  Due to the uncertainty of traffic participants' intentions, generating safe
but not overly cautious behavior in interactive driving scenarios remains a
formidable challenge for autonomous driving. In this paper, we address this
issue by combining a deep learning-based trajectory prediction model with risk
potential field-based motion planning. In order to comprehensively predict the
possible future trajectories of other vehicles, we propose a target-region
based trajectory prediction model(TRTP) which considers every region a vehicle
may arrive in the future. After that, we construct a risk potential field at
each future time step based on the prediction results of TRTP, and integrate
risk value to the objective function of Model Predictive Contouring
Control(MPCC). This enables the uncertainty of other vehicles to be taken into
account during the planning process. Balancing between risk and progress along
the reference path can achieve both driving safety and efficiency at the same
time. We also demonstrate the security and effectiveness performance of our
method in the CARLA simulator.
