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Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing
Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles.
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.
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems.
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.
