Search Results for author:
Found 10 papers, 7 papers with code
Language-Guided Traffic Simulation via Scene-Level Diffusion
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development.
Guided Conditional Diffusion for Controllable Traffic Simulation
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles.
Repairing Group-Level Errors for DNNs Using Weighted Regularization
In particular, it can repair confusion error and bias error of DNN models for both single-label and multi-label image classifications.
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation
To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works.
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems
We define the failures (e. g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors.
Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles
Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability -- which can most practically and convincingly be achieved by testing.
Understanding Local Robustness of Deep Neural Networks under Natural Variations
To this end, we study the local per-input robustness properties of the DNNs and leverage those properties to build a white-box (DeepRobust-W) and a black-box (DeepRobust-B) tool to automatically identify the non-robust points.
Metric Learning for Adversarial Robustness
Deep networks are well-known to be fragile to adversarial attacks.
Testing DNN Image Classifiers for Confusion & Bias Errors
We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others.
Noise-tolerant fair classification
We finally show that our procedure is empirically effective on two case-studies involving sensitive feature censoring.
