  Personalized motion planning holds significant importance within urban
automated driving, catering to the unique requirements of individual users.
Nevertheless, prior endeavors have frequently encountered difficulties in
simultaneously addressing two crucial aspects: personalized planning within
intricate urban settings and enhancing planning performance through data
utilization. The challenge arises from the expensive and limited nature of user
data, coupled with the scene state space tending towards infinity. These
factors contribute to overfitting and poor generalization problems during model
training. Henceforth, we propose an instance-based transfer imitation learning
approach. This method facilitates knowledge transfer from extensive expert
domain data to the user domain, presenting a fundamental resolution to these
issues. We initially train a pre-trained model using large-scale expert data.
Subsequently, during the fine-tuning phase, we feed the batch data, which
comprises expert and user data. Employing the inverse reinforcement learning
technique, we extract the style feature distribution from user demonstrations,
constructing the regularization term for the approximation of user style. In
our experiments, we conducted extensive evaluations of the proposed method.
Compared to the baseline methods, our approach mitigates the overfitting issue
caused by sparse user data. Furthermore, we discovered that integrating the
driving model with a differentiable nonlinear optimizer as a safety protection
layer for end-to-end personalized fine-tuning results in superior planning
performance.
