# End-to-End Automated Guided Modular Vehicle
## 2 Background Overview
### Navigation Approaches  
By defining the levels of driving automation, it is possible to know the scope and limitations that a navigation system must have when driving the vehicle. The generation of navigation systems for AVs has been achieved in recent years through various approaches. Two main scopes aim to solve the navigation problem:  
* **Traditional approach:** It focuses on the individual and specialized tasks for perception, navigation, control, and actuation. The environment and the ego vehicle are modeled with the gathered data. Thus, for highly dynamic environments this approach is limited by the computing power, model complexity, and sensor quality. Robotic systems have used this modular solution for many years. Its complexity tends to be directly proportional to the model's accuracy [2; 3; 9]. * **End-to-end approach (E2E):** This scope allows users to generate a direct correlation of the input data of a system with certain output data through the use of Deep Learning (DL). Artificial intelligence has been a breakthrough for AGVs. Machine learning (ML) serves to generate intelligent perception and control systems for robot navigation. Deep Learning (DL) is a state-of-the-art technique for creating models that resemble human behavior and decision-making [10; 11; 12; 13; 14]. DL is trained with positive and negative examples using Deep Neural Networks (DNN). There are several types of architectures to choose from, including Convolution or Deconvolution layers, Recurrence or Memory layers, and Fully Connected Neurons, as well as Encoding or Decoding layers. These layers form popular architectures, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long-Short Term Memories (LSTM), and Fully Convolutional Networks (FCN), among others. Specific architectures have been generated by companies or development groups, such as LeNet, GoogLeNet, VGG, ResNet, Inception, and others [15; 16]. The performance of each type of network will strongly depend on the data with which it is trained. Data comes mostly from the ego vehicle's front view, along with little processed data [17; 18]. Outputs are usually referenced values to be given to closed-loop controllers for the steering, speed and braking systems [14]. Among all the DNN, convolutional neural networks (CNN) have proven to be \begin{table}
\begin{tabular}{c|p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**SAE Levels** & **Driver Tasks** & **Vehicle Tasks** & **Examples** \\ \hline \multirow{3}{*}{0} & The driver has the main control even though some of the automated systems are engaged. & The systems work only as momentary assistance providing warnings. & Blindspot warnings, lane departure warnings, parking alerts assistance. \\ \cline{2-4} \multirow{3}{*}{1} & The driver must superise the environment and the system to interfere as he requires it. & The vehicle has partial control and assistance in the steering OR brake/acceleration. & Lane centering OR adaptive cruise control. \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{2} & The vehicle controls the steering and brake/acceleration in specific circumstances. & Lane centering AND adaptive cruise control at the same time. \\ \cline{2-4} \multirow{3}{*}{3} & Passive passengers when automated driving is engaged. The driver needs to intervene upon the system request. & The vehicle has autonomous driving in limited conditions and will not operate unless all the conditions are met. & Traffic jam and highway chauffeur. \\ \hline \multirow{3}{*}{4} & Passive passengers when automated driving is engaged. & will not operate unless all the conditions are met. & Local driverless taxi (pedals or steering wheel is optional). \\ \cline{2-4} \multirow{3}{*}{5} & The passenger never takes over control. & The vehicle has driving control under all conditions. & Robo taxi or full autonomous car (pedals and steering wheel are not required). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Levels of driving automation based on the Society of Automotive Engineers (SAE) J3016 [6; 7; 8].