# SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic

arXiv: https://arxiv.org/abs/1911.04074 · HTML: https://mlsanity.com/paper/1911.04074/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 30 of 40)

- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024)
- [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022)
- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (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)
- [GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS](https://mlsanity.com/paper/2408.01584/) (2024)
- [A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models](https://mlsanity.com/paper/2502.13187/) (2025)
- [Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization](https://mlsanity.com/paper/2110.13827/) (2021)
- [Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments](https://mlsanity.com/paper/2503.22496/) (2025)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility](https://mlsanity.com/paper/2407.08725/) (2024)
- [What Truly Matters in Trajectory Prediction for Autonomous Driving?](https://mlsanity.com/paper/2306.15136/) (2023)
- [Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks](https://mlsanity.com/paper/2106.13052/) (2021)
- [Closing the Planning-Learning Loop with Application to Autonomous Driving](https://mlsanity.com/paper/2101.03834/) (2021)
- [Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections](https://mlsanity.com/paper/2301.05294/) (2023)
- [LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty](https://mlsanity.com/paper/2209.11422/) (2022)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion](https://mlsanity.com/paper/2308.06931/) (2023)
- [TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters](https://mlsanity.com/paper/2405.04491/) (2024)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios](https://mlsanity.com/paper/2109.10557/) (2021)
- [WorldGen: A Large Scale Generative Simulator](https://mlsanity.com/paper/2210.00715/) (2022)
- [An Intelligent Self-driving Truck System For Highway Transportation](https://mlsanity.com/paper/2112.15304/) (2021)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [Getting SMARTER for Motion Planning in Autonomous Driving Systems](https://mlsanity.com/paper/2502.15824/) (2025)
- [TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments](https://mlsanity.com/paper/2208.07943/) (2022)
- [TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles](https://mlsanity.com/paper/2408.15538/) (2024)
- [Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios](https://mlsanity.com/paper/2503.23708/) (2025)
- [OpenPodcar: an Open Source Vehicle for Self-Driving Car Research](https://mlsanity.com/paper/2205.04454/) (2022)

## Abstract (arXiv)

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants. This paper presents SUMMIT, a high-fidelity simulator that facilitates the development and testing of crowd-driving algorithms. By leveraging the open-source OpenStreetMap map database and a heterogeneous multi-agent motion prediction model developed in our earlier work, SUMMIT simulates dense, unregulated urban traffic for heterogeneous agents at any worldwide locations that OpenStreetMap supports. SUMMIT is built as an extension of CARLA and inherits from it the physics and visual realism for autonomous driving simulation. SUMMIT supports a wide range of applications, including perception, vehicle control and planning, and end-to-end learning. We provide a context-aware planner together with benchmark scenarios and show that SUMMIT generates complex, realistic traffic behaviors in challenging crowd-driving settings.