  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.
