A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System
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Abstract (arXiv)
Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.
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References · 12
- 2021Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trendscited by 26
- 2018Verification for Machine Learning, Autonomy, and Neural Networks Surveycited by 24
- 2020Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detectioncited by 17
- 2019YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detectioncited by 16
- 2019Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Drivingcited by 12
- 2018Automated Verification of Neural Networks: Advances, Challenges and Perspectivescited by 7
- 2021Autoencoder-based Semantic Novelty Detection: Towards Dependable AI-based Systemscited by 4
- 2020Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimationcited by 3
- 2023Towards exploring adversarial learning for anomaly detection in complex driving scenescited by 2
- 2023Virtually increasing the measurement frequency of LIDAR sensor utilizing a single RGB cameracited by 1
- 2023Runtime Safety Assurance of Autonomous Vehicles used for Last-mile Delivery in Urban Environmentscited by 1
- 2023Connected Dependability Cage Approach for Safe Automated Drivingcited by 1
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