# Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning

arXiv: https://arxiv.org/abs/2405.07543 · HTML: https://mlsanity.com/paper/2405.07543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 13 May 2024.

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

- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023)
- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving](https://mlsanity.com/paper/1905.00229/) (2019)
- [Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving](https://mlsanity.com/paper/2005.11470/) (2020)
- [A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model](https://mlsanity.com/paper/1702.01228/) (2017)
- [Safe, efficient and socially-compatible decision of automated vehicles: a case study of unsignalized intersection driving](https://mlsanity.com/paper/2111.02977/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios](https://mlsanity.com/paper/2606.31483/) (2026)

## Abstract (arXiv)

Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from driver's takeover interventions. By leveraging online takeover data, the driving zone is generated to ensure perceived safety using Gaussian discriminant analysis. Real-time corrections to trajectory planning rewards are enacted through apprenticeship learning. Guided by the objective of optimizing rewards within the constraints of the driving zone, this approach employs model predictive control for trajectory planning. This lesson learning framework is highlighted for its faster evolution capability, adeptness at experience accumulating, assurance of perceived safety, and computational efficiency. Simulation results demonstrate that the proposed system consistently achieves a successful customization without further takeover interventions. Accumulated experience yields a 24% enhancement in evolution efficiency. The average number of learning iterations is only 13.8. The average computation time is 0.08 seconds.