SUMO2Unity: An Open-Source Traffic Co-Simulation Tool to Improve Road Safety
Bibliographic record
Abstract
In traffic safety research, simulation tools are considered more straightforward and cost-effective than direct observations of real-world conditions, especially when dealing with scenarios that may not exist in reality. The tools include traffic micro-simulation tools (e.g., SUMO) and driver simulators developed in game engines (e.g., Unity). However, the tools also have limitations. For example, the equations used to simulate human behavior may not always reflect real-world behavior accurately, and driver simulators’ lack of realistic traffic systems affect the interaction between the simulator vehicle and other vehicles. Co-simulation allows two different simulation tools to exchange data to enhance the capabilities of each tool, but many traffic safety researchers currently spend significant amounts of time, effort, and budget working on their own version of a co-simulation tool to integrate, for example, a traffic micro-simulation tool such as SUMO with a driver simulator such as the Unity game engine. This situation takes time away from focusing on the goal of improving traffic safety. In this paper, we developed an open-source traffic co-simulation tool. Development involved three tasks: 1. integration of SUMO and Unity; 2. development of a 2D and 3D environment (a 3D road environment in Unity was generated from a 2D road environment in SUMO); and 3. development of a 3D model of a simulator vehicle and development of a VR-based driver simulator. We named our tool SUMO2Unity and believe that it can significantly help traffic safety researchers to conduct future research aimed at improving traffic safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".