Deep learning has transformed object detection in autonomous driving and robotics. Yet, it requires training with large datasets, driving up costs and resource demands. While data reduction techniques offer a solution, most re-search has focused on image classification, leaving object detection largely unexplored. This paper introduces, adapts, and evaluates different data reduction strategies for 2$D$object detection. Experiments with Faster R-CNN on nulmages and BDDI00K reveal that: (1) Reduction methods based on loss prove to be both simple and effective, achieving up to 40 % dataset reduction while preserving model performance, (2) We introduce a novel predictive measure for dataset quality, leveraging intrinsic dimensionality to evaluate dataset diversity. This metric achieves up to 80 % alignment (Spearman correlation) with the final performance on the full dataset, enabling efficient pre-evaluation of potential reductions and streamlining the reduction process, (3) We investigate the impact of label errors on data reduction, revealing their influence, especially at high dataset compression rates, and offering key insights for developing robust reduction strategies.

In safety-critical domains like autonomous driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As raw evaluation metrics are not an adequate safety indicator, recent works leverage domain knowledge to identify safety-relevant VRU, and to back-annotate the criticality of the interaction to the object detector. However, those approaches do not consider the safety factor in the deep neural network (DNN) training process. Thus, state-of-the-art DNN penalize all misdetections equally irrespective of their importance for the safe driving task. Hence, to mitigate the occurrence of safety-critical failure cases like false negatives, a safety-aware training strategy is needed to enhance the detection performance for critical pedestrians. In this paper, we propose a novel, safety-adapted loss variation that leverages the estimated per-pedestrian criticality during training. Therefore, we exploit the reachable set-based time-to-collision (TTCRSB) metric from the motion domain along with distance information to account for the worst-case threat. Our evaluation results using RetinaNet and FCOS on the nuScenes dataset demonstrate that training the models with our safety-adapted loss function mitigates the misdetection of safety-critical pedestrians with robust performance for the general case, i.e., safety-irrelevant pedestrians.

The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reduce development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances.Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Fréchet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.

Systems and functions that rely on machine learning (ML) are the basis of highly automated driving. An essential task of such ML models is to reliably detect and interpret unusual, new, and potentially dangerous situations. The detection of those situations, which we refer to as corner cases, is highly relevant for successfully developing, applying, and validating automotive perception functions in future vehicles where multiple sensor modalities will be used. A complication for the development of corner case detectors is the lack of consistent definitions, terms, and corner case descriptions, especially when taking into account various automotive sensors. In this work, we provide an application-driven view of corner cases in highly automated driving. To achieve this goal, we first consider existing definitions of the general outlier, novelty, anomaly, and out-of-distribution detection to show relations and differences to corner cases. Moreover, we extend an existing camera-focused systematization of corner cases by adding RADAR (radio detection and ranging) and LiDAR (light detection and ranging) sensors. For this, we describe an exemplary toolchain for data acquisition and processing, highlighting the interfaces of corner case detection. We also define a novel level of corner cases, the method layer corner cases, which appear due to uncertainty inherent in the methodology.

In near future, vulnerable road users (VRUs) such as cyclists and pedestrians will be equipped with smart devices and wearables which are capable to communicate with intelligent vehicles and other traffic participants. Road users are then able to cooperate on different levels, such as in cooperative intention detection for advanced VRU protection. Smart devices can be used to detect intentions, e.g., an occluded cyclist intending to cross the road, to warn vehicles of VRUs, and prevent potential collisions. This article presents a human activity recognition approach to detect the starting movement of cyclists wearing smart devices. We propose a novel two-stage feature selection procedure using a score specialized for robust starting detection reducing the false positive detections and leading to understandable and interpretable features. The detection is modelled as a classification problem and realized by means of a machine learning classifier. We introduce an auxiliary class, that models starting movements and allows to integrate early movement indicators, i.e., body part movements indicating future behaviour. In this way we improve the robustness and reduce the detection time of the classifier. Our empirical studies with real-world data originating from experiments which involve 49 test subjects and consists of 84 starting motions show that we are able to detect the starting movements early. Our approach reaches an F1-score of 67 % within 0.33 s after the first movement of the bicycle wheel. Investigations concerning the device wearing location show that for devices worn in the trouser pocket the detector has less false detections and detects starting movements faster on average. % compared to reference detector involving all wearing locations. We found that we can further improve the results when we train distinct classifiers for different wearing locations. In this case we reach an F1-score of 94 % with a mean detection time of 0.34 s for the device worn in the trouser pocket.

As robots are employed for automating processes in industry, there is a strong demand on robots being able to solve new tasks without costly adaptions. In this article, we present a probabilistic active learning approach for adapting robots to tasks involving object sorting by means of classification according to a human understanding of the problem and its solution. In the beginning, the robot extracts appropriate features of images of the available objects. These features are used by the robot to actively learn to solve the sorting task by asking a human teacher. A novel visualization tool allows to supervise the learning process and to determine when training is complete. Then, the robot is able to successfully sort the objects autonomously. We show our method's superiority compared to other active learning strategies and present results of its application on a real robot to prove the above concept.