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Sampling-based motion planning (SBMP) is a major trajectory planning approach in autonomous driving given its high efficiency in practice. As the core of SBMP schemes, sampling strategy holds the key to whether a smooth and collision-free trajectory can be found in real-time. Although some bias sampling strategies have been explored in the literature to accelerate SBMP, the trajectory generated under existing bias sampling strategies may lead to sharp lane changing. To address this issue, we propose a new learning framework for SBMP. Specifically, we develop a novel automatic labeling scheme and a 2-Stage prediction model to improve the accuracy in predicting the intention of surrounding vehicles. We then develop an imitation learning scheme to generate sample points based on the experience of human drivers. Using the prediction results, we design a new bias sampling strategy to accelerate the SBMP algorithm by strategically selecting necessary sample points that can generate a smooth and collision-free trajectory and avoid sharp lane changing. Data-driven experiments show that the proposed sampling strategy outperforms existing sampling strategies, in terms of the computing time, traveling time, and smoothness of the trajectory. The results also show that our scheme is even better than human drivers. 

Simple question: How sensitive is your simulation output to the variance of your simulation input models? Unfortunately, the answer is not simple because the variance of many standard parametric input distributions can achieve the same change in multiple ways as a function of the parameters. In this paper we propose a family of output-mean-with-respect-to-input-variance sensitivity measures and identify two particularly useful members of it. A further benefit of this family is that there is a straightforward estimator of any member with no additional simulation effort beyond the nominal experiment. A numerical example is provided to illustrate the method and interpretation of results.