# Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation

arXiv: https://arxiv.org/abs/2207.05853 · HTML: https://mlsanity.com/paper/2207.05853/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2022.

## Built on by (most cited first, 20 of 20)

- [Dream to Drive with Predictive Individual World Model](https://mlsanity.com/paper/2501.16733/) (2025)
- [Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings](https://mlsanity.com/paper/2304.08260/) (2023)
- [Waymo-3DSkelMo: A Multi-Agent 3D Skeletal Motion Dataset for Pedestrian Interaction Modeling in Autonomous Driving](https://mlsanity.com/paper/2508.09404/) (2025)
- [Modeling 3D Pedestrian-Vehicle Interactions for Vehicle-Conditioned Pose Forecasting](https://mlsanity.com/paper/2602.08962/) (2026)
- [IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning](https://mlsanity.com/paper/2311.02850/) (2023)
- [How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games](https://mlsanity.com/paper/2210.13064/) (2022)
- [A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System](https://mlsanity.com/paper/2310.03303/) (2023)
- [Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network](https://mlsanity.com/paper/2310.20148/) (2023)
- [An Evolving Scenario Generation Method based on Dual-modal Driver Model Trained by Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2508.02027/) (2025)
- [Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow](https://mlsanity.com/paper/2304.12821/) (2023)
- [Human-Like Implicit Intention Expression for Autonomous Driving Motion Planning: A Method Based on Learning Human Intention Priors](https://mlsanity.com/paper/2307.15950/) (2023)
- [RACE-SM: Reinforcement Learning Based Autonomous Control for Social On-Ramp Merging](https://mlsanity.com/paper/2403.03359/) (2024)
- [Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework](https://mlsanity.com/paper/2501.06089/) (2025)
- [Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions](https://mlsanity.com/paper/2504.20004/) (2025)
- [Free Energy-Inspired Cognitive Risk Integration for AV Navigation in Pedestrian-Rich Environments](https://mlsanity.com/paper/2507.20850/) (2025)
- [SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model](https://mlsanity.com/paper/2509.17850/) (2025)
- [How to Brake? Ethical Emergency Braking with Deep Reinforcement Learning](https://mlsanity.com/paper/2512.10698/) (2025)
- [Agentic MPC for Semantic Control System Resynthesis](https://mlsanity.com/paper/2606.12774/) (2026)
- [Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos](https://mlsanity.com/paper/2606.18824/) (2026)
- [Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2607.12293/) (2026)

## Abstract (arXiv)

Motion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutual interactions between AVs and pedestrians. On the other hand, recent advances in Deep Reinforcement Learning allow for the automatic learning of policies without manual designs. To tackle the problem of decision-making in the presence of pedestrians, the authors introduce a framework based on Social Value Orientation and Deep Reinforcement Learning (DRL) that is capable of generating decision-making policies with different driving styles. The policy is trained using state-of-the-art DRL algorithms in a simulated environment. A novel computationally-efficient pedestrian model that is suitable for DRL training is also introduced. We perform experiments to validate our framework and we conduct a comparative analysis of the policies obtained with two different model-free Deep Reinforcement Learning Algorithms. Simulations results show how the developed model exhibits natural driving behaviours, such as short-stopping, to facilitate the pedestrian's crossing.