# Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles

arXiv: https://arxiv.org/abs/2503.15407 · HTML: https://mlsanity.com/paper/2503.15407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [Safe Reinforcement Learning Using Robust MPC](https://mlsanity.com/paper/1906.04005/) (2019)
- [Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes](https://mlsanity.com/paper/2203.11382/) (2022)
- [Stability-informed Bayesian Optimization for MPC Cost Function Learning](https://mlsanity.com/paper/2404.12187/) (2024)
- [Safety-Aware Preference-Based Learning for Safety-Critical Control](https://mlsanity.com/paper/2112.08516/) (2021)
- [Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control](https://mlsanity.com/paper/2409.10171/) (2024)
- [Preference-based MPC calibration](https://mlsanity.com/paper/2003.11294/) (2020)

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

- [Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models](https://mlsanity.com/paper/2603.24138/) (2026)

## Abstract (arXiv)

Trajectory planning for automated vehicles commonly employs optimization over a moving horizon - Model Predictive Control - where the cost function critically influences the resulting driving style. However, finding a suitable cost function that results in a driving style preferred by passengers remains an ongoing challenge. We employ preferential Bayesian optimization to learn the cost function by iteratively querying a passenger's preference. Due to increasing dimensionality of the parameter space, preference learning approaches might struggle to find a suitable optimum with a limited number of experiments and expose the passenger to discomfort when exploring the parameter space. We address these challenges by incorporating prior knowledge into the preferential Bayesian optimization framework. Our method constructs a virtual decision maker from real-world human driving data to guide parameter sampling. In a simulation experiment, we achieve faster convergence of the prior-knowledge-informed learning procedure compared to existing preferential Bayesian optimization approaches and reduce the number of inadequate driving styles sampled.