Guest editorial: AI applications to intelligent vehicles for advancing intelligent transport systems

Without a doubt, one of the most promising ways to improve the efficiency and safety of intelligent transport systems (ITS) is through the electrification and automation of intelligent vehicles (IVs). Their current importance in both academia and industry is unquestionable. IVs offer an opportunity to fundamentally change the conventional transport system, with the key advantage of avoiding the huge costs of adapting the transport infrastructure. For example, electric vehicles (EVs) appear to be an effective solution to improve the efficiency of mobility alleviating problems such as energy depletion and air pollution. On the other hand, automated vehicles – from SAE Level 4-, open up a huge range of new shared mobility services, new entertainment services for users, and can lead to a considerable increase in the efficiency and safety of transport in general. In this context, recent advances in artificial intelligence (AI) technologies, such as deep learning, reinforcement learning techniques, machine learning, Bayesian modelling, fuzzy systems, big data, etc., emerge as powerful tools to deal with the system complexity, high nonlinearity, system uncertainty and unknown environment for the perception, decision-making, signal processing, communication and network, sensing, mechatronics, actuation and control of IVs. More specifically, AI-based powertrain modelling, battery state estimation, energy management, optimal charging strategies, smart grid, and energy-oriented vehicle automation, have elongated the driving mileage, and are more efficient, robust, and reliable in the energy management systems. With the help of AI-based techniques, autonomous driving technologies could be more advanced and intelligent to bring more self-driving electric cars, buses, taxis, and trucks on our roads.