# BARK: Open Behavior Benchmarking in Multi-Agent Environments

arXiv: https://arxiv.org/abs/2003.02604 · HTML: https://mlsanity.com/paper/2003.02604/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 21 of 21)

- [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024)
- [The Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2305.12032/) (2023)
- [Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world](https://mlsanity.com/paper/2206.09889/) (2022)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Rethinking Closed-loop Training for Autonomous Driving](https://mlsanity.com/paper/2306.15713/) (2023)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving](https://mlsanity.com/paper/2203.16792/) (2022)
- [What Truly Matters in Trajectory Prediction for Autonomous Driving?](https://mlsanity.com/paper/2306.15136/) (2023)
- [An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility](https://mlsanity.com/paper/2203.07676/) (2022)
- [Learning to Drive via Asymmetric Self-Play](https://mlsanity.com/paper/2409.18218/) (2024)
- [TIP: Task-Informed Motion Prediction for Intelligent Vehicles](https://mlsanity.com/paper/2110.08750/) (2021)
- [RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow](https://mlsanity.com/paper/2211.03408/) (2022)
- [A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios](https://mlsanity.com/paper/2109.10557/) (2021)
- [Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles](https://mlsanity.com/paper/2402.04720/) (2024)
- [Scenario-Based Curriculum Generation for Multi-Agent Autonomous Driving](https://mlsanity.com/paper/2403.17805/) (2024)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Survey](https://mlsanity.com/paper/2508.20315/) (2025)
- [Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving](https://mlsanity.com/paper/2102.03053/) (2021)
- [P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving](https://mlsanity.com/paper/2211.01634/) (2022)
- [Demonstration-guided Deep Reinforcement Learning for Coordinated Ramp Metering and Perimeter Control in Large Scale Networks](https://mlsanity.com/paper/2303.03395/) (2023)
- [A Utility Maximization Model of Pedestrian and Driver Interactions](https://mlsanity.com/paper/2110.11015/) (2021)

## Abstract (arXiv)

Predicting and planning interactive behaviors in complex traffic situations presents a challenging task. Especially in scenarios involving multiple traffic participants that interact densely, autonomous vehicles still struggle to interpret situations and to eventually achieve their own mission goal. As driving tests are costly and challenging scenarios are hard to find and reproduce, simulation is widely used to develop, test, and benchmark behavior models. However, most simulations rely on datasets and simplistic behavior models for traffic participants and do not cover the full variety of real-world, interactive human behaviors. In this work, we introduce BARK, an open-source behavior benchmarking environment designed to mitigate the shortcomings stated above. In BARK, behavior models are (re-)used for planning, prediction, and simulation. A range of models is currently available, such as Monte-Carlo Tree Search and Reinforcement Learning-based behavior models. We use a public dataset and sampling-based scenario generation to show the inter-exchangeability of behavior models in BARK. We evaluate how well the models used cope with interactions and how robust they are towards exchanging behavior models. Our evaluation shows that BARK provides a suitable framework for a systematic development of behavior models.