LiRTest: augmenting LiDAR point clouds for automated testing of autonomous driving systems
An Guo, Feng Yang, Zhenyu Chen
Abstract
With the tremendous advancement of Deep Neural Networks (DNNs), autonomous driving systems (ADS) have achieved significant development and been applied to assist in many safety-critical tasks. However, despite their spectacular progress, several real-world accidents involving autonomous cars even resulted in a fatality. While the high complexity and low interpretability of DNN models, which empowers the perception capability of ADS, make conventional testing techniques inapplicable for the perception of ADS, the existing testing techniques depending on manual data collection and labeling become time-consuming and prohibitively expensive.
Topics & Concepts
InterpretabilityComputer sciencePoint cloudDeep neural networksLidarPerceptionArtificial intelligencePoint (geometry)Deep learningHuman–computer interactionMachine learningRemote sensingGeologyNeuroscienceGeometryBiologyMathematicsAutonomous Vehicle Technology and SafetyAdvanced Neural Network ApplicationsAdversarial Robustness in Machine Learning