# Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality

arXiv: https://arxiv.org/abs/2507.03002 · HTML: https://mlsanity.com/paper/2507.03002/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins](https://mlsanity.com/paper/2208.11036/) (2022)
- [Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach](https://mlsanity.com/paper/2005.11064/) (2020)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections](https://mlsanity.com/paper/1904.05423/) (2019)
- [Cooperative Decision Making of Connected Automated Vehicles at Multi-lane Merging Zone: A Coalitional Game Approach](https://mlsanity.com/paper/2103.07887/) (2021)
- [Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approach](https://mlsanity.com/paper/2305.03563/) (2023)
- [Dynamic Game-Theoretical Decision-Making Framework for Vehicle-Pedestrian Interaction with Human Bounded Rationality](https://mlsanity.com/paper/2409.15629/) (2024)
- [Game Theoretic Application to Intersection Management: A Literature Review](https://mlsanity.com/paper/2311.12341/) (2023)
- [CDKFormer: Contextual Deviation Knowledge-Based Transformer for Long-Tail Trajectory Prediction](https://mlsanity.com/paper/2503.12695/) (2025)
- [Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning](https://mlsanity.com/paper/2211.04665/) (2022)
- [Rhythmic Control of Automated Traffic -- Part I: Concept and Properties at Isolated Intersections](https://mlsanity.com/paper/2010.04322/) (2020)
- [Rhythmic Control of Automated Traffic -- Part II: Grid Network Rhythm and Online Routing](https://mlsanity.com/paper/2010.05416/) (2020)

## Built on by (most cited first, 2 of 2)

- [C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Driving](https://mlsanity.com/paper/2605.10744/) (2026)
- [BAP-SRL: Bayesian Adaptive Priority Safe Reinforcement Learning for Vehicle Motion Planning at Mixed Traffic Intersections](https://mlsanity.com/paper/2601.21679/) (2026)

## Abstract (arXiv)

Modeling the decision-making behavior of vehicles presents unique challenges, particularly during unprotected left turns at intersections, where the uncertainty of human drivers is especially pronounced. In this context, connected autonomous vehicle (CAV) technology emerges as a promising avenue for effectively managing such interactions while ensuring safety and efficiency. Traditional approaches, often grounded in game theory assumptions of perfect rationality, may inadequately capture the complexities of real-world scenarios and drivers' decision-making errors. To fill this gap, we propose a novel decision-making model for vehicle unprotected left-turn scenarios, integrating game theory with considerations for drivers' bounded rationality. Our model, formulated as a two-player normal-form game solved by a quantal response equilibrium (QRE), offers a more nuanced depiction of driver decision-making processes compared to Nash equilibrium (NE) models. Leveraging an Expectation-Maximization (EM) algorithm coupled with a subtle neural network trained on precise microscopic vehicle trajectory data, we optimize model parameters to accurately reflect drivers' interaction-aware bounded rationality and driving styles. Through comprehensive simulation experiments, we demonstrate the efficacy of our proposed model in capturing the interaction-aware bounded rationality and decision tendencies between players. The proposed model proves to be more realistic and efficient than NE models in unprotected left-turn scenarios. Our findings contribute valuable insights into the vehicle decision-making behaviors with bounded rationality, thereby informing the development of more robust and realistic autonomous driving systems.