  Advances in deep learning have revolutionized cyber-physical applications,
including the development of Autonomous Vehicles. However, real-world
collisions involving autonomous control of vehicles have raised significant
safety concerns regarding the use of Deep Neural Networks (DNN) in
safety-critical tasks, particularly Perception. The inherent unverifiability of
DNNs poses a key challenge in ensuring their safe and reliable operation.
  In this work, we propose Perception Simplex (PS), a fault-tolerant
application architecture designed for obstacle detection and collision
avoidance. We analyze an existing LiDAR-based classical obstacle detection
algorithm to establish strict bounds on its capabilities and limitations. Such
analysis and verification have not been possible for deep learning-based
perception systems yet. By employing verifiable obstacle detection algorithms,
PS identifies obstacle existence detection faults in the output of unverifiable
DNN-based object detectors. When faults with potential collision risks are
detected, appropriate corrective actions are initiated. Through extensive
analysis and software-in-the-loop simulations, we demonstrate that PS provides
predictable and deterministic fault tolerance against obstacle existence
detection faults, establishing a robust safety guarantee.
