Advanced driver assistance systems (ADAS) hold great promise for improving transportation
safety and efficiency. However, the development of these technologies requires a robust and …

Deep reinforcement learning (DRL) has become the mainstream method to design
intelligent energy management strategies (EMSs) for fuel cell hybrid electric vehicles with …

This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

Figure 1 Feedback mechanism and its applications. success of industrial control. Nowadays,
intelligent systems use multimodal sensing as feedback to mimic, enhance and replace …

Forms of both simple and complex machine intelligence are increasingly acting within
human groups in order to affect collective outcomes. Considering the nature of collective …

To standardize definitions and guide the design, regulation, and policy related to automated
transportation, the Society of Automotive Engineers (SAE) has established a taxonomy …

Current advanced driver assistance systems (ADASs) do not consider drivers' preferences of
evasive behavior types and risk levels under rear-end near-crash scenarios, which …

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms
with a performance improvement guarantee is challenging, mainly attributed to the high …

This article investigates the issue of cooperative real-time trajectory design for multiple
unmanned aerial vehicles (UAVs) to support emergency communication in disaster areas …

The widespread application of Reinforcement Learning (RL) in real-world situations is yet to
come to fruition, largely as a result of its failure to satisfy the essential safety demands of …