# Cloud Incubator Car: A Reliable Platform for Autonomous Driving
## 3 High-Level CICar Architecture
### Decision-Making System  
To operate reliably in the real world, an autonomous vehicle must evaluate and make decisions about the consequences of its possible actions, anticipating the intentions of other traffic participants. The new Intelligent Decision-Making Systems are based on cognitive systems [25], agent systems [26], fuzzy systems [27], neural networks [28], evolutionary algorithms [29], multi-criteria [30] or rule-based methods [31]. The main element of the CICar is a Decision-Making System which incorporates a new local trajectory planner [32] based on Bezier curves [33]. Path-Planning System (PPS)  
Global planners are a key element in autonomous navigation systems. The development of intelligent transport systems and autonomous vehicles has increased the demand for route planners that allow dynamic route generation, capable of adapting to the varying aspects of the road, such as traffic, roadworks, roads that have been cut off, or unexpected events. The initial requirements for the CICar global planner are listed below:  
1. It must be able to create global routes using digital map sources
2. It should allow an easy introduction of traffic restrictions or unplanned events. 3. It should be executed in an agile manner and produce results in an acceptable time. For the map source used for the global route planning, Google maps was selected, the reasons for this being:  
1. It has an easily accessible Application Programming Interface (API) and is multiplatform. 2. The maps can be downloaded with different layers of information. 3. It allows georeferencing of the map points. 4. It is constantly updated and is available for free. \begin{table}
\begin{tabular}{l l} \hline \hline
**Sensor** & **Characteristics and Configurations** \\ \hline SICK LASER scanner 2D TIM551 & Operating range: 0.05 m... 4 m, Aperture angle: 270\({}^{\circ}\) \\ \hline LIDAR VELODYNE HDL64 scanner 3D & 120 m range, 1.3 Million Points per Second, 26.9\({}^{\circ}\) Vertical FOV \\ \hline Prosilica GT1290 cam & 1.2 Megapixel Ethernet gigabit, RJ45 Ethernet connector, colour \\ \hline DGPS & GPS aided AHRS \\ \hline ToF Sentis3D-M420 Kit cam & Range Indoor: 7 m Outdoor: 4 m, Horizontal FOV: 90\({}^{\circ}\) \\ \hline IMU Moog Crossbow NAV440 CA-202 & Pitch and roll accuracy of \textless{} 0.4\({}^{\circ}\), Position Accuracy \textless{} 0.3 m \\ \hline EMIID RTK GNSS Receiver & 7 mm positioning precision \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensors features of the perception system. The first algorithm evaluated for global route generation was the A* Algorithm [34; 35]. The following disadvantage was reported with the use of the A* algorithm: the algorithm was not capable of generating routes in a reasonable time using the 512 \(\times\) 512 resolution binary maps tested. To increase the performance, the size of the binary maps was reduced by a constant factor (2, 3, or 4). The size reduction of the binary map speeded up the calculation time, but, because of the pixelisation effects, the following problems occurred: (1) the destruction of narrow roads, (2) the deterioration of main roads, and, as a result, (3) the routes generated were not smooth and were very abrupt. As a solution to the problems reported, a new heuristic search algorithm for global route planning, called SCP (Search for Cross Points for obtaining global routes), was developed. The SCP algorithm is based on a binary map of nxm pixels where the navigable routes are given a value of \"1\" and the non-navigable areas a value of \"0\", and an initial point p\({}_{0}\), a goal point p\({}_{\mathrm{g}}\), and an initial angle \(\theta_{0}\) are set. The stages performed by the SCP algorithm to obtain the global route are:  
1. A search direction is chosen ('N'-North, 'S'-South, 'E'-East, 'W'-West) according to the position of the angle between the positions p\({}_{\mathrm{g}}\) and p\({}_{0}\).