Before revision:  The quantitative results also reveal that the  personalized modeling method significantly outperforms the general modeling approach, reducing the  errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .
After revision:  The quantitative results also reveal that the learned reward function is robust, which is manifested by only a marginal decline in proximity to the human driving trajectories when applying the reward function in the testing conditions. For the testing performance, the personalized modeling method significantly outperforms the general modeling approach, reducing the errors in human likeness by 24 \%, and the proposed reward-function-based method delivers better results compared to other baseline methods .