6 research outputs found
Online Out-of-Domain Detection for Automated Driving
Ensuring safety in automated driving is a major challenge for the automotive
industry. Special attention is paid to artificial intelligence, in particular
to Deep Neural Networks (DNNs), which is considered a key technology in the
realization of highly automated driving. DNNs learn from training data, which
means that they only achieve good accuracy within the underlying data
distribution of the training data. When leaving the training domain, a
distributional shift is caused, which can lead to a drastic reduction of
accuracy. In this work, we present a proof of concept for a safety mechanism
that can detect the leaving of the domain online, i.e. at runtime. In our
experiments with the Synthia data set we can show that a 100 % correct
detection of whether the input data is inside or outside the domain is
achieved. The ability to detect when the vehicle leaves the domain can be an
important requirement for certification.Comment: Machine Learning in Certified Systems (MLCS) Workshop, 14.-15.01.202
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Accurate detection of 3D objects is a fundamental problem in computer vision
and has an enormous impact on autonomous cars, augmented/virtual reality and
many applications in robotics. In this work we present a novel fusion of neural
network based state-of-the-art 3D detector and visual semantic segmentation in
the context of autonomous driving. Additionally, we introduce
Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable
evaluation metric for comparison of object detections, which speeds up our
inference time up to 20\% and halves training time. On top, we apply
state-of-the-art online multi target feature tracking on the object
measurements to further increase accuracy and robustness utilizing temporal
information. Our experiments on KITTI show that we achieve same results as
state-of-the-art in all related categories, while maintaining the performance
and accuracy trade-off and still run in real-time. Furthermore, our model is
the first one that fuses visual semantic with 3D object detection
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation
Averaging predictions of a deep ensemble of networks is apopular and
effective method to improve predictive performance andcalibration in various
benchmarks and Kaggle competitions. However, theruntime and training cost of
deep ensembles grow linearly with the size ofthe ensemble, making them
unsuitable for many applications. Averagingensemble weights instead of
predictions circumvents this disadvantageduring inference and is typically
applied to intermediate checkpoints ofa model to reduce training cost. Albeit
effective, only few works haveimproved the understanding and the performance of
weight averaging.Here, we revisit this approach and show that a simple weight
fusion (WF)strategy can lead to a significantly improved predictive performance
andcalibration. We describe what prerequisites the weights must meet interms of
weight space, functional space and loss. Furthermore, we presenta new test
method (called oracle test) to measure the functional spacebetween weights. We
demonstrate the versatility of our WF strategy acrossstate of the art
segmentation CNNs and Transformers as well as real worlddatasets such as
BDD100K and Cityscapes. We compare WF with similarapproaches and show our
superiority for in- and out-of-distribution datain terms of predictive
performance and calibration
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly