  Recent years have seen a growing research interest in applications of Deep
Neural Networks (DNN) on autonomous vehicle technology. The trend started with
perception and prediction a few years ago and it is gradually being applied to
motion planning tasks. Despite the performance of networks improve over time,
DNN planners inherit the natural drawbacks of Deep Learning. Learning-based
planners have limitations in achieving perfect accuracy on the training dataset
and network performance can be affected by out-of-distribution problem. In this
paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving
fusion framework which combines physics-informed control for safety assurance.
By incorporating Potential Field into Model Predictive Control, FusionAssurance
is capable of navigating through scenarios that are not included in the
training dataset and scenarios where neural network fail to generalize. The
effectiveness of the approach is demonstrated by extensive experiments under
various scenarios on the CARLA benchmark.
