# Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

arXiv: https://arxiv.org/abs/2010.14712 · HTML: https://mlsanity.com/paper/2010.14712/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [ReasonNet: End-to-End Driving with Temporal and Global Reasoning](https://mlsanity.com/paper/2305.10507/) (2023)
- [Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors](https://mlsanity.com/paper/2305.04412/) (2023)
- [SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction](https://mlsanity.com/paper/2403.11492/) (2024)
- [DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving](https://mlsanity.com/paper/2601.01528/) (2026)
- [Transferable and Adaptable Driving Behavior Prediction](https://mlsanity.com/paper/2202.05140/) (2022)
- [Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills](https://mlsanity.com/paper/2209.12072/) (2022)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [ComDrive: Comfort-Oriented End-to-End Autonomous Driving](https://mlsanity.com/paper/2410.05051/) (2024)
- [Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation](https://mlsanity.com/paper/2303.13830/) (2023)
- [SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction](https://mlsanity.com/paper/2410.08669/) (2024)
- [Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction](https://mlsanity.com/paper/2112.06129/) (2021)
- [ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting](https://mlsanity.com/paper/2508.07089/) (2025)
- [A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System](https://mlsanity.com/paper/2310.03303/) (2023)
- [MTR-VP: Towards End-to-End Trajectory Planning through Context-Driven Image Encoding and Multiple Trajectory Prediction](https://mlsanity.com/paper/2511.22181/) (2025)
- [Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework](https://mlsanity.com/paper/2501.06089/) (2025)
- [Cross-cultural Deployment of Autonomous Vehicles Using Data-light Inverse Reinforcement Learning](https://mlsanity.com/paper/2504.11506/) (2025)
- [An Evolutionary Game-Theoretic Merging Decision-Making Considering Social Acceptance for Autonomous Driving](https://mlsanity.com/paper/2508.07080/) (2025)

## Abstract (arXiv)

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavior prediction. In this paper, we propose an uncertain-aware integrated prediction and planning (UAPP) framework. It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence. Based on that, their influences on the behaviors of AVs in interactive driving scenarios are explored. Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior against ground truth. Results show that the online inference can significantly improve the human-likeness of the generated behaviors. Furthermore, we find that human drivers show great courtesy to others, even for those without right-of-way. We also find that such driving preferences vary significantly in different cultures.