Autonomous Vehicle Task Scheduling With Precompiled Event Loops
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Solution Overview
Problem
Current operating system schedulers for autonomous vehicles perform poorly due to their failure to consider task dependencies, required computing units, and other critical factors, leading to inefficiencies in scheduling and execution.
Innovation Solution
A method and system for scheduling tasks in autonomous vehicles that identify fixed and variable parameters prior to runtime, develop a schedule with an event loop to minimize execution time, and implement the plan using data from sensors, potentially skipping or terminating tasks if their execution time exceeds the maximum allowed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current operating system schedulers are used, then the system is simple to implement, but task execution efficiency deteriorates due to ignoring task dependencies and computing unit requirements
Solution Approach 1:
The patent applies preliminary action by pre-compiling task schedules and execution plans before runtime. The system identifies tasks, their dependencies, and required computing units in advance, then compiles an optimized execution plan that minimizes overall execution time. This pre-planning approach resolves the contradiction by establishing efficient task scheduling beforehand, avoiding the need for complex real-time scheduling decisions during runtime.
2Productivity
If task schedules are optimized to minimize execution time, then productivity improves, but the complexity of schedule compilation increases
Solution Approach 1:
The patent applies segmentation by dividing the scheduling process into distinct phases: task identification, dependency analysis, schedule development, and execution plan compilation. Each phase handles specific aspects of the scheduling problem independently, making the overall complex task more manageable. The system segments tasks into those with dependencies and those without, and separately handles schedule optimization for each category.
Solution Approach 2:
The system performs preliminary analysis of task dependencies and computing unit requirements before compiling the final schedule. By pre-identifying critical paths and resource constraints, the system reduces the complexity of the actual schedule compilation while achieving optimal execution time.
3Productivity
If all tasks are executed according to schedule, then task completion reliability is maintained, but response time to changing conditions deteriorates due to lack of flexibility
Solution Approach 1:
The patent applies dynamics by creating a flexible execution plan that can adapt to changing conditions during runtime. While the overall schedule is predetermined, the system incorporates dynamic elements that allow tasks to be skipped, suspended, or terminated based on real-time conditions. This dynamic approach maintains reliability for critical tasks while enabling rapid response to changing environmental conditions or sensor data.
Data Source
AI summary
A method of scheduling a plurality of tasks in an autonomous vehicle system (AVS) includes, by a processor, prior to runtime of an autonomous vehicle, identifying a plurality of tasks to be implemented by the AVS of the autonomous vehicle, for each of the tasks, identifying at least one fixed parameter and at least one variable, and developing a schedule for each of the tasks. The schedule includes an event loop that minimizes an overall time for execution of the tasks. The method includes compiling the schedule into an execution plan, and saving the execution plan to a memory of the autonomous vehicle. During runtime of the autonomous vehicle, the processor receives data corresponding to the variables of the tasks, and uses the variables to implement the execution plan on the autonomous vehicle.