# smirk

> SMIRK is an experimental pedestrian emergency breaking ADAS facilitating research on quality assurance of critical components that rely on machine learning.

**Wikidata**: [Q127485674](https://www.wikidata.org/wiki/Q127485674)  
**Source**: https://4ort.xyz/entity/smirk-q127485674

## Summary
SMIRK is an experimental pedestrian emergency braking Advanced Driver Assistance System (ADAS) designed to support research on quality assurance for machine learning-based critical components. It facilitates testing and validation of safety-critical systems in autonomous driving.

## Key Facts
- **Type**: Software-based ADAS for pedestrian emergency braking
- **Primary Function**: Research tool for quality assurance of machine learning components
- **Latest Version**: 1.0 (released 2023-02-07)
- **Previous Version**: 0.99 (released 2022-06-30)
- **Source Code**: Hosted on GitHub at [https://github.com/RI-SE/smirk](https://github.com/RI-SE/smirk)
- **Classification**: Instance of software
- **Description**: Experimental system for testing ADAS reliability in pedestrian collision scenarios

## FAQs
### Q: What is SMIRK used for?
A: SMIRK is an experimental ADAS designed to research quality assurance for machine learning-based pedestrian emergency braking systems. It simulates real-world scenarios to test the reliability of critical components.

### Q: Where can I find SMIRK's source code?
A: The source code is available on GitHub at [https://github.com/RI-SE/smirk](https://github.com/RI-SE/smirk).

### Q: What versions of SMIRK are available?
A: SMIRK has two stable versions: 0.99 (released June 30, 2022) and 1.0 (released February 7, 2023).

### Q: Is SMIRK a commercial product?
A: No, SMIRK is an experimental research tool, not a commercial product.

### Q: What makes SMIRK unique?
A: SMIRK focuses specifically on pedestrian emergency braking scenarios, providing a specialized research platform for machine learning-based ADAS quality assurance.

## Why It Matters
SMIRK plays a crucial role in advancing the safety and reliability of autonomous driving systems by addressing the challenges of machine learning-based critical components. As ADAS technology evolves, ensuring the robustness of pedestrian emergency braking systems is vital for public safety. SMIRK’s experimental approach allows researchers to systematically test and validate these systems under various conditions, helping to identify and mitigate potential failures before they occur in real-world deployments. By focusing on quality assurance, SMIRK contributes to the broader goal of making autonomous vehicles safer for pedestrians and other vulnerable road users.

## Notable For
- **Specialized Research Tool**: Focuses exclusively on pedestrian emergency braking scenarios, unlike general-purpose ADAS testing frameworks.
- **Machine Learning Validation**: Designed to assess the reliability of machine learning components in critical safety systems.
- **Open-Source Accessibility**: Source code is publicly available on GitHub, enabling researchers to contribute and build upon the project.
- **Versioned Releases**: Multiple stable versions (0.99 and 1.0) demonstrate iterative development and refinement.
- **Experimental Design**: Serves as a platform for testing and validating ADAS performance in high-stakes situations.

## Body
### Overview
SMIRK is an experimental Advanced Driver Assistance System (ADAS) designed to facilitate research on quality assurance for machine learning-based pedestrian emergency braking systems. It is classified as software and serves as a research tool rather than a commercial product.

### Versions and Releases
SMIRK has two stable versions:
- **Version 0.99**: Released on June 30, 2022, with a stable version designation.
- **Version 1.0**: Released on February 7, 2023, marked as the preferred stable version.

Both versions are documented on GitHub, with release notes available for reference.

### Source Code and Accessibility
The source code for SMIRK is hosted on GitHub at [https://github.com/RI-SE/smirk](https://github.com/RI-SE/smirk). The repository is publicly accessible, allowing researchers to review, modify, and contribute to the project.

### Research Focus
SMIRK is specifically designed to test and validate the reliability of machine learning components in pedestrian emergency braking scenarios. Its experimental nature makes it a valuable tool for identifying and addressing potential failures in critical safety systems.

### Classification and Function
As an instance of software, SMIRK is a non-tangible executable component of a computer. Its primary function is to support research in quality assurance for machine learning-based ADAS systems, particularly in high-stakes pedestrian collision scenarios.

## References

1. [Release 0.99. 2022](https://github.com/RI-SE/smirk/releases/tag/v0.99)
2. [Release 1.0. 2023](https://github.com/RI-SE/smirk/releases/tag/v1.0)