Workshop on Foundation Intelligence for Intelligent Vehicles
Organizer(s): Jiaqi Ma, Yutong Wang, Xin Xia
Code: Foundation Intelligence for Intelligent Vehicles
Submission Guidelines: The review process of this workshop paper will be conducted through the submission system of IEEE Transactions on Intelligent Vehicles (TIV):
1.
Visit the submission website of IEEE Transactions on Intelligent Vehicles (https://mc.manuscriptcentral.com/t-iv) and log in.
2. Select "Authors" → "Start New Submission" → "Begin Submission".
3. Choose "Special Issue on Foundation Intelligence for Intelligent Vehicles" from the type menu.
4. Proceed with the upload instructions.
Papers that are accepted must subsequently be submitted by the authors in their final versions through its.papercept.net. Please first choose "Submit a contribution to IEEE IV 2024" and then choose "Workshop paper" under "Type of submission" and use our workshop code Foundation Intelligence for Intelligent Vehicles during the submission process.
Outstanding workshop papers will be recommended for revision for possible paper extension to a full-length TIV regular paper and publication in the TIV Special Issue on Foundation Intelligence for Intelligent Vehicles.
The purpose and scope of this joint workshop
As the field of Intelligent Vehicles (IV) continues to advance, the integration of foundation intelligence technologies is becoming increasingly critical. This workshop is dedicated to exploring the forefront of Foundation Intelligence and its pivotal role in advancing the field of IV. The foundation technologies may encompass a range of established methods like traditional machine learning, data-driven artificial intelligence (AI), and sensor fusion, as well as emerging advancements such as large language models (LLM) and vision language models (VLM), other multi-modal foundation solutions and knowledge-driven AI.
This workshop is dedicated to exploring these key technologies and their integral role in enhancing the capabilities of IV. We aim to delve into how these innovative technologies, are contributing to the development of intelligent vehicles. These models are not only pivotal in processing vast amounts of data for improved decision-making but also play a crucial role in advancing autonomous driving, vehicular communication, and human-machine interactions. We seek contributions that demonstrate the application of these foundation models in various aspects of intelligent vehicles, from enhancing vehicular autonomy and safety to optimizing traffic management and vehicle-to-everything (V2X) communication systems. We welcome submissions that explore the potential of traditional and emerging foundation intelligence technologies in the context of IV, including their application in algorithm development, system integration, real-world implementations, and theoretical advancements. Our goal is to compile a collection of research that not only highlights the current state of these technologies in intelligent vehicles but also provides insights into future directions and innovations in this rapidly evolving field. The topics of interest within the scope of this special issue include (although not limited to) the following:
- Advanced AI-Enhanced Perception Systems for Environmental Understanding in IV
- Implementing Advanced Sensor Technologies in IV for Improved Navigation
- AI-Enabled Predictive Analytics for Vehicle Dynamics and Performance
- Machine Learning in Enhancing IV Safety Features
- Decision-Making in Autonomous IV
- Next-Generation Vehicular Networks for Enhanced IV Communication
- AI Approaches to IV Cybersecurity and Data Privacy
- Enhancing IV Performance by vehicle to everything (V2X) such as Cloud and Edge Computing Technologies
- Social Impact of IV
- Cyber-security of IV
- Human-machine Interaction and Collaboration in IV
- Predictive diagnosis and maintenance of IV
- Motion Prediction and Decision-Making of IV
- Muiti-Sensor Fusion State Estimation of IV
- Testing, Verification and Assessment for IV
- Knowledge-driven AI (e.g., LLMs, VLMs) for IV
Please feel free to contact via email the main organizers Jiaqi Ma ( jiaqima@ucla.edu), Yutong Wang ( yutong.wang@ia.ac.cn) and Xin Xia (x35xia@ucla.edu) if the authors had any questions.