Preliminary Approach to Parallelizing Autonomous Driving Applications Using High-Performance Many-core Processor

Advancements in autonomous driving technologies leverage real-time computing and embedded systems to enable vehicles to make quick decisions based on dynamic road conditions. These increasingly complex systems face rising computational demands. This study introduces a preliminary approach to parallelizing autonomous driving applications using a high-performance many-core processor. By distributing tasks across multiple cores, the approach enhances concurrent execution, reduces conflicts, and minimizes resource contention, improving the efficiency and performance of autonomous driving systems.

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Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/rtcsa62462.2024.00030
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
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article

Preliminary Approach to Parallelizing Autonomous Driving Applications Using High-Performance Many-core Processor

Takuya Azumi, Xuankeng He
Parallel Computing and Optimization Techniques
article

Preliminary Approach to Parallelizing Autonomous Driving Applications Using High-Performance Many-core Processor

Takuya Azumi, Xuankeng He
article en

Abstract

Advancements in autonomous driving technologies leverage real-time computing and embedded systems to enable vehicles to make quick decisions based on dynamic road conditions. These increasingly complex systems face rising computational demands. This study introduces a preliminary approach to parallelizing autonomous driving applications using a high-performance many-core processor. By distributing tasks across multiple cores, the approach enhances concurrent execution, reduces conflicts, and minimizes resource contention, improving the efficiency and performance of autonomous driving systems.

Saitama University (JP)
Affordable and clean energy
Openalex Percentile: Top 83%
Parallel Computing and Optimization Techniques
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