DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929635082633216 |
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| author | Cheng, Mingfei Zhou, Yuan Xie, Xiaofei |
| author_facet | Cheng, Mingfei Zhou, Yuan Xie, Xiaofei |
| contents | Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is the complexity of preparing and configuring simulation environments, particularly in terms of compatibility and stability between the simulator and the ADS. This complexity often results in researchers dedicating significant effort to customize their own environments, leading to disparities in development platforms and underlying systems. Consequently, reproducing and comparing these methodologies on a unified ADS testing platform becomes difficult. To address these challenges, we introduce DriveTester, a unified simulation-based testing platform built on Apollo, one of the most widely used open-source, industrial-level ADS platforms. DriveTester provides a consistent and reliable environment, integrates a lightweight traffic simulator, and incorporates various state-of-the-art ADS testing techniques. This enables researchers to efficiently develop, test, and compare their methods within a standardized platform, fostering reproducibility and comparison across different ADS testing approaches. The code is available: https://github.com/MingfeiCheng/DriveTester. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12656 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing Cheng, Mingfei Zhou, Yuan Xie, Xiaofei Software Engineering Artificial Intelligence Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is the complexity of preparing and configuring simulation environments, particularly in terms of compatibility and stability between the simulator and the ADS. This complexity often results in researchers dedicating significant effort to customize their own environments, leading to disparities in development platforms and underlying systems. Consequently, reproducing and comparing these methodologies on a unified ADS testing platform becomes difficult. To address these challenges, we introduce DriveTester, a unified simulation-based testing platform built on Apollo, one of the most widely used open-source, industrial-level ADS platforms. DriveTester provides a consistent and reliable environment, integrates a lightweight traffic simulator, and incorporates various state-of-the-art ADS testing techniques. This enables researchers to efficiently develop, test, and compare their methods within a standardized platform, fostering reproducibility and comparison across different ADS testing approaches. The code is available: https://github.com/MingfeiCheng/DriveTester. |
| title | DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2412.12656 |