Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning

TL;DR

This paper introduces a lesson learning framework for personalized automated lane change that learns from driver interventions, enabling faster online adaptation with high safety and computational efficiency, demonstrated through simulation results.

Contribution

The paper presents a novel lesson learning approach using takeover data for online personalization of lane change systems, improving evolution speed and safety assurance.

Findings

24% increase in evolution efficiency

Average of 13.8 learning iterations

Computation time of 0.08 seconds per iteration

Abstract

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.