# Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network

arXiv: https://arxiv.org/abs/2310.20148 · HTML: https://mlsanity.com/paper/2310.20148/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 54 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Model-free Deep Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/1904.09503/) (2019)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization](https://mlsanity.com/paper/2110.13827/) (2021)
- [Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy](https://mlsanity.com/paper/1610.01238/) (2016)
- [Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems](https://mlsanity.com/paper/1608.08589/) (2016)
- [Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation](https://mlsanity.com/paper/2207.05853/) (2022)
- [B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving](https://mlsanity.com/paper/2011.03748/) (2020)
- [Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/1909.13582/) (2019)
- [Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios](https://mlsanity.com/paper/2112.07624/) (2021)
- [Potential Game-Based Decision-Making for Autonomous Driving](https://mlsanity.com/paper/2201.06157/) (2022)
- [LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1703.08987/) (2017)
- [Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data](https://mlsanity.com/paper/2103.04289/) (2021)
- [Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars](https://mlsanity.com/paper/2102.00675/) (2021)
- [Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors](https://mlsanity.com/paper/2309.14497/) (2023)

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

- [Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control](https://mlsanity.com/paper/2505.11546/) (2025)

## Abstract (arXiv)

Autonomous vehicles need to accomplish their tasks while interacting with human drivers in traffic. It is thus crucial to equip autonomous vehicles with artificial reasoning to better comprehend the intentions of the surrounding traffic, thereby facilitating the accomplishments of the tasks. In this work, we propose a behavioral model that encodes drivers' interacting intentions into latent social-psychological parameters. Leveraging a Bayesian filter, we develop a receding-horizon optimization-based controller for autonomous vehicle decision-making which accounts for the uncertainties in the interacting drivers' intentions. For online deployment, we design a neural network architecture based on the attention mechanism which imitates the behavioral model with online estimated parameter priors. We also propose a decision tree search algorithm to solve the decision-making problem online. The proposed behavioral model is then evaluated in terms of its capabilities for real-world trajectory prediction. We further conduct extensive evaluations of the proposed decision-making module, in forced highway merging scenarios, using both simulated environments and real-world traffic datasets. The results demonstrate that our algorithms can complete the forced merging tasks in various traffic conditions while ensuring driving safety.