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Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
The processing requirement of autonomous vehicles (AVs) for high-accuracy perception in complex scenarios can exceed the resources offered by the in-vehicle computer, degrading safety and comfort.
Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
The ability of Convolutional Neural Networks (CNNs) to accurately process real-time telemetry has boosted their use in safety-critical and high-performance computing systems.
HarDNN: Feature Map Vulnerability Evaluation in CNNs
As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical applications, it is important that they behave reliably in the face of hardware errors.
ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection
The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents.
