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.

个性化驾驶辅助系统的在线进化能力是确保用户偏好匹配必不可少的。本文提出了一种以驾驶员接管干预为基础的课程学习方法，通过利用在线的接管数据生成驾驶区域，采用高斯判别分析确保被感知的安全性，通过学徒学习实时对轨迹规划回报进行纠正，利用模型预测控制以在驾驶区域约束下优化回报目标，该方法具有更快的进化能力、经验累积的熟练度、被感知的安全性保证和较低的计算复杂性，仿真结果表明所提系统始终能成功自定义轨迹规划而无需进一步接管干预，累积经验可以使进化效率提高 24％，平均学习迭代次数仅为 13.8 次，平均计算时间为 0.08 秒。