scholarly journals Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO)

2021 ◽  
Vol 11 (17) ◽  
pp. 7782
Author(s):  
Itziar Urbieta ◽  
Marcos Nieto ◽  
Mikel García ◽  
Oihana Otaegui
Keyword(s):  
Large Scale ◽  
Autonomous Driving ◽  
Graph Databases ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Academic Communities ◽  
Semantic Labeling ◽  
Knowledge Organization System ◽  
Open Datasets

Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities.

A collection of easily deployable adversarial traffic sign stickers

2021 ◽  
Vol 69 (6) ◽  
pp. 511-523
Author(s):  
Henrietta Lengyel ◽  
Viktor Remeli ◽  
Zsolt Szalay
Keyword(s):  
Traffic Safety ◽  
Autonomous Driving ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Practical Possibility ◽  
Special Knowledge ◽  
Environment Perception ◽  
Assistance Systems ◽  
Traffic Sign Classification

Abstract The emergence of new autonomous driving systems and functions – in particular, systems that base their decisions on the output of machine learning subsystems responsible for environment perception – brings a significant change in the risks to the safety and security of transportation. These kinds of Advanced Driver Assistance Systems are vulnerable to new types of malicious attacks, and their properties are often not well understood. This paper demonstrates the theoretical and practical possibility of deliberate physical adversarial attacks against deep learning perception systems in general, with a focus on safety-critical driver assistance applications such as traffic sign classification in particular. Our newly developed traffic sign stickers are different from other similar methods insofar that they require no special knowledge or precision in their creation and deployment, thus they present a realistic and severe threat to traffic safety and security. In this paper we preemptively point out the dangers and easily exploitable weaknesses that current and future systems are bound to face.

Fuse -Replacement Noise-Filter Development for Automotive Audio–Video Navigation Systems Based on Loudness Isotropy Curves

2020 ◽  
Vol 25 (3) ◽  
pp. 83-92
Author(s):  
Bong-Seo Park ◽  
Hyun-cheol Park ◽  
Jung-jun Her
Keyword(s):  
Autonomous Driving ◽  
Navigation Systems ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Audio Quality ◽  
Noise Filter ◽  
Supply Noise ◽  
Low Pass ◽  
Assistance Systems ◽  
Audio Video

With the development of advanced driver assistance systems, the more reliable the autonomous driving technology is, the more the rest and entertainment times of the driver of the car increases. Hence, the importance of the entertainment function of automotive audio-video navigation (AVN) systems is increasing. Currently, the AVN system of automobiles has a monitoring function for fault diagnosis and a combination of functions. Applying these technologies is challenging for drivers who want to tune the audio quality to their musical taste. In this study, a method for upgrading the sound quality using a power supply noise filter without deforming the AVN system was developed. The low-pass attenuation that appeared as a side effect was solved by applying a filter using the loudness isotropic curve. In the installation method of the filter, the method of using a fuse holder minimized the inconvenience of AVN detachment and wiring. Based on the results obtained in this study, further research and improvement of the filter are required for audio tuning of various models.

scholarly journals Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers

2020 ◽  
Vol 10 (9) ◽  
pp. 3289
Author(s):  
Hanwool Woo ◽  
Mizuki Sugimoto ◽  
Hirokazu Madokoro ◽  
Kazuhito Sato ◽  
Yusuke Tamura ◽  
...  
Keyword(s):  
Warning System ◽  
Autonomous Driving ◽  
Field Method ◽  
Lane Change ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Lane Changing ◽  
Lane Changes ◽  
Dynamic Potential ◽  
Novel Method

In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method.

scholarly journals Semi-Automatic Cloud-Native Video Annotation for Autonomous Driving

2020 ◽  
Vol 10 (12) ◽  
pp. 4301
Author(s):  
Sergio Sánchez-Carballido ◽  
Orti Senderos ◽  
Marcos Nieto ◽  
Oihana Otaegui
Keyword(s):  
High Performance ◽  
Ground Truth ◽  
Autonomous Driving ◽  
Video Annotation ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Innovative Solution ◽  
Ground Truth Generation ◽  
Reliable Design ◽  
High Performance Computing Cluster

An innovative solution named Annotation as a Service (AaaS) has been specifically designed to integrate heterogeneous video annotation workflows into containers and take advantage of a cloud native highly scalable and reliable design based on Kubernetes workloads. Using the AaaS as a foundation, the execution of automatic video annotation workflows is addressed in the broader context of a semi-automatic video annotation business logic for ground truth generation for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). The document presents design decisions, innovative developments, and tests conducted to provide scalability to this cloud-native ecosystem for semi-automatic annotation. The solution has proven to be efficient and resilient on an AD/ADAS scale, specifically in an experiment with 25 TB of input data to annotate, 4000 concurrent annotation jobs, and 32 worker nodes forming a high performance computing cluster with a total of 512 cores, and 2048 GB of RAM. Automatic pre-annotations with the proposed strategy reduce the time of human participation in the annotation up to 80% maximum and 60% on average.

scholarly journals Anticipatory experience in everyday autonomous driving

2020 ◽  
Vol 24 (6) ◽  
pp. 747-762
Author(s):  
Thomas Lindgren ◽  
Vaike Fors ◽  
Sarah Pink ◽  
Katalin Osz
Keyword(s):  
User Experience ◽  
Autonomous Driving ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Everyday Lives ◽  
Ethnographic Studies ◽  
Assistance Systems ◽  
Everyday Context

AbstractIn this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday lives for a duration of 1½ years. Our analysis shows that people gradually adopt AD cars, through a process that involves anticipating if they can trust them, what the ADAS features will do and what the longer-term technological possibilities will be. It also showed that this anticipatory UX occurs within specific socio-technical and environmental circumstances, which could not be captured easily in experimental settings. The implication is that studying anticipation offers us new insights into how people adopt AD in their everyday commute driving.

Integration of Security in the Development Lifecycle of Dependable Automotive CPS

2018 ◽  
pp. 383-423 ◽  
Author(s):  
Georg Macher ◽  
Eric Armengaud ◽  
Christian Kreiner ◽  
Eugen Brenner ◽  
Christoph Schmittner ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Security Threats ◽  
Gear Up ◽  
Reliability Engineering ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Development Lifecycle ◽  
Safety And Reliability ◽  
Assistance Systems ◽  
Implementation Level

The exciting new features, such as advanced driver assistance systems, fleet management systems, and autonomous driving, drive the need for built-in security solutions and architectural designs to mitigate emerging security threats. Thus, cybersecurity joins reliability and safety as a cornerstone for success in the automotive industry. As vehicle providers gear up for cybersecurity challenges, they can capitalize on experiences from many other domains, but nevertheless must face several unique challenges. Therefore, this article focuses on the enhancement of state-of-the-art development lifecycle for automotive cyber-physical systems toward the integration of security, safety and reliability engineering methods. Especially, four engineering approaches (HARA at concept level, FMEA and FTA at design level and HSI at implementation level) are extended to integrate security considerations into the development lifecycle.

Sign in / Sign up

Export Citation Format

Share Document