# Optimizing Multi-Lane Intersection Performance in Mixed Autonomy Environments

arXiv: https://arxiv.org/abs/2511.02217 · HTML: https://mlsanity.com/paper/2511.02217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Mixed platoon control of automated and human-driven vehicles at a signalized intersection: dynamical analysis and optimal control](https://mlsanity.com/paper/2010.16105/) (2020)
- [Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study](https://mlsanity.com/paper/2201.13229/) (2022)
- [Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow](https://mlsanity.com/paper/2108.13038/) (2021)

## Built on by (most cited first, 1 of 1)

- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)

## Abstract (arXiv)

One of the main challenges in managing traffic at multilane intersections is ensuring smooth coordination between human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). This paper presents a novel traffic signal control framework that combines Graph Attention Networks (GAT) with Soft Actor-Critic (SAC) reinforcement learning to address this challenge. GATs are used to model the dynamic graph- structured nature of traffic flow to capture spatial and temporal dependencies between lanes and signal phases. The proposed SAC is a robust off-policy reinforcement learning algorithm that enables adaptive signal control through entropy-optimized decision making. This design allows the system to coordinate the signal timing and vehicle movement simultaneously with objectives focused on minimizing travel time, enhancing performance, ensuring safety, and improving fairness between HDVs and CAVs. The model is evaluated using a SUMO-based simulation of a four-way intersection and incorporating different traffic densities and CAV penetration rates. The experimental results demonstrate the effectiveness of the GAT-SAC approach by achieving a 24.1% reduction in average delay and up to 29.2% fewer traffic violations compared to traditional methods. Additionally, the fairness ratio between HDVs and CAVs improved to 1.59, indicating more equitable treatment across vehicle types. These findings suggest that the GAT-SAC framework holds significant promise for real-world deployment in mixed-autonomy traffic systems.