# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 1 Introduction  
Since the Fourth Industrial Revolution was discussed at the Davos Forum in Switzerland in 2016, there has been increasing interest in autonomous vehicles. Particularly in urban areas, low-speed autonomous shuttles, which offer advantages in terms of safety and convenience, are being developed and deployed as alternative means of transportation to traditional buses and passenger cars [1, 2]. Autonomous shuttle services aim to replace private vehicles and public transportation to address the transit needs of urban commuters by supporting the connection of short transit nodes that appear in typical mobility patterns, known as the first mile or last mile. This reduces urban congestion and parking problems caused by unnecessary private vehicle usage through shared mobility solutions. Moreover, low-speed autonomous shuttles provide an opportunity for cities to reduce congestion and carbon emissions, thus contributing to net-zero targets and many of the United Nations' Sustainable Development Goals. Designed for operation on predefinedroutes in low-speed environments, they are often used in commercial, business, or university campuses [3]. A similar eco-friendly transportation option in urban areas is the tram. However, installing rails on the ground incurs infrastructure costs, and in existing narrow road sections, it can lead to severe traffic congestion. These autonomous shuttles, depending on their intended purpose, must accommodate either a large number of passengers or provide services that require ample space and thus typically have a longer overall length and width compared to conventional vehicles. To ensure the stable operation of such shuttles on urban roads, driving control technology that allows a small turning radius, suited for the characteristics of urban roads, is required [4; 5]. A small turning radius and the ability to reliably follow a path are essential for driving irregular urban road sections [6; 7; 8]. Recently, to improve urban driving performance, many companies have been adopting four-wheel steering (4 WS) systems, which aim to enhance the lateral driving performance of vehicles by steering the front and rear wheels independently [6; 7]. In the past, 4 WS systems were introduced in some high-end vehicles during the 1980s and 1990s. However, due to high costs and complex structures, their commercialization was limited. Recently, advancements in electronic control technology, along with the integration of 4 WS with autonomous driving and electric vehicle technologies, have brought renewed attention to this system. In particular, by combining 4 WS with electric vehicles' e-corner modules and independent driving technology, vehicle maneuverability can be maximized, offering significant advantages for urban driving, automated parking, and off-road driving [9]. Additionally, 4 WS enables the implementation of innovative steering modes such as crab walking, zero turning, and axis rotation, making it a promising technology for next-generation mobility solutions. These vehicles leverage 4 WS technology to enhance steering performance, improving both driving safety and driver convenience. In particular, improvements in driving dynamics through 4 WS not only enhance high-speed driving stability but also maximize parking and turning maneuverability in tight spaces, establishing it as a crucial and practical technology. The path trackers that can be applied to 4 WS-based autonomous vehicles can be divided into two categories. One is geometry-based, which mainly includes pure pursuit, the Stanley method, etc. [10]. These methods exploit geometric relationships between the vehicle and the path and are often used for low-speed automated driving systems with good interpretability and fast calculation speed [10; 11]. The trade-off between stability and tracking performance is difficult to balance with the pure pursuit method, which ignores the curvature of the path [11]. In general, the Stanley method outperforms pure pursuit in most scenarios.