AV Simulation Scheduling Model for Concurrent Task Execution
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Solution Overview
Problem
The high cost and inefficiency of training and testing autonomous vehicles in the physical world, coupled with the challenge of creating or simulating diverse driving scenarios, necessitate an effective infrastructure for autonomous vehicle simulation and code build scheduling to reduce physical road tests and accelerate algorithm development.
Innovation Solution
A completion time-driven scheduling model utilizing machine learning and resource-aware pipeline scheduling is implemented to efficiently allocate resources for autonomous vehicle simulation and code build jobs, optimizing the use of different worker types and resource capacities within a cloud platform, allowing for concurrent task execution and prioritization of critical tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles are trained and tested in the physical world, then realistic driving scenarios can be covered, but the cost and time consumption increase significantly
Solution Approach 1:
The patent creates virtual copies of physical driving environments through simulation systems. These simulations replicate real-world road conditions, traffic scenarios, and environmental factors, allowing comprehensive testing without physical deployment. The virtual environment serves as a faithful reproduction that enables repeated, cost-free testing iterations.
Solution Approach 2:
The system performs preliminary testing and validation in virtual simulations before any physical road tests. By conducting exhaustive scenario coverage, edge case testing, and algorithm validation in the virtual environment first, the patent eliminates the need for repeated physical testing, significantly reducing time and cost while maintaining testing rigor.
2Adaptability or versatility
If diverse driving scenarios are created through physical road tests, then comprehensive training data can be obtained, but the infrastructure cost and complexity increase
Solution Approach 1:
The simulation system creates virtual replicas of diverse driving scenarios including different weather conditions, traffic patterns, road types, and environmental obstacles. These virtual scenarios provide unlimited scenario diversity without requiring physical reconstruction of each environment, eliminating infrastructure complexity while maintaining scenario versatility.
Solution Approach 2:
The virtual environment dynamically generates and modifies driving scenarios in real-time, allowing the system to adapt to any testing requirement. Scenarios can be changed, combined, or extreme conditions created without physical constraints, providing infinite scenario diversity with simple computational infrastructure.
3Productivity
If multiple AV simulation and code build jobs are run concurrently, then development efficiency improves, but resource management complexity and scheduling difficulty increase
Solution Approach 1:
The patent employs machine learning models that continuously learn from historical job execution data to predict task runtimes with high accuracy. These predicted runtime parameters are fed into the scheduling system, which automatically optimizes resource allocation and job sequencing. The system dynamically adjusts scheduling parameters based on learned patterns, achieving efficient concurrent execution without manual scheduling complexity.
Solution Approach 2:
The scheduling system autonomously manages concurrent simulation and code build jobs by automatically predicting runtimes, allocating resources, and optimizing execution sequences. The machine learning component continuously improves scheduling decisions by learning from past performance data, enabling the system to self-optimize without external intervention or complex manual configuration.
Data Source
AI summary
Systems and methods for autonomous vehicle (AV) simulation and code build scheduling are provided. A method includes receiving a first task specification for a first task associated with a first AV simulation and/or a first AV code build, receiving, a second task specification for a second task associated with a second AV simulation and/or a second AV code build, and executing a portion of the first task concurrently with a portion of the second task based on the portion of the first task and the portion of the second task have different resource requirements. The portion of the first task is associated with one of an AV asset download, an AV code execution, or an AV artifact upload. The portion of the second task is associated with a different one of the AV asset download, the AV code execution, or the AV artifact upload.