ITSIVUE 2025

IEEE ITSC Workshop ITSIVUE 2025

ITSIVUE 2025

Fifth Workshop on Intelligent Transportation Systems, Intelligent Vehicles and Advanced Driver Assistant Systems for Unstructured Environments

18 November, 2025 ● ITSC 2025 ● Gold Coast, Australia ● Hybrid mode

About the Workshop

The 5th Workshop on Intelligent Transportation Systems, Intelligent Vehicles and Advanced Driver Assistant Systems for Unstructured Environments will be held on November 18, 2025 as part of the Workshops at IEEE International Conference on Intelligent Transportation Systems (ITSC) 2025 at Gold Coast, Australia.

Workshop Schedule

Speakers

James Misener is Senior Director, Product Management and Global V2X Ecosystem Lead at Qualcomm Technologies, Inc. He drives global C-V2X deployment strategies and develops IoT solutions for transportation markets. A pioneer in vehicle automation and safety communications, Jim previously led automotive standards at Qualcomm and advanced key projects at UC Berkeley’s PATH. He also serves on the boards of IEEE ITS Society, 5GAA, and ITS America, and is an IEEE Fellow.

Javier Ibanez-Guzman is a Corporate Expert in autonomous systems at Renault SAS and the Co-Director of the SIVALab Common Laboratory, a collaboration between CNRS, UTC Compiègne, and Renault, dedicated to advancing intelligent vehicle technologies. He is also an Expert for the EU and Eureka Research Programs.

Dr. Ibanez-Guzman is a Chartered Engineer (C.Eng.) and a Fellow of the Institute of Engineering Technology (IET), UK. A Life Member of IEEE, he serves as a Senior Editor and Associate Editor for related IEEE Transactions and is the Area Editor for the IEEE IROS Conference from 2023 to 2025. Additionally, he represents his country in ISO groups associated with automated vehicles and AI.

Kangwon Lee is an Associate Professor in the Department of Mechanical Engineering at TUKorea (formerly Korea Polytechnic University). He received his Ph.D. in Mechanical Engineering from the University of Michigan–Ann Arbor in 2004, where he developed early collision-warning and human-driver modelling algorithms. From 2004–2007 he was a Senior Algorithm Engineer at Autoliv Electronics America, designing radar-based threat assessment and adaptive cruise control systems. His recent research interests include cybersecurity and applications of LLMs toward programming education. He presented on Rust qualification challenges at the 2024 AAAM+KASA Conference and serves as Chapter Organizer of the IEEE ITSS Korea Chapter.

Ross Greer is an Assistant Professor in the Department of Computer Science & Engineering at the University of California, Merced, and completed the Ph.D. in Electrical & Computer Engineering at the University of California, San Diego in Mohan Trivedi's Laboratory for Intelligent & Safe Automobiles. His research explores questions in computer vision and machine learning related to safe, human-interactive autonomous systems, especially focused on handling cases which sit on the long-tail distribution of novel and risky real-world scenarios. His research emphasizes active and representation learning, vision-language and multimodal information, human-robot interaction, and autonomous perception, prediction, and planning.

Catherine M. Elias is a lecturer in the Computer Science and Engineering Department, Faculty of Media Engineering and Technology (MET). She received her Ph.D. degree in Mechatronics Engineering from the GUC in Dec. 2022 in the field of Cooperative Architecture for Transportation Systems. She is currently the director of the Cognitive Driving Research in Vehicular Systems (C-DRiVeS Lab), working in the field of autonomous driving modules. She serves as a Board of Governors (BoG) member in the IEEE ITS Society during the interval 2023-2025, the 2023-2025 Co-chair of the committee on Diversity, Equity, and Inclusion in ITS committee chair, and the 2023-2025 IEEE TAB Committee on Diversity & Inclusion representative for the ITS Society.

Ayesha Choudhary is an Assistant Professor in the School of Computer and Systems Sciences, Jawaharlal Nehru University (JNU), New Delhi, India. She received her Ph. D. in Computer Science and Engineering from the Indian Institute of Technology, Delhi. Her research interests include computer vision and machine learning with focus on applications in intelligent transportation systems, assisted living applications, behavioral studies and cybersecurity. She is the founding chair of the IEEE ITSS Delhi Chapter and currently serves as the Secretary of the IEEE ITSS Standards Committee.

S. Indu is a Professor in the Department of Electronics & Communication Engineering and Dean, Digital Education at the Delhi Technological University (DTU), Delhi, India. She received her Ph. D. in the area of Visual Sensor Networks from University of Delhi, Delhi, India. She worked as Vice Chancellor of DTU during Sept-Nov. 2023 and was Dean, Student welfare, DTU for 6 years and HOD, ECE Department of DTU for 3 years. Her research interests are in computer vision, machine learning, sensor networks and embedded systems. She received Commendable research award of DTU for 6 consecutive years from 2018 to 2024 . She also received Premium Research award in 2024. She is the recipient of Best Branch Councillor award from IEEE USA and also recipient of Outstanding Branch Councillor award of IEEE Delhi section for 5 consecutive years from 2013-2018. Currently, she is the Treasurer of the IEEE ITSS Delhi Chapter.

Scope of the Workshop

There is a growing demand for Intelligent Transportation Systems (ITS), Intelligent Vehicles (IV), Advanced Driver Assistant Systems (ADAS) and Assistive Mobility (AM) in unstructured environments. Current solutions deployed by vehicle manufacturers in the ITS, IV and ADAS space are based on the assumptions that the environment is well-structured with well-defined lanes, road surface markings, traffic signs and lights and stringent and well-defined traffic rules are followed.

These assumptions may not hold in unstructured environments where there may be a need to identify the drivable area, free of all dynamic and static obstacles as well as road anomalies. Detection of static obstacles such as vehicles parked on the side of the road, presence of vendor carts and people around it, various barriers, trees, poles, sinages as well as dynamic obstacles such as animals, pedestrians, cyclists, and other standard and non-standard vehicles, are important for safe driving. Presence of a large number of two-wheeler traffic also pose challenges, both for the riders as well as the drivers of other vehicles. Lack of proper lane markings, traffic signals and traffic lights along with presence of road anomalies such as pot-holes, water puddles, cracks, etc. add to the complexities of driving in such environments. Therefore, the ITS, IV and ADAS solutions developed and deployed in well-structured environments are difficult to seamlessly adapt to unstructured environments. These challenges offer new research opportunities for developing more robust and all-encompassing ITS, IV and ADAS solutions. These novel solutions will also help advance the areas of assistive mobility and transportation. Driver behaviour is highly affected by these driving conditions, and therefore, these conditions also pose novel problems in the area of driver monitoring.

Intelligent traffic management in such scenarios also poses novel challenges, not present in the well-structured environments. Optimal routing, scheduling and management of various modes of transportation such as Road, Railways, Air and Sea also form a part of intelligent transportation. These complex transportation networks require highly complex real-time computational algorithms to ensure time bound and accurate performance. Communication among vehicles, use of wireless networks and smartphone apps for V2X communication in such scenarios also bring in a large number of novel research challenges, particularly in ensuring the security and reliability of communication channels and the trustworthiness of the devices and onboard systems involved.

Assistive mobility gives dignity of life to the less abled, giving them the freedom to lead a normal life. Unstructured environments currently pose many challenges for assistive mobility solutions to be deployed easily. Therefore, not only is it important to address the challenges of complex unstructured environments for bringing safety and autonomy in vehicles, transport and traffic infrastructure, it is also necessary to adapt or find novel solutions in the areas of assistive mobility in unstructured environments.

The aim of the workshop is to engage with the the vast and diverse research community to explore and find novel solutions or adapt existing solutions to the problems in the areas of Intelligent Transportation systems, Intelligent vehicles and Advanced Driver Assistance Systems in chaotic and unstructured environments. The workshop will also be a platform to share with the research community the ongoing research efforts in these areas. It will also be a good platform to discuss approaches to gather and share large amounts of real, high quality data of the varied unstructured environments.

Past Workshops

The 1st Workshop on Intelligent Transportation Systems, Intelligent Vehicles and Advanced Driver Assistant Systems for Unstructured Environments (ITSIVUE2020) was held in virtual mode on November 6, 2020 as part of the Workshops at 31st IEEE Intelligent Vehicle Symposium (IV), 2020. The 1st workshop was successfully conducted with a Keynote talk, 4 invited talks and paper presentations with more than 60 participants.
The 2nd Workshop on Intelligent Transportation Systems, Intelligent Vehicles and Advanced Driver Assistant Systems for Unstructured Environments (ITSIVUE2021) was held in virtual mode on July 11, 2021 as part of the Workshops at 32nd IEEE Intelligent Vehicle Symposium (IV), 2021. The 2nd workshop was successfully conducted with 2 invited talks, a panel discussion, paper presentations and spot-light presentation and had more than 65 attendees.
The 3rd Workshop on Intelligent Transportation Systems, Intelligent Vehicles and Advanced Driver Assistant Systems for Unstructured Environments (ITSIVUE2022) was held in virtual mode on June 2, 2022 as part of the Workshops at 33rd IEEE Intelligent Vehicle Symposium (IV), 2022. The 3rd workshop was successfully conducted with 2 invited talks, a panel discussion, an Industry R&D session and spot-light presentation and had a large participation.
The 4th Workshop on Intelligent Transportation Systems, Intelligent Vehicles and Advanced Driver Assistant Systems for Unstructured Environments (ITSIVUE2024) was held in hybrid mode on June 2, 2024 as part of the Workshops at 35th IEEE Intelligent Vehicle Symposium (IV), 2024 at Jeju Island, Korea. The 4th workshop was successfully conducted with 1 Keynote, 3 invited talks, and oral paper presentations and had a large participation both offline and online.

Topics of Interest

Topics of interest include all aspects of ITS, IV, ADAS and Assistive Mobility including but not limited to:

Intelligent Vehicles for unstructured environments
Road scene understanding in unstructured environments
Semantic labelling, object detection and recognition in complex road scenes
Operational Design Domain
Two-wheeler mobility in complex environments
Advanced Rider Assistance Systems for Intelligent two-wheelers
Driver Activity Monitoring Systems for drivers in chaotic environments
Intent recognition and behaviour prediction of vehicles and VRUs.
Design and development of robust and real-time systems for IV and ITS in unstructured driving conditions
Use of emerging sensors (e.g., multispectral, RGB-D, LIDAR and RADAR) and sensor fusion for IV and ITS in unstructured environments
Safety applications for vulnerable road users in complex and unstructured driving environments
Datasets and Evaluation Procedures for complex and unstructured driving environments
Real-world driving datasets for unstructured environments
Advanced Driver Assistance Systems in Unstructured Environment with presence of:
- - - novel vehicle classes
    - unmarked, or incomplete or disoriented road signs
    - high density traffic
    - harsh environmental conditions
    - environment influenced by climiate changes
    - unpredictable behaviour of traffic agents (pedestrians, cyclists, vehicles, animals, etc.)
    - presence of various types of animals on the road
    - large intra-class variations in appearance of vehicles
Autonomous driving
Intelligent Transportation Systems
Intelligent Traffic management
Railways, Air transportation and Sea transportation with highly complex traffic scheduling
Real-time decision making
Assistive mobility in Unstructured Environments
- - Applications for the visually impaired
  - Applications for the aging society
  - Applications for hearing impaired