Autonomous Vehicle Data-Arrival Scheduling for Real-Time Failover
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Solution Overview
Problem
Autonomous driving systems face challenges in performing operations within strict time windows due to the need for hard real-time processing, leading to potential system failures if deadlines are not met.
Innovation Solution
Implementing process scheduling based on data arrival in autonomous vehicles, utilizing redundant processing units, redundant power and data fabrics, and dynamic switching mechanisms to ensure continuous operation even in the event of component failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hard real-time scheduling is used to ensure timely processing of sensor data, then system reliability is improved, but system complexity increases and the system becomes vulnerable to failures when data arrival does not meet strict time constraints
Solution Approach 1:
The patent implements dynamic process scheduling where the scheduling algorithm adapts to actual data arrival patterns in real-time. Instead of rigid pre-defined schedules, the system dynamically adjusts process execution timing and selection based on when sensor data actually arrives, allowing the scheduling mechanism to be flexible rather than static.
Solution Approach 2:
The system changes scheduling parameters dynamically based on data arrival characteristics. When data arrives within expected time windows, normal processing schedules are used. When data arrival deviates from expectations, the system modifies scheduling parameters such as process priority, execution timing, and selection criteria to accommodate the actual data arrival pattern.
2Reliability
If strict time constraints are imposed on data processing, then real-time operation quality is improved, but the system becomes vulnerable to failures when data arrival does not meet these constraints
Solution Approach 1:
The patent employs dynamic scheduling that adapts to varying data arrival patterns. The system monitors actual data arrival times and adjusts processing schedules accordingly, transitioning between different operational modes based on whether data arrives within expected time windows or experiences delays, thus maintaining reliability while adapting to temporal variations.
Solution Approach 2:
The system prepares multiple process schedules in advance - a first schedule for normal operation when data arrives on time, and a second schedule for degraded operation when data arrival is delayed. This pre-prepared flexibility allows the system to switch between operational modes without failure, cushioning against the harmful effects of data arrival variations.
3Adaptability or versatility
If multiple process schedules are maintained for different data arrival scenarios, then adaptability is improved, but memory usage and system complexity increase
Solution Approach 1:
The patent segments the scheduling system into distinct operational modes with predefined schedules - a first process schedule for normal data arrival and a second process schedule for delayed data arrival. By dividing the scheduling problem into discrete segments rather than maintaining a continuous complex schedule, the system achieves adaptability while managing memory resources efficiently through structured segmentation.
Data Source
AI summary
Process scheduling based on data arrival in an autonomous vehicle, including: receiving, by a node and from one or more other nodes of a distributed automation computing system, a plurality of portions of data; generating a process schedule by scheduling, for each portion of data of the plurality of portions of data, a process for processing a corresponding portion of data within a time window, wherein an ordering of the process schedule corresponds to an order of arrival of the plurality of portions of data; and executing, during the time window, the process schedule.