Vehicle Software Update Scheduling for Low-Downtime Fleet Operation
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Solution Overview
Problem
Current methods for updating software in automated heavy-duty vehicles often disrupt their operation, leading to inefficiencies as they require being taken offline, and do not consider the specific usage patterns or types of vehicles, resulting in unnecessary downtime and reduced availability.
Innovation Solution
A computer-implemented method that predicts optimal time periods for software updates based on the usage patterns of similar vehicles, allowing updates to be scheduled during non-stationary behavior states when the vehicle is operational but not actively using certain software modules, thereby minimizing downtime and maximizing availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software updates are performed on heavy-duty vehicles, then the software is updated to improve functionality and reliability, but the vehicle must be taken offline resulting in loss of operational efficiency
Solution Approach 1:
The system performs preliminary analysis of usage patterns from multiple vehicles to predict future idle periods before they occur. By anticipating when vehicles will naturally be idle based on historical data from similar vehicles, the system can proactively schedule software updates to coincide with these predicted idle periods, thus avoiding disruption to operational efficiency while ensuring updates are completed.
2Productivity
If software updates are scheduled during vehicle operation, then operational efficiency is maintained, but update failures may occur resulting in abortive updates
Solution Approach 1:
The system continuously monitors actual vehicle usage patterns and compares them against predicted patterns. This feedback loop allows the system to learn from discrepancies and improve its predictions over time. By using real-world operational data from the fleet, the system adapts its scheduling algorithm to accurately predict when vehicles will be idle, thereby increasing update success rates while maintaining operational efficiency.
3Stability of the object's composition
If updates are performed on all vehicles simultaneously, then software consistency across the fleet is maintained, but a significant portion of the fleet becomes unavailable at the same time
Solution Approach 1:
The system applies different update schedules to different vehicles based on their individual usage patterns and operational requirements. Instead of a uniform approach, each vehicle receives customized scheduling that considers its specific idle periods and operational constraints. This localized approach allows the fleet to maintain software consistency over time while ensuring that updates are distributed across different time periods, preventing mass downtime.
4Productivity
If updates are delayed to minimize disruption, then operational efficiency is maintained, but software security vulnerabilities and performance issues are prolonged
Solution Approach 1:
The system performs preliminary analysis of usage patterns from multiple vehicles to predict future idle periods before they occur. By anticipating when vehicles will naturally be idle based on historical data from similar vehicles, the system can proactively schedule software updates to coincide with these predicted idle periods, thus avoiding disruption to operational efficiency while ensuring updates are completed.
Data Source
AI summary
A computer-implemented method for scheduling a vehicle software update in a vehicle, for example, in heavy-duty vehicle which may be operating in a defined area or site, and/or along a defined trajectory, the method comprising associating a probability of the vehicle having an updateable operational state with one or more time periods, wherein the probability of the vehicle being updateable is determined based at least partly on data representing behavioural states of one or more similar vehicles, in other words, other vehicles which have usage characteristics which have a relationship to the usage characteristics of the vehicle; scheduling a pending software update until the next time period of the one or more time periods where the probability of the vehicle having an updateable operational states meets a software update condition; and, enabling the vehicle to perform the software update when the vehicle probability having the updateable operational state meets the software update condition.