  Deep Reinforcement Learning (DRL) enhances the efficiency of Autonomous
Vehicles (AV), but also makes them susceptible to backdoor attacks that can
result in traffic congestion or collisions. Backdoor functionality is typically
incorporated by contaminating training datasets with covert malicious data to
maintain high precision on genuine inputs while inducing the desired
(malicious) outputs for specific inputs chosen by adversaries. Current defenses
against backdoors mainly focus on image classification using image-based
features, which cannot be readily transferred to the regression task of
DRL-based AV controllers since the inputs are continuous sensor data, i.e., the
combinations of velocity and distance of AV and its surrounding vehicles. Our
proposed method adds well-designed noise to the input to neutralize backdoors.
The approach involves learning an optimal smoothing (noise) distribution to
preserve the normal functionality of genuine inputs while neutralizing
backdoors. By doing so, the resulting model is expected to be more resilient
against backdoor attacks while maintaining high accuracy on genuine inputs. The
effectiveness of the proposed method is verified on a simulated traffic system
based on a microscopic traffic simulator, where experimental results showcase
that the smoothed traffic controller can neutralize all trigger samples and
maintain the performance of relieving traffic congestion
