Vehicle Software Deployment Planning for ECU Fallback Placement

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Solution Overview

Problem

Existing vehicle software deployment systems often result in sub-optimal resource utilization and fail to efficiently handle component failures, leading to degraded performance or inoperability, without providing efficient fallback deployment plans.

Innovation Solution

A vehicle software deployment system that generates optimized primary and fallback deployment plans using telemetry data, fleet-wide data analytics, and customer preferences, ensuring continuous vehicle operation even in the event of component failures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If vehicle software applications are deployed to vehicles with multiple ECUs and sensors, then the applications can function with various vehicle configurations, but the resource utilization becomes sub-optimal and deployment complexity increases

Engineering Contradiction:

Improvevehicle configuration compatibilityVSAvoiddeployment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system dynamically changes deployment parameters by generating multiple deployment plans (primary and fallback) with different resource allocations and ECU assignments. The optimization engine adjusts deployment configurations based on real-time vehicle state, resource availability, and failure scenarios, transforming static deployment into dynamic parameter optimization

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system performs preliminary actions by pre-generating fallback deployment plans and pre-identifying alternative ECU assignments before failures occur. The optimization engine prepares multiple deployment scenarios in advance, so when a failure occurs, the system can immediately switch to a pre-computed fallback plan without complex real-time decision-making

Inventive Principle:

Principle #10Preliminary action

2Reliability

If vehicle software applications are deployed in feasible configurations, then the applications can operate, but resource utilization is not optimized and performance is degraded

Engineering Contradiction:

Improveapplication operabilityVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements feedback mechanisms where the optimization engine continuously monitors vehicle resource usage, ECU status, and application performance. Based on this feedback, the system dynamically adjusts deployment plans to optimize resource utilization while maintaining application operability, transforming static feasible deployment into dynamic optimized deployment

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system transitions from static deployment configurations to dynamic deployment plans that adapt in real-time. The optimization engine continuously evaluates alternative ECU assignments and resource allocations, adjusting deployment plans dynamically based on changing vehicle conditions, resource availability, and performance requirements

Inventive Principle:

Principle #15Dynamics

3Productivity

If vehicle components fail, then resources required to implement software applications become unavailable, but the system lacks fallback deployment plans causing application inoperability

Engineering Contradiction:

Improveapplication functionalityVSAvoidsystem resilience to failures

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system applies beforehand cushioning by pre-generating fallback deployment plans and identifying alternative ECU assignments before failures occur. When a component fails, the system has pre-computed backup deployment configurations ready to maintain application functionality, cushioning against the impact of failures

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

Solution Approach 2:

The optimization engine acts as an intermediary between application requirements and ECU resources, especially during failure scenarios. It mediates by重新分配 resources and generating fallback deployment plans that match applications with alternative ECUs, ensuring continuous functionality despite component failures

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12481490B2Vehicle application deployment system with optimized placement

Publication Date: 2025.11.25 AMAZON TECH INC

AI summary

Systems and methods of determining and providing optimized deployment plans for deploying software to vehicles are disclosed. In some embodiments, a vehicle software deployment system evaluates one or more cost functions to determine relative costs of different deployment configuration options for deploying software to a vehicle, such as resource costs (e.g., bandwidth, compute, memory, etc.), isolation costs (e.g., limited access to input information, limited connectivity to other ECUs, etc.), performance costs, etc. Based on the evaluation of the one or more cost functions, the vehicle software deployment system determines an optimized deployment plan. Also, the vehicle software deployment system receives telemetry data from the vehicle and automatically determines updated optimized deployment plans in response to changes in configuration of the vehicle indicated in the telemetry data.