A Review and Strategic Approach for the Transition towards Third-Wave Trustworthy and Explainable AI in Connected, Cooperative and Automated Mobility (CCAM)

Kanak, Alper; Ergün, Salih; Atalay, Ali Serdar; Persi, Stefano; Karci, Ahu Ece Hartavi

doi:10.1109/apcc55198.2022.9943588

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When passengers or traffic authorities can discern why an autonomous vehicle took a specific action, belief in the technology surges, and the system's accountability is reinforced. Moreover, XAI substantially aids in error detection and correction by identifying instances where the DL model may generate incorrect or unsafe predictions [252], [253]. For instance, if a model misclassifies a traffic sign or consistently makes erroneous decisions, XAI can pinpoint the source of the error, enabling developers to refine the model and enhance safety.…”

Section: F Explainable Ai For Safe and Trustworthy Its Systemsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

show abstract

Section: F Explainable Ai For Safe and Trustworthy Its Systemsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

Applications of Blockchain and Smart Contracts to Address Challenges of Cooperative, Connected, and Automated Mobility

Kontos,

Panagiotakopoulos,

Kameas

2024

Sensors

View full text Add to dashboard Cite

Population growth and environmental burden have turned the efforts of cities globally toward smarter and greener mobility. Cooperative and Connected Automated Mobility (CCAM) serves as a concept with the power and potential to help achieve these goals building on technological fields like Internet of Things, computer vision, and distributed computing. However, its implementation is hindered by various challenges covering technical parameters such as performance and reliability in tandem with other issues, such as safety, accountability, and trust. To overcome these issues, new distributed and decentralized approaches like blockchain and smart contracts are needed. This paper identifies a comprehensive inventory of CCAM challenges including technical, social, and ethical challenges. It then describes the most prominent methodologies using blockchain and smart contracts to address them. A comparative analysis of the findings follows, to draw useful conclusions and discuss future directions in CCAM and relevant blockchain applications. The paper contributes to intelligent transportation systems’ research by offering an integrated view of the difficulties in substantiating CCAM and providing insights on the most popular blockchain and smart contract technologies that tackle them.

show abstract

A Review and Strategic Approach for the Transition towards Third-Wave Trustworthy and Explainable AI in Connected, Cooperative and Automated Mobility (CCAM)

Cited by 2 publications

References 17 publications

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Applications of Blockchain and Smart Contracts to Address Challenges of Cooperative, Connected, and Automated Mobility

Contact Info

Product

Resources

About