## 2 Materials and Methods
### Obstacle Detection  
The development of a practical obstacle detection and safety system for vehicles often involves working in complex scenarios. The experimental platform includes two LIDAR systems, the LMS-221 and LMS-291. Both lasers (electronic device based) provide non-contact measurement of the surrounding environment in the form of a cloud of 2D data points. This experimental platform is completed by a catadioptric omnidirectional stereovision system [59; 60], which together with the LIDAR make up a reliable obstacle detection system, and it could be useful for other related tasks.  
Figure 6: Implemented flowchart for steering control.  
The chosen system is designed to work in harsh conditions (rain, snow, fog, warm or cold weather), mainly using the LMS-221. Some of the applications are: objects' measurement, positioning with respect to other vehicles and obstacle detection (in particular, pedestrian detection). The LIDAR can be used also to calculate the slope of the road, which can help the control system with additional useful information.  
Figure 7a shows the LIDAR system installed at the bus front, at a height of 930 mm and 1490 mm above the soil plane. The supporting structure is mechanically adjustable to permit several angles of inclination for these sensors. Figure 7b shows the omnidirectional stereo tracking system, which has been attached to the LIDAR system, with the aim of being used in some experimental trials to evaluate the efficiency of the obstacle detection process. With a range of angular action of 180 degrees and its real-time capabilities, the LIDAR can be used to detect animate or inanimate objects. This system can be used for the bus's own safety and that of its passengers in the case of finding inanimate obstacles close to the bus front and/or for the safety of a moving object crossing the bus path.  
Figure 7: (**a**) LIDAR sensors placed at the bus front; (**b**) omnidirectional stereo tracking system; (**c**) dynamic obstacle detection (more critical close up (Section 1), intermediate (Section 2) and less critical (Section 3)).  
In order to test the capabilities of the experimental vehicle, a simplified object classification method is proposed, where the sensory system classifies the sensed scenario into two groups: sections with obstacles and safe path lane areas. The combination of LIDAR systems and cameras is widely used because of its complementary capabilities, and several techniques have been proposed for data fusion [61; 62]. In these works, a standard chessboard was utilized to calculate the transformation matrix between sensors. To compute obstacle detection, the data measurements from lasers are split into several subsets/segments by using the jump distance condition. Then, the subsets are classified into one of the two-defined groups by analyzing the geometrical properties of the segments such as the average deviations from the median and the jump distance to each succeeding segment, as described in [63]. The laser segments detected as obstacles are considered as inputs for computing disparities of the regions of interest of the omnidirectional images.  
Obstacles detected along the vehicle trajectory are sorted into the three warning sections (see Figure 7c). This classification relies on the distance and position between obstacles and the vehicle. When objects are placed in Section 3 or in Section 2, the vehicle decreases its velocity to 40% or 80%, respectively. In that way, a safety distance of 8-12 m could be achieved. However, when an object is detected within Section 1 (2.5 m), a full stop is carried out. Movement is resumed automatically when the obstacle disappears. Due to the vehicle characteristics, an avoidance maneuver was not considered. Many experimental tests were carried out with success.