# MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving

arXiv: https://arxiv.org/abs/2301.08595 · HTML: https://mlsanity.com/paper/2301.08595/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks](https://mlsanity.com/paper/2110.03655/) (2021)
- [A First Look at Ad-block Detection: A New Arms Race on the Web](https://mlsanity.com/paper/1605.05841/) (2016)
- [Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studies](https://mlsanity.com/paper/2001.03231/) (2020)
- [Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration](https://mlsanity.com/paper/1903.06047/) (2019)
- [Improving Robot-Centric Learning from Demonstration via Personalized Embeddings](https://mlsanity.com/paper/2110.03134/) (2021)

## Built on by (most cited first, 22 of 22)

- [Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future](https://mlsanity.com/paper/2512.16760/) (2025)
- [StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving](https://mlsanity.com/paper/2506.23982/) (2025)
- [On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation](https://mlsanity.com/paper/2411.11913/) (2024)
- [StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving](https://mlsanity.com/paper/2603.09482/) (2026)
- [Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2410.17373/) (2024)
- [PADriver: Towards Personalized Autonomous Driving](https://mlsanity.com/paper/2505.05240/) (2025)
- [Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving](https://mlsanity.com/paper/2603.25740/) (2026)
- [PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning](https://mlsanity.com/paper/2407.18569/) (2024)
- [CIVIL: Causal and Intuitive Visual Imitation Learning](https://mlsanity.com/paper/2504.17959/) (2025)
- [Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving](https://mlsanity.com/paper/2603.25672/) (2026)
- [Driving Towards Inclusion: A Systematic Review of AI-powered Accessibility Enhancements for People with Disability in Autonomous Vehicles](https://mlsanity.com/paper/2401.14571/) (2024)
- [Situation Awareness for Driver-Centric Driving Style Adaptation](https://mlsanity.com/paper/2403.19595/) (2024)
- [Quantifying and Modeling Driving Styles in Trajectory Forecasting](https://mlsanity.com/paper/2503.04994/) (2025)
- [Your Ride, Your Rules: Psychology and Cognition Enabled Automated Driving Systems](https://mlsanity.com/paper/2506.11842/) (2025)
- [BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2509.14151/) (2025)
- [Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.18757/) (2026)
- [Personalized Autonomous Driving via Optimal Control with Clearance Constraints from Questionnaires](https://mlsanity.com/paper/2602.08326/) (2026)
- [Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning](https://mlsanity.com/paper/2603.17148/) (2026)
- [PersonaDrive: Human-Style Retrieval-Augmented VLA Agents for Closed-Loop Driving Simulation](https://mlsanity.com/paper/2606.12616/) (2026)
- [Do as the Romans Do: Learning Universal Behaviors from Heterogeneous Agents](https://mlsanity.com/paper/2606.18537/) (2026)
- [A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios](https://mlsanity.com/paper/2606.31483/) (2026)
- [A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform](https://mlsanity.com/paper/2610.00926/) (2026)

## Abstract (arXiv)

Personalization of autonomous vehicles (AV) may significantly increase trust, use, and acceptance. In particular, we hypothesize that the similarity of an AV's driving style compared to the end-user's driving style will have a major impact on end-user's willingness to use the AV. To investigate the impact of driving style on user acceptance, we 1) develop a data-driven approach to personalize driving style and 2) demonstrate that personalization significantly impacts attitudes towards AVs. Our approach learns a high-level model that tunes low-level controllers to ensure safe and personalized control of the AV. The key to our approach is learning an informative, personalized embedding that represents a user's driving style. Our framework is capable of calibrating the level of aggression so as to optimize driving style based upon driver preference. Across two human subject studies (n = 54), we first demonstrate our approach mimics the driving styles of end-users and can tune attributes of style (e.g., aggressiveness). Second, we investigate the factors (e.g., trust, personality etc.) that impact homophily, i.e. an individual's preference for a driving style similar to their own. We find that our approach generates driving styles consistent with end-user styles (p<.001) and participants rate our approach as more similar to their level of aggressiveness (p=.002). We find that personality (p<.001), perceived similarity (p<.001), and high-velocity driving style (p=.0031) significantly modulate the effect of homophily.