## 1 Introduction
In this competition, Caltech contributed with a highly modified Ford E-350 van, nicknamed Alice [26; 27], and the Team AnnieWAY demonstrated the results of the Cognitive Automobiles Project [28], an autonomous vehicle that is capable of driving through urban scenarios. Another very relevant and cited contribution in the field was offered by [29], where a prototype nicknamed Babieca (a modified Citroen Berlingo), equipped with a color camera combined with DGPS and on-board controls, allowed lateral and orientation position of the ego-vehicle with regard to the center of the lane, so steering an autonomous vehicle using vision. For lateral control and guidance, the use of magnetic markers on the floor was thoroughly investigated [30; 31], and also, the fusion of magnetic markers with encoders has been considered [32]. Significant works on lateral control of heavy-duty vehicles like tractor semitrailers have been produced by the PATH program, implementing nonlinear and adaptive controllers for lateral control of heavy vehicles and presenting an experimental comparative study [33; 34; 35]. Other relevant problems for vehicle automation have been investigated, and, in particular, those related to control systems have received ample consideration: nonlinear control [36; 37], observation of lateral vehicle dynamics [38], external disturbance estimation [39], driver assistance systems based on artificial potential fields [40] and fault-tolerant lateral control for heavy vehicles [41]. While lateral control aims to keep the vehicle at the minimum departure distance from the required path, the longitudinal control [42; 43] mission is to properly control the vehicle advance along the desired trajectory, and, to achieve this, a number of critical adaptations need to be implemented. The main issues are start/stop, acceleration and deceleration, velocity control, obstacle detection/avoidance [43] and several others related to the safety of the overall operation [44]. From another side, it is important also to consider that for improved accuracy and reliability of autonomous vehicles' lateral and longitudinal controllers should be integrated because there is a coupling between steering and velocity actions, and the cross-effects of each one on the other need to be taken into account [45]. This is especially relevant when increasing the speed of operation, in platoon formation, and in variable road and vehicle conditions (slippage, cornering, tire traction forces, etc.). An interesting work regarding automatic steering control based on how drivers steer has been performed by using the data of drivers' steering obtained through a number of experiments, showing that drivers in effect execute a naturally robust controller that allows high-gain corrections and is insensitive to variations in vehicle dynamics and speeds [46]. This controller has been implemented and validated on an 18.3 m articulated bus for revenue service [47]. Having examined briefly the major components that could be needed to achieve autonomous vehicle driving, it is worth mentioning that there is a great interest in the development of more complex strategies like those regarding cooperative autonomous driving [48] aiming to obtain intelligent vehicles that could share roads and streets [49; 50; 51]. More recently and regarding bus automation, a small electric Cyberbus was used to demonstrate a parametric-based path generation algorithm for roundabouts [52]. Other related works have been concentrated on automated parking [53]. It is also worth mentioning the works using artificial intelligence methods based on fuzzy logic to control autonomous vehicles [54; 55; 56] and the latest developments regarding traffic sign detection [57] and an automatic system to detect distraction and drowsiness in drivers [58]. In this paper, an experimental platform for research on the automatic control of an articulated bus is presented [14; 15]. The aim of the platform is to allow full experimentation in real conditions for testing technological developments and control algorithms.