18 research outputs found
One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving
The core obstacle towards a large-scale deployment of autonomous vehicles
currently lies in the long tail of rare events. These are extremely challenging
since they do not occur often in the utilized training data for deep neural
networks. To tackle this problem, we propose the generation of additional
synthetic training data, covering a wide variety of corner case scenarios. As
ontologies can represent human expert knowledge while enabling computational
processing, we use them to describe scenarios. Our proposed master ontology is
capable to model scenarios from all common corner case categories found in the
literature. From this one master ontology, arbitrary scenario-describing
ontologies can be derived. In an automated fashion, these can be converted into
the OpenSCENARIO format and subsequently executed in simulation. This way, also
challenging test and evaluation scenarios can be generated.Comment: Daniel Bogdoll and Stefani Guneshka contributed equally. Accepted for
publication at ECCV 2022 SAIAD worksho
Anomaly Detection in Autonomous Driving: A Survey
Nowadays, there are outstanding strides towards a future with autonomous
vehicles on our roads. While the perception of autonomous vehicles performs
well under closed-set conditions, they still struggle to handle the unexpected.
This survey provides an extensive overview of anomaly detection techniques
based on camera, lidar, radar, multimodal and abstract object level data. We
provide a systematization including detection approach, corner case level,
ability for an online application, and further attributes. We outline the
state-of-the-art and point out current research gaps.Comment: Daniel Bogdoll and Maximilian Nitsche contributed equally. Accepted
for publication at CVPR 2022 WAD worksho
DLCSS: Dynamic Longest Common Subsequences
Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility models. However, to evaluate, whether two or more route requests have the potential for a shared ride, is a compute-intensive task, if done by rerouting. In this work, we propose the Dynamic Longest Common Subsequences algorithm for fast and cost-efficient comparison of two routes for their compatibility, dynamically only incorporating parts of the routes which are suited for a shared trip. Based on this, one can also estimate, how many autonomous vehicles might be necessary to fulfill the local mobility demands. This can help providers to estimate the necessary fleet sizes, policymakers to better understand mobility patterns and cities to scale necessary infrastructure
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Deep neural networks (DNN) which are employed in perception systems for
autonomous driving require a huge amount of data to train on, as they must
reliably achieve high performance in all kinds of situations. However, these
DNN are usually restricted to a closed set of semantic classes available in
their training data, and are therefore unreliable when confronted with
previously unseen instances. Thus, multiple perception datasets have been
created for the evaluation of anomaly detection methods, which can be
categorized into three groups: real anomalies in real-world, synthetic
anomalies augmented into real-world and completely synthetic scenes. This
survey provides a structured and, to the best of our knowledge, complete
overview and comparison of perception datasets for anomaly detection in
autonomous driving. Each chapter provides information about tasks and ground
truth, context information, and licenses. Additionally, we discuss current
weaknesses and gaps in existing datasets to underline the importance of
developing further data.Comment: Accepted for publication at IV 202
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world. While many approaches, such as uncertainty estimation or segmentation-based image resynthesis, are extremely promising, there is more to be explored. Especially inspired by works on anomaly detection based on image resynthesis, we propose a novel approach for anomaly detection through style transfer. We leverage generative models to map an image from its original style domain of road traffic to an arbitrary one and back to generate pixelwise anomaly scores. However, our experiments have proven our hypothesis wrong, and we were unable to produce significant results. Nevertheless, we want to share our findings, so that others can learn from our experiments
Multimodal Detection of Unknown Objects on Roads for Autonomous Driving
Tremendous progress in deep learning over the last years has led towards a
future with autonomous vehicles on our roads. Nevertheless, the performance of
their perception systems is strongly dependent on the quality of the utilized
training data. As these usually only cover a fraction of all object classes an
autonomous driving system will face, such systems struggle with handling the
unexpected. In order to safely operate on public roads, the identification of
objects from unknown classes remains a crucial task. In this paper, we propose
a novel pipeline to detect unknown objects. Instead of focusing on a single
sensor modality, we make use of lidar and camera data by combining state-of-the
art detection models in a sequential manner. We evaluate our approach on the
Waymo Open Perception Dataset and point out current research gaps in anomaly
detection.Comment: Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche, and Christin Scheib
contributed equally. Accepted for publication at SMC 202
Multimodal Detection of Unknown Objects on Roads for Autonomous Driving
Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection
Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality
One core challenge in the development of automated vehicles is their
capability to deal with a multitude of complex trafficscenarios with many, hard
to predict traffic participants. As part of the iterative development process,
it is necessary to detect criticalscenarios and generate knowledge from them to
improve the highly automated driving (HAD) function. In order to tackle this
challenge,numerous datasets have been released in the past years, which act as
the basis for the development and testing of such algorithms.Nevertheless, the
remaining challenges are to find relevant scenes, such as safety-critical
corner cases, in these datasets and tounderstand them completely.Therefore,
this paper presents a methodology to process and analyze naturalistic motion
datasets in two ways: On the one hand, ourapproach maps scenes of the datasets
to a generic semantic scene graph which allows for a high-level and objective
analysis. Here,arbitrary criticality measures, e.g. TTC, RSS or SFF, can be set
to automatically detect critical scenarios between traffic participants.On the
other hand, the scenarios are recreated in a realistic virtual reality (VR)
environment, which allows for a subjective close-upanalysis from multiple,
interactive perspectives.Comment: Accepted for publication at ICITE 202
Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
In recent years there have been remarkable advancements in autonomous
driving. While autonomous vehicles demonstrate high performance in closed-set
conditions, they encounter difficulties when confronted with unexpected
situations. At the same time, world models emerged in the field of model-based
reinforcement learning as a way to enable agents to predict the future
depending on potential actions. This led to outstanding results in sparse
reward and complex control tasks. This work provides an overview of how world
models can be leveraged to perform anomaly detection in the domain of
autonomous driving. We provide a characterization of world models and relate
individual components to previous works in anomaly detection to facilitate
further research in the field.Comment: Accepted for publication at SSCI 202
Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets
Autonomous Driving (AD), the area of robotics with the greatest potential
impact on society, has gained a lot of momentum in the last decade. As a result
of this, the number of datasets in AD has increased rapidly. Creators and users
of datasets can benefit from a better understanding of developments in the
field. While scientometric analysis has been conducted in other fields, it
rarely revolves around datasets. Thus, the impact, attention, and influence of
datasets on autonomous driving remains a rarely investigated field. In this
work, we provide a scientometric analysis for over 200 datasets in AD. We
perform a rigorous evaluation of relations between available metadata and
citation counts based on linear regression. Subsequently, we propose an
Influence Score to assess a dataset already early on without the need for a
track-record of citations, which is only available with a certain delay.Comment: Daniel Bogdoll and Jonas Hendl contributed equally. Accepted for
publication at ICCRE 202