low power mode is identified based on the estimated stop time. Upon stopping the vehicle in the area, the available vehicle sensor is transitioned to the low power mode based on the estimated stop time being greater than a threshold.