sim_bug_tools - Python 3.9

A toolkit for exploring bugs in software simulations using a geometric approach. The toolkit is implemented as a Python 3.9 module.

scenarioxp - Python 3.9

A complete black-box meta-heuristic scenario-based targeted testing regime which explores and exploits the geometric properties of scenarios. 

scenarioxp is implemented as a Python 3.9 module that utilizes sim_bug_tools for practical applications.

avit-entropy - Python 3.9

A practical application of information theory to quantify complexity of driving scenarios. avit-entropy is implemented as a Python 3.9 module. 

Additionally, we leverage the avit-entropy module to evaluate driving scenarios made in SUMO simulator. Github Repo

Modular and Measuable AV Scenarios in M-SDL

Our goal is to validate the decision making layer of autonomous vehicles (AV). This repo is one of many steps in that journey. Here, we construct logical scenarios called "atomic blocks" from a dataset of function scenarios using the Measurable Scenario Description Language (M-SDL) by Fortellix.

Integrated Scenario-Based Testing and Explanation Framework for Autonomous Vehicles

Source code and examples for our paper subission to IEEE MOST 2024 "An Integrated Scenario-Based Testing and Explanation Framework for Autonomous Vehicles" 

This utilizes a developmental version of our autonomous vehicle testing and interpretation strategy.

62-D Traffic Scenario

This github repository features a 62 dimension traffic scenario constructed using SUMO and  state-of-the-art Autonomous Vehicle Validation and Verification Scenario modeling methodologies. This repo contains a logical scenario with 62 parameter ranges, 15 performance metrics, and a dataset of 90,000 simulation tests configurations and results.

SUMO - SCENIC Interface  

This is a developmental version of SUMO Traffic Simulator interface for Scenic 2.0.0. The SUMO-SCENIC project has papers accepted in IEEE IPCCC 2024 and published IEEE MOST 2023.