# EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning

arXiv: https://arxiv.org/abs/2311.12261 · HTML: https://mlsanity.com/paper/2311.12261/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 56 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments](https://mlsanity.com/paper/1705.01693/) (2017)
- [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023)
- [A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning](https://mlsanity.com/paper/2007.05156/) (2020)
- [Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning](https://mlsanity.com/paper/2306.08094/) (2023)
- [Limitations and Improvements of the Intelligent Driver Model (IDM)](https://mlsanity.com/paper/2104.02583/) (2021)
- [Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections](https://mlsanity.com/paper/2301.05294/) (2023)
- [Large-scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing](https://mlsanity.com/paper/2311.11347/) (2023)
- [Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study](https://mlsanity.com/paper/1811.06395/) (2018)
- [Mixed Traffic Control and Coordination from Pixels](https://mlsanity.com/paper/2302.09167/) (2023)
- [Smoothing Traffic Flow via Control of Autonomous Vehicles](https://mlsanity.com/paper/1812.09544/) (2018)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)
- [Controllability Analysis and Optimal Control of Mixed Traffic Flow with Human-driven and Autonomous Vehicles](https://mlsanity.com/paper/2002.02099/) (2020)
- [Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model](https://mlsanity.com/paper/2203.04749/) (2022)
- [Prediction Based Decision Making for Autonomous Highway Driving](https://mlsanity.com/paper/2209.02106/) (2022)

## Built on by (most cited first, 9 of 9)

- [Mixture-of-Personas Language Models for Population Simulation](https://mlsanity.com/paper/2504.05019/) (2025)
- [CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control](https://mlsanity.com/paper/2404.00796/) (2024)
- [Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.04691/) (2025)
- [Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2403.14879/) (2024)
- [Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature Learning in Trajectory Prediction](https://mlsanity.com/paper/2412.13419/) (2024)
- [Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems](https://mlsanity.com/paper/2507.09836/) (2025)
- [Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks](https://mlsanity.com/paper/2412.12622/) (2024)
- [LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2504.05062/) (2025)
- [Learning to Design City-scale Transit Routes](https://mlsanity.com/paper/2512.19767/) (2025)

## Abstract (arXiv)

Human-driven vehicles (HVs) amplify naturally occurring perturbations in traffic, leading to congestion--a major contributor to increased fuel consumption, higher collision risks, and reduced road capacity utilization. While previous research demonstrates that Robot Vehicles (RVs) can be leveraged to mitigate these issues, most such studies rely on simulations with simplistic models of human car-following behaviors. In this work, we analyze real-world driving trajectories and extract a wide range of acceleration profiles. We then incorporates these profiles into simulations for training RVs to mitigate congestion. We evaluate the safety, efficiency, and stability of mixed traffic via comprehensive experiments conducted in two mixed traffic environments (Ring and Bottleneck) at various traffic densities, configurations, and RV penetration rates. The results show that under real-world perturbations, prior RV controllers experience performance degradation on all three objectives (sometimes even lower than 100% HVs). To address this, we introduce a reinforcement learning based RV that employs a congestion stage classifier to optimize the safety, efficiency, and stability of mixed traffic. Our RVs demonstrate significant improvements: safety by up to 66%, efficiency by up to 54%, and stability by up to 97%.