Cristina Olaverri-Monreal

Virginia Tech

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Co-Authors

Matthew Barth

University of California, Riverside

Nikos Papanikolopoulos

Institute of Electrical and Electronics Engineers

Christoph Mecklenbraeuker

Institute of Electrical and Electronics Engineers

Stewart Birrell

Coventry University

Javier Barria

University of Atacama

Fernando García

Universitat Politècnica de Catalunya

Yibing Wang

Shanghai University of Sport

Bart De Schutter

Delft University of Technology

Hani S. Mahmassani

Northwestern University

Hussein Dia

Swinburne University of Technology

Cooperative Institutions

Institute of Electrical and Electronics Engineers

365

Virginia Tech

318

OrthoVirginia

Engineering (Italy)

Canadian Standards Association

The Ohio State University

Chinese Academy of Sciences

Engineering Systems (United States)

University of Memphis

Antea Group (France)

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Unsupervised Traffic Sign Classification Relying on Explanatory Visible Factors

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (2023)

Wilfried Wöber Jakub Waikat Lars Mehnen Cristina Olaverri-Monreal

Intelligent behavior of autonomous vehicles must rely on the understanding of explanatory factors of the environment in order to operate safely. To implement such intelligence, machine learning models such as CNNs are used to identify objects such as traffic signs. However, the process behind arriving at a certain result is hard to understand. Explainable artificial intelligence has the potential to overcome this limitation by unveiling explanatory factors learned by models and hence, increase the reliability of recognition systems. Recent progress in explainable artificial intelligence motivates research in various fields, and their application must become a core part of intelligent transport systems. We present in this paper an explainable and unsupervised methodology for traffic sign classification. The proposed pipeline combines methods for explainable feature extraction as well as out-of-distribution detection, which were previously applied in anomaly detection and evolutionary biology. This pipeline learns explanatory factors of a traffic sign class, and models a classification function without knowing other classes. Our method is evaluated using the GTSRB as well as the Tsinghua-Tencent-100k dataset and compared to a deep learning counterpart, namely GANs. The results show that the presented methodology is feasible to classify traffic sign images, is explainable and outperforms deep learning-based models.

10.1109/itsc57777.2023.10422705

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Road detection with thermal cameras through 3D information

2022 IEEE Intelligent Vehicles Symposium (IV) (2015)

Gustavo Peláez D. Bacara Arturo de la Escalera Fernando García Cristina Olaverri-Monreal

Ground detection algorithms are important for locating the area where a vehicle can travel during autonomous navigation or for collision avoidance Advanced Driver Assistance Systems (ADAS). Although many algorithms rely on a single camera to detect the ground, they don't consider the possibility of combining transformation approaches with additional information that could be obtained from another camera mounted on the vehicle. In this paper, we propose an algorithm for ground detection that allows the use of disparity maps and the combination of infrared information to enhance road detection. To this end we propose an original approach based on a thermal stereo system to analyze the thermal features of the road that are then complemented by the depth information obtained from the disparity map. We tested the algorithm with a variety of complex scenarios and results showed that the 2D and 3D data enabled accurate detection of the road independent of the weather conditions.

Adverse weather

10.1109/ivs.2015.7225695

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Citations (10)

The JKU DORA Traffic Dataset

IEEE Access (2022)

Павло Іванович Ткаченко Novel Certad Gunda Singer Cristina Olaverri-Monreal Luigi del Re

Development and testing of ADAS is largely based on models and simulations, but real data are indispensable for many reasons – to determine the relevant scenarios, to establish a connection between the results of the simulations and the real situation and of course as elements to set up realistic models. Using data, however, is not trivial, as not all data are informative, and even extensive data sets are often incomplete. Indeed, data is not automatically information, and the richness of the data sets is more important than their size. Data should be diverse not only with respect to different scenarios but also geographically in order to be not biased towards a specific location. In this paper, we present a new aerial-view dataset of highway exits and entrances. The data have been collected in Austria and Italy and contain positions, velocities, and accelerations (in both local and global coordinate systems) of cars, vans, and trucks for Austria and additionally for motorbikes and buses in Italy. The uniqueness of the dataset consists not only in the measurements in different countries, but also in the flight height in Austria, where the recordings have been taken from 300 meters altitude allowing to observe an over 600 meters long section of the road. For non-commercial use, the dataset is available free of charge at the IEEE DataPort.

Data set

Geographic coordinate system

10.1109/access.2022.3203188

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Citations (2)

Perceived Pedestrian Safety: Public Interaction with Driverless Vehicles

2022 IEEE Intelligent Vehicles Symposium (IV) (2019)

Miguel Ángel de Miguel Daniel Fuchshuber Ahmed Hussein Cristina Olaverri-Monreal

Trust plays a decisive role in the public's acceptance of the new self-driving car technology. In order to better understand how to promote confidence in vehicle automation safety among the public, we studied pedestrian behavior shortly before and while crossing a marked crosswalk. Such information is also essential for setting parameters for automated vehicles to act accordingly during interactions with pedestrians. Through the analysis of the recorded videos and subjective qualitative data, we identified factors that potentially influence the perception of a road situation as safe in an environment in which vehicles operate with full driving automation (level 5) in a public space. A variety of responses were observed that exhibit several levels of trust, uncertainty and a certain degree of fear. It became clear, however, that the longer the people interacted with the vehicles, the more confident and trusting they became in automation capabilities. The existence of a communication system to interact with driverless vehicles was also evaluated as positive.

Schema crosswalk

Public space

Self driving

10.1109/ivs.2019.8814145

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Citations (21)

Autonomous Vehicle Calibration via Linear Optimization

2022 IEEE Intelligent Vehicles Symposium (IV) (2022)

Georg Novotny Yuzhou Liu Wilfried Wöber Cristina Olaverri-Monreal

In navigation activities, kinematic parameters of a mobile vehicle play a significant role. Odometry is most commonly used for dead reckoning. However, the unrestricted accumulation of errors is a disadvantage using this method. As a result, it is necessary to calibrate odometry parameters to minimize the error accumulation. This paper presents a pipeline based on sequential least square programming to minimize the relative position displacement of an arbitrary landmark in consecutive time steps of a kinematic vehicle model by calibrating the parameters of applied model. Results showed that the developed pipeline produced accurate results with small datasets.

Odometry

Dead reckoning

Position (finance)

Landmark

10.1109/iv51971.2022.9827109

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Citations (2)

Driving the Future: Highlights From the 2023 IEEE Intelligent Vehicles Symposium [President’s Message]

IEEE Intelligent Transportation Systems Magazine (2023)

Cristina Olaverri-Monreal

This year, the IEEE Intelligent Vehicles Symposium (IV), the flagship conference of the IEEE Intelligent Transportation Systems (ITS) Society, took place in June, in Anchorage, AK, USA. The event witnessed remarkable engagement and participation from researchers and scholars worldwide, attracting a significant number of paper submissions from various countries. After a rigorous review process, 300 papers were accepted, showcasing the conference’s high-quality research.

10.1109/mits.2023.3295393

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Autonomous Driving: Framework for Pedestrian Intention Estimation in a Real World Scenario

2022 IEEE Intelligent Vehicles Symposium (IV) (2020)

Walter Morales Alvarez Francisco Miguel Moreno Óscar Sipele Nikita Smirnov Cristina Olaverri-Monreal

Rapid advancements in driver-assistance technology will lead to the integration of fully autonomous vehicles on our roads that will interact with other road users. To address the problem that driverless vehicles make interaction through eye contact impossible, we describe a framework for estimating the crossing intentions of pedestrians in order to reduce the uncertainty that the lack of eye contact between road users creates. The framework was deployed in a real vehicle and tested with three experimental cases that showed a variety of communication messages to pedestrians in a shared space scenario. Results from the performed field tests showed the feasibility of the presented approach.

10.1109/iv47402.2020.9304624

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Citations (21)

Transport & Planning at Delft University of Technology [ITS Research Lab]

IEEE Intelligent Transportation Systems Magazine (2015)

Cristina Olaverri-Monreal

Reports on major areas of research and development at the Delft University of Technology.

Research development

10.1109/mits.2014.2374037

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Queensland University of Technology Transport Research Group [ITS Research Lab]

IEEE Intelligent Transportation Systems Magazine (2020)

Cristina Olaverri-Monreal

The Queensland University of Technology (QUT) Transport Research Group (Figure 1) focuses on advancing knowledge, engineering, and science in traffic management and control; travel behavior; performance evaluation; traffic safety; data analytics; and public and active transportation. Our work is underpinned by a strong intelligent transportation systems foundation. We have a long-standing, productive relationship with the Queensland Department of Transport and the Main Roads and Motor Accident Insurance Commission through our Transport Academic Partnership agreement, and we recently established a partnership with the Brisbane City Council. Through these networks, we undertake strategic transport research and development and build mutual transport capability within the university sector, government agencies, and industry to address existing and emerging transportation challenges.

European commission

10.1109/mits.2020.3017055

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Promotion of Sustainable Transport by a Tracking System for Bicycles

Lecture notes in computer science (2020)

Daniel Braunauer Franz Kopica Cristina Olaverri-Monreal

Promotion (chess)

Sustainable transport

Personal mobility

10.1007/978-3-030-45096-0_22

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