# Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive Vehicles

arXiv: https://arxiv.org/abs/2410.15912 · HTML: https://mlsanity.com/paper/2410.15912/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 50 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 29 of 29)

- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](https://mlsanity.com/paper/2406.15349/) (2024)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023)
- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes](https://mlsanity.com/paper/2305.10430/) (2023)
- [Guided Conditional Diffusion for Controllable Traffic Simulation](https://mlsanity.com/paper/2210.17366/) (2022)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [Baidu Apollo EM Motion Planner](https://mlsanity.com/paper/1807.08048/) (2018)
- [BITS: Bi-level Imitation for Traffic Simulation](https://mlsanity.com/paper/2208.12403/) (2022)
- [TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction](https://mlsanity.com/paper/2303.04116/) (2023)
- [BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction](https://mlsanity.com/paper/2405.17372/) (2024)
- [Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic](https://mlsanity.com/paper/2105.05701/) (2021)
- [Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023](https://mlsanity.com/paper/2306.11868/) (2023)
- [EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments](https://mlsanity.com/paper/2108.07993/) (2021)
- [Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic](https://mlsanity.com/paper/1906.11021/) (2019)
- [METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors](https://mlsanity.com/paper/2109.07648/) (2021)
- [MARC: Multipolicy and Risk-aware Contingency Planning for Autonomous Driving](https://mlsanity.com/paper/2308.12021/) (2023)
- [Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching](https://mlsanity.com/paper/2003.02746/) (2020)
- [Autonomous Highway Merging in Mixed Traffic Using Reinforcement Learning and Motion Predictive Safety Controller](https://mlsanity.com/paper/2204.01024/) (2022)
- [Robust Driving Policy Learning with Guided Meta Reinforcement Learning](https://mlsanity.com/paper/2307.10160/) (2023)
- [Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks](https://mlsanity.com/paper/2403.19633/) (2024)
- [Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging](https://mlsanity.com/paper/2310.07840/) (2023)
- [Exploring the stimulative effect on following drivers in a consecutive lane-change using microscopic vehicle trajectory data](https://mlsanity.com/paper/2205.11252/) (2022)
- [Goal-Aware RSS for Complex Scenarios via Program Logic](https://mlsanity.com/paper/2207.02387/) (2022)
- [Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages](https://mlsanity.com/paper/2103.09726/) (2021)

## Abstract (arXiv)

While the capabilities of autonomous driving have advanced rapidly, merging into dense traffic remains a significant challenge, many motion planning methods for this scenario have been proposed but it is hard to evaluate them. Most existing closed-loop simulators rely on rule-based controls for other vehicles, which results in a lack of diversity and randomness, thus failing to accurately assess the motion planning capabilities in highly interactive scenarios. Moreover, traditional evaluation metrics are insufficient for comprehensively evaluating the performance of merging in dense traffic. In response, we proposed a closed-loop evaluation benchmark for assessing motion planning capabilities in merging scenarios. Our approach involves other vehicles trained in large scale datasets with micro-behavioral characteristics that significantly enhance the complexity and diversity. Additionally, we have restructured the evaluation mechanism by leveraging Large Language Models (LLMs) to assess each autonomous vehicle merging onto the main lane. Extensive experiments and test-vehicle deployment have demonstrated the progressiveness of this benchmark. Through this benchmark, we have obtained an evaluation of existing methods and identified common issues. The simulation environment and evaluation process can be accessed at https://github.com/WZM5853/Bench4Merge.