Philipp Reis

Christian Steinhauser, Philipp Reis, Hubert Padusinski et al.

Precise perception of the environment is essential in highly automated driving systems, which rely on machine learning tasks such as object detection and segmentation. Compression of sensor data is commonly used for data handling, while virtualization is used for hardware-in-the-loop validation. Both methods can alter sensor data and degrade model performance. This necessitates a systematic approach to quantifying image validity. This paper presents a four-step framework to evaluate the impact of image modifications on machine learning tasks. First, a dataset with modified images is prepared to ensure one-to-one matching image pairs, enabling measurement of deviations resulting from compression and virtualization. Second, image deviations are quantified by comparing the effects of compression and virtualization against original camera-based sensor data. Third, the performance of state-of-the-art object detection models is analyzed to determine how altered input data affects perception tasks, including bounding box accuracy and reliability. Finally, a correlation analysis is performed to identify relationships between image quality and model performance. As a result, the LPIPS metric achieves the highest correlation between image deviation and machine learning performance across all evaluated machine learning tasks.

Philipp Reis, Joshua Ransiek, David Petri et al.

High-quality datasets are essential for training robust perception systems in autonomous driving. However, real-world data collection is often biased toward common scenes and objects, leaving novel cases underrepresented. This imbalance hinders model generalization and compromises safety. The core issue is the curse of rarity. Over time, novel events occur infrequently, and standard logging methods fail to capture them effectively. As a result, large volumes of redundant data are stored, while critical novel cases are diluted, leading to biased datasets. This work presents a real-time data selection method focused on object-level novelty detection to build more balanced and diverse datasets. The method assigns a data-driven novelty score to image frames using a novel dynamic Mean Shift algorithm. It models normal content based on mean and covariance statistics to identify frames with novel objects, discarding those with redundant elements. The main findings show that reducing the training dataset size with this method can improve model performance, whereas higher redundancy tends to degrade it. Moreover, as data redundancy increases, more aggressive filtering becomes both possible and beneficial. While random sampling can offer some gains, it often leads to overfitting and unpredictability in outcomes. The proposed method supports real-time deployment with 32 frames per second and is constant over time. By continuously updating the definition of normal content, it enables efficient detection of novelties in a continuous data stream.