Reduced-Scale Mobile Robots for Autonomous Driving Research

Reduced-scale mobile robots (RSMRs) are extensively used for studying autonomous driving due to their ability to test models and algorithms in physical environments, their lack of constraints related to regulations and laws, and their advantages of low cost and space-saving. Nevertheless, there is currently a lack of systematic analysis and review of these autonomous driving studies involving RSMRs. Hence, this paper comprehensively reviews 134 studies on the application of RSMRs in autonomous driving research. Through the analysis of these studies, we summarize the commonly used methods for three modules (perception, decision-making, and actuation) of the autonomous driving process of RSMRs, and thoroughly examine the main applications (navigation and obstacle avoidance, vehicle fleet coordination, intersection management, parking control, drift control, passenger unease, and hands-free control) covered in these studies. Furthermore, we identify the limitations and gaps in the existing studies related to RSMRs, and provide recommendations for future research initiatives: 1) focusing on common interactive driving events in real-world traffic such as lane changing, merging, cut-in, and overtaking, 2) extending the experiment duration and distance, 3) increasing the randomness in experimental design, 4) exploring the transferability of autonomous driving algorithms from RSMRs to real vehicles, 5) researching on the mixed fleet consisting of manually controlled RSMRs and self-driving RSMRs.