Contents I. Introduction
Traffic accidents usually result in a large number of casualties and economic losses [1]. The development of intelligent transportation systems (ITS), and intelligent vehicle technology can effectively reduce traffic accidents [2]. As indicated by studies, the tire road friction coefficient (TRFC) is one of the major factors affecting the occurrence of traffic accidents, i.e., traffic accidents occur more frequently on slippery roads [3]. In ITS, vehicles can communicate with other vehicles or roadway facilities to obtain tire-road friction conditions in advance and change driving strategies to improve vehicle safety [4]. Some advanced active safety systems such as path planning [5] and active collision avoidance [6] as well as electronic stability control systems [7] require real-time access to TRFC to work effectively. However, it is impossible to measure TRFC directly using low-cost on-board sensors. Consequently, measuring TRFC directly with special sensors and estimating TRFC with low-cost sensors are two types of popular methods. The former is usually referred to as an off-board sensor-based approach, while the latter is called a model-based method.
