# Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2402.04720 · HTML: https://mlsanity.com/paper/2402.04720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [BARK: Open Behavior Benchmarking in Multi-Agent Environments](https://mlsanity.com/paper/2003.02604/) (2020)
- [Cyber-Physical Mobility Lab: An Open-Source Platform for Networked and Autonomous Vehicles](https://mlsanity.com/paper/2004.10063/) (2020)
- [Overview of Tools Supporting Planning for Automated Driving](https://mlsanity.com/paper/2003.04081/) (2020)

## Built on by (most cited first, 3 of 3)

- [MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation](https://mlsanity.com/paper/2505.13959/) (2025)
- [Multi-Agent Coordination in Autonomous Vehicle Routing: A Simulation-Based Study of Communication, Memory, and Routing Loops](https://mlsanity.com/paper/2511.17656/) (2025)
- [Declarative Scenario-based Testing with RoadLogic](https://mlsanity.com/paper/2603.09455/) (2026)

## Abstract (arXiv)

Current validation methods often rely on recorded data and basic functional checks, which may not be sufficient to encompass the scenarios an autonomous vehicle might encounter. In addition, there is a growing need for complex scenarios with changing vehicle interactions for comprehensive validation. This work introduces a novel synchronous multi-agent simulation framework for autonomous vehicles in interactive scenarios. Our approach creates an interactive scenario and incorporates publicly available edge-case scenarios wherein simulated vehicles are replaced by agents navigating to predefined destinations. We provide a platform that enables the integration of different autonomous driving planning methodologies and includes a set of evaluation metrics to assess autonomous driving behavior. Our study explores different planning setups and adjusts simulation complexity to test the framework's adaptability and performance. Results highlight the critical role of simulating vehicle interactions to enhance autonomous driving systems. Our setup offers unique insights for developing advanced algorithms for complex driving tasks to accelerate future investigations and developments in this field. The multi-agent simulation framework is available as open-source software: https://github.com/TUM-AVS/Frenetix-Motion-Planner