# Learn2Drive: A neural network-based framework for socially compliant automated vehicle control

arXiv: https://arxiv.org/abs/2510.21736 · HTML: https://mlsanity.com/paper/2510.21736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [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)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [A Physics-Informed Deep Learning Paradigm for Car-Following Models](https://mlsanity.com/paper/2012.13376/) (2020)
- [openACC. An open database of car-following experiments to study the properties of commercial ACC systems](https://mlsanity.com/paper/2004.06342/) (2020)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Calibrating microscopic car following models for adaptive cruise control vehicles: a multi-objective approach](https://mlsanity.com/paper/2010.03109/) (2020)
- [Socially Compatible Control Design of Automated Vehicle in Mixed Traffic](https://mlsanity.com/paper/2112.02041/) (2021)
- [Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach](https://mlsanity.com/paper/2310.17091/) (2023)
- [RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality](https://mlsanity.com/paper/2312.07003/) (2023)
- [Interaction-Aware Model Predictive Decision-Making for Socially-Compliant Autonomous Driving in Mixed Urban Traffic Scenarios](https://mlsanity.com/paper/2503.01852/) (2025)

## Abstract (arXiv)

This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics-informed constraints. As automated vehicles (AVs) adopt advanced features like ACC, transportation systems are becoming increasingly intelligent and efficient. However, existing AV control strategies primarily focus on optimizing the performance of individual vehicles or platoons, often neglecting their interactions with human-driven vehicles (HVs) and the broader impact on traffic flow. This oversight can exacerbate congestion and reduce overall system efficiency. To address this critical research gap, we propose a neural network-based, socially compliant AV control framework that incorporates social value orientation (SVO). This framework enables AVs to account for their influence on HVs and traffic dynamics. By leveraging AVs as mobile traffic regulators, the proposed approach promotes adaptive driving behaviors that reduce congestion, improve traffic efficiency, and lower energy consumption. Numerical results demonstrate the effectiveness of the proposed method in adapting to varying traffic conditions, thereby enhancing system-wide efficiency. Specifically, when the AV's control mode shifts from prioritizing its own energy conservation to optimizing collective traffic flow efficiency, the controlled AV proactively adapts its acceleration profile. This prosocial behavior yields at least a 38.39% improvement in the average speed of downstream vehicles and effectively dampens traffic oscillations, demonstrating significant enhancements in system-wide dynamics. The implementation code is available https://github.com/lilab2024/Learn2Drive-SVOv1.git.