  Planning is complicated by the combination of perception and map information,
particularly when driving in heavy traffic. Developing an extendable and
efficient representation that visualizes sensor noise and provides constraints
to real-time planning tasks is desirable. We aim to develop an extendable map
representation offering prior to cost in planning tasks to simplify the
planning process of dealing with complex driving scenarios and visualize sensor
noise. In this paper, we illustrate a unified context representation empowered
by a modern deep learning motion prediction model, representing statistical
cognition of motion prediction for human beings. A sampling-based planner is
adopted to train and compare the difference in risk map generation methods. The
training tools and model structures are investigated illustrating their
efficiency in this task.
