Introspection of dnn-based perception functions in automated driving systems: State-of-the-art and open research challenges
Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
Toward ensuring safety for autonomous driving perception: standardization progress, research advances, and perspectives
Perception systems play a crucial role in autonomous driving by reading the sensory data
and providing meaningful interpretation of the operating environment for decision-making …
and providing meaningful interpretation of the operating environment for decision-making …
Open-set semi-supervised object detection
Abstract Recent developments for Semi-Supervised Object Detection (SSOD) have shown
the promise of leveraging unlabeled data to improve an object detector. However, thus far …
the promise of leveraging unlabeled data to improve an object detector. However, thus far …
Introspection of 2d object detection using processed neural activation patterns in automated driving systems
While deep neural network (DNN) models have become extremely popular for object
detection in automated driving systems (ADS), the dynamic and varied nature of the road …
detection in automated driving systems (ADS), the dynamic and varied nature of the road …
CLIFF: Continual Latent Diffusion for Open-Vocabulary Object Detection
Open-vocabulary object detection (OVD) utilizes image-level cues to expand the linguistic
space of region proposals, thereby facilitating the detection of diverse novel classes. Recent …
space of region proposals, thereby facilitating the detection of diverse novel classes. Recent …
Open-set recognition in the age of vision-language models
Are vision-language models (VLMs) for open-vocabulary perception inherently open-set
models because they are trained on internet-scale datasets? We answer this question with a …
models because they are trained on internet-scale datasets? We answer this question with a …
SAFE: Sensitivity-aware features for out-of-distribution object detection
We address the problem of out-of-distribution (OOD) detection for the task of object
detection. We show that residual convolutional layers with batch normalisation produce …
detection. We show that residual convolutional layers with batch normalisation produce …
Out-of-distribution detection for lidar-based 3d object detection
3D object detection is an essential part of automated driving, and deep neural networks
(DNNs) have achieved state-of-the-art performance for this task. However, deep models are …
(DNNs) have achieved state-of-the-art performance for this task. However, deep models are …
Hyperdimensional feature fusion for out-of-distribution detection
We introduce powerful ideas from Hyperdimensional Computing into the challenging field of
Out-of-Distribution (OOD) detection. In contrast to many existing works that perform OOD …
Out-of-Distribution (OOD) detection. In contrast to many existing works that perform OOD …
Bayesian deep learning for affordance segmentation in images
Affordances are a fundamental concept in robotics since they relate available actions for an
agent depending on its sensory-motor capabilities and the environment. We present a novel …
agent depending on its sensory-motor capabilities and the environment. We present a novel …