Abstract: Drivers are the center of vehicles and transportation systems. Because of the rapid development of advanced technologies, artificial drivers have been developed as key elements in vehicles and transportation systems. The inconsistency between human drivers and artificial drivers will lead to accidents and congestion. To make future vehicles and transportation systems trustworthy in driving safety and acceptable in travel efficiency, developing technologies based on human drivers’ reliable knowledge and cognitive intelligence together with smart operations is an essential and promising solution. However, there are many challenges to be addressed including the learning of smart human perception, reliable smart inference strategies in decision-making, adaptive correction of inappropriate driving operation, knowledge mapping and enhancement of smart human driving in various scenarios, etc.
To alleviate these challenges, emerging technologies inspired by human intelligence (e.g., self-supervised learning, reinforcement learning, game theory, etc.) have been extensively developed in the related communities. This special issue aims to provide a platform for researchers, engineers, and policymakers to share their latest innovative ideas and contributions in developing and applying these novel technologies to address the challenges concerning human-like smart autonomous driving in intelligent vehicles and transportation systems.
Four articles are collected in this feature topic that promotes the recent advances in the field of human-like autonomous driving systems. The feature topic highlights the progress in environment perception, driver behavior analysis, human-vehicle shared control, and decision-making. The core contributions of these four articles are listed below.