Flow: A Modular Learning Framework for Mixed Autonomy Traffic
By Cathy Wu, Abdul Rahman Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre M. Bayen. 2017
230 citations recorded by the source corpus.
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partial control and observation, multivehicle interactions, and the sheer variety of scenarios represented by real-world networks. To shed light into near-term AV impacts, this article studies the suitability of deep reinforcement learning (RL) for overcoming these challenges in a low AV-adoption regime. A modular learning framework is presented, which leverages deep RL to address complex traffic dynamics. Modules are composed to capture common traffic phenomena (stop-and-go traffic jams, lane changing, intersections). Learned control laws are found to improve upon human driving performance, in terms of system-level velocity, by up to 57% with only 4–7% adoption of AVs. Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic—surpassing all known model-based controllers to achieve near-optimal performance—and generalize to out-of-distribution traffic densities.
Subfields: Artificial Intelligence, Automotive Engineering, Control and Systems Engineering, Reinforcement Learning, Robotics & Control
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kush Bhatia
- Alexander Pan
- Jacob Steinhardt
- Percy Liang
- Gregory Valiant
- Dan Hendrycks
- J. Kolter
- Jerry Zheng Li
In Control and Systems Engineering
In Reinforcement Learning
In Robotics & Control
Citations in the corpus
- The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models (2022, methodology): “We use the Flow traffic simulator, implemented by Wu et al. (2021) and Vinitsky et al. (2018), which extends the popular SUMO traffic simulator (Lopez et al., 2018).”