The realm of autonomous driving research has witnessed remarkable progress, with simulation technologies [1][2][3][4] reaching unprecedented levels of realism and the burgeoning availability of real-world driving datasets [5][6][7][8]. Despite these advancements, data-driven planning continues to confront a formidable obstacle: the infinite state space and extensive data distribution characteristic of real-world driving. Imitation learning approaches encounter hurdles [9][10] when presented with scenarios that deviate from the training distribution, exemplified by rare events like emergency braking for unforeseen obstacles. Similarly, these methods grapple with long-tail distribution phenomena, such as navigating through unexpected weather conditions or handling the erratic movements of a jaywalking pedestrian. On the other hand, reinforcement learning (RL) strategies aim to cultivate policies through reward-based learning. RL has difficulty bridging the sim-real gap and sampling efficiency [11].

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.