AUTOSAR Algorithm Task Scheduling via Dependency Graphs
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Solution Overview
Problem
The existing procedure for implementing functions of algorithms using AUTOSAR is complex and inefficient, requiring developers to perform extensive configuration using multiple tools.
Innovation Solution
A task scheduling method that utilizes an interface function to obtain and parse registration information of an algorithm, generating a dependency graph and execution flow graph, and schedules tasks using pre-deployed software components, thereby simplifying the algorithm deployment process and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the existing AUTOSAR procedure is used to implement algorithm functions, then the system provides standardized software architecture, but the deployment procedure becomes complex and efficiency is reduced
Solution Approach 1:
The patent extracts the core algorithm deployment functionality from the complex AUTOSAR configuration process. By creating a standalone algorithm management component that handles task registration, dependency analysis, and scheduling independently, the solution removes the burden of complex multi-tool configuration while maintaining AUTOSAR compatibility.
Solution Approach 2:
The patent introduces an algorithm management component as an intermediary layer between the algorithm code and the AUTOSAR scheduling system. This mediator automatically generates execution flow graphs from dependency relationships, translating high-level algorithm descriptions into AUTOSAR-compatible task schedules without requiring developers to manually configure multiple tools.
2Productivity
If manual configuration using multiple AUTOSAR tools is performed, then complete system integration is achieved, but deployment time and complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-compiling algorithm dependency relationships into execution flow graphs during the algorithm development phase. These pre-processed execution graphs are stored and automatically loaded by the algorithm management component, eliminating the need for time-consuming manual configuration during deployment.
Solution Approach 2:
The patent creates simplified copies of the AUTOSAR configuration process through automatic code generation. The algorithm management component generates standard AUTOSAR configuration files and task schedules from the execution flow graph, providing a streamlined alternative to manual multi-tool configuration while maintaining full AUTOSAR compatibility.
3Reliability
If tasks are scheduled sequentially, then resource conflicts are avoided, but task scheduling delays increase
Solution Approach 1:
The patent implements dynamic task scheduling by continuously monitoring task execution states and resource availability. The algorithm management component dynamically adjusts the execution order of tasks based on real-time conditions, enabling parallel execution of independent tasks while automatically preventing resource conflicts through dependency-based scheduling constraints.
Solution Approach 2:
The patent incorporates feedback mechanisms where the algorithm management component monitors task execution progress and resource status in real-time. Based on this feedback, the system dynamically recalculates optimal execution schedules, allowing parallel task execution when resources are available while maintaining reliability by preventing resource conflicts through continuous state monitoring.
Data Source
AI summary
A task scheduling method and an apparatus that belongs to the field of intelligent vehicles is provided. The method may be applied to an embedded device using AUTomotive Open System Architecture (AUTOSAR), the embedded device includes a memory and a processor, the memory stores an interface function, and a first software component and a second software component are deployed in the processor. In this solution, registration information of a to-be-deployed algorithm may be obtained and parsed by using the interface function, and a task in the algorithm may be scheduled and executed by using the software component.