# Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

arXiv: https://arxiv.org/abs/1705.01693 · HTML: https://mlsanity.com/paper/1705.01693/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2017.

## Built on by (most cited first, 30 of 45)

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- [Unified Automatic Control of Vehicular Systems with Reinforcement Learning](https://mlsanity.com/paper/2208.00268/) (2022)
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- [Automatic vehicle trajectory data reconstruction at scale](https://mlsanity.com/paper/2212.07907/) (2022)
- [Car-Following Models: A Multidisciplinary Review](https://mlsanity.com/paper/2304.07143/) (2023)
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- [Parameterized Decision-making with Multi-modal Perception for Autonomous Driving](https://mlsanity.com/paper/2312.11935/) (2023)
- [CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control](https://mlsanity.com/paper/2404.00796/) (2024)
- [Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control](https://mlsanity.com/paper/2408.17380/) (2024)
- [Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2103.03796/) (2021)
- [CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.13143/) (2022)
- [Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy](https://mlsanity.com/paper/2312.09436/) (2023)
- [Offline Reinforcement Learning for Road Traffic Control](https://mlsanity.com/paper/2201.02381/) (2022)
- [Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction](https://mlsanity.com/paper/2112.03759/) (2021)
- [Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments](https://mlsanity.com/paper/2302.07453/) (2023)
- [PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems](https://mlsanity.com/paper/2308.00864/) (2023)
- [Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy](https://mlsanity.com/paper/2106.04678/) (2021)
- [The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking](https://mlsanity.com/paper/2308.14833/) (2023)
- [Vessel-following model for inland waterways based on deep reinforcement learning](https://mlsanity.com/paper/2207.03257/) (2022)
- [Artificial Intelligence for Smart Transportation](https://mlsanity.com/paper/2308.07457/) (2023)
- [Vehicle-group-based Crash Risk Prediction and Interpretation on Highways](https://mlsanity.com/paper/2402.12415/) (2024)
- [Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data](https://mlsanity.com/paper/2105.01820/) (2021)
- [Learning energy-efficient driving behaviors by imitating experts](https://mlsanity.com/paper/2208.12534/) (2022)
- [Learning "Look-Ahead" Nonlocal Traffic Dynamics in a Ring Road](https://mlsanity.com/paper/2312.02770/) (2023)
- [RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality](https://mlsanity.com/paper/2312.07003/) (2023)
- [Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test](https://mlsanity.com/paper/2402.17050/) (2024)
- [Cooperative Advisory Residual Policies for Congestion Mitigation](https://mlsanity.com/paper/2407.00553/) (2024)
- [Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles](https://mlsanity.com/paper/2412.04341/) (2024)
- [Optimal Smoothing Distribution Exploration for Backdoor Neutralization in Deep Learning-based Traffic Systems](https://mlsanity.com/paper/2303.14197/) (2023)

## Abstract (arXiv)

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.