\section{Requirement Analysis}\label{sec:requirement-analysis}
Components in autonomous vehicles must meet the unique requirements of the car.
Such applies to the mechanical, electrical, and software components.\cite{DBLP:conf/seuh/Weinmann03}

\subsection{General Requirements for Software and Hardware in Cars}\label{subsec:general-requirements-for-software-and-hardware-in-cars}

\subsubsection{Durability}\label{subsubsec:durability}
The average age of passenger cars in Germany in 2010 was 8.1 years.
In 2019 it was already 9.5 years, and the trend is still rising.
For tractors, the average age in 2019 will even be almost 30 years on average.\cite{kraftfahrtBundesamt}
Accordingly, the additional hardware used for artificial intelligence should also be durable and long\textendash lasting.
Furthermore, tractors, in particular, are already operating autonomously in many countries and are an \q{elementary component of smart farming}.
Smart Farming describes the use of modern communication and information technology in agriculture.\cite{smartFarming}
Cameras have already proven themselves in several industrial sectors and are also used in the automotive industry as rearview cameras and blind\textendash spot assistants for trucks.

\subsubsection{Portability}\label{subsubsec:portability}
Furthermore, the technology to be carried must have a reasonable degree of portability because laws and practical handling limit the space that vehicles may occupy.
However, autonomous driving gives rise to new possibilities for shaping the shape of the cars.
In the fourth and fifth steps of automation, the steering wheel and pedals can be omitted for the driver or concealed while driving in autonomous mode.
One concept for a new form factor is \q{Sedric} from Volkswagen.\cite{Sedric}
The LIDAR sensors (Light Detection and Ranging) on the roof, which seem to be indispensable for approaches of the various developers, should be emphasized.
Since 2019 alone, Google Scholar shows about 16,000 hits for the keyword \q{lidar car} (as of June 2020).\cite{lidarCarResults}

\begin{figure}[!htbp]
    \vspace{0cm}
    \minipage{0.0\textwidth}
    \endminipage\hfill
    \minipage{0.85\textwidth}
    \includegraphics[width=\linewidth]{../data/sedric.jpeg}
    \caption{\q{Sedric} \textemdash a concept car from Volkswagen for autonomous driving\cite{Sedric}}    %Bildunterschrift
    \label{fig:sedric}       %fig:ID
    \endminipage\hfill
    \minipage{0.0\textwidth}
    \endminipage
\end{figure}

\subsubsection{Safety}\label{subsubsec:safety}
The safety of vehicles has a high priority.
A safe vehicle prevents expensive repairs and saves the lives of outsiders and passengers.
Ultimately, this will also become a selling point, especially for autonomous cars.
After all, people entrust their lives to this machine.
Software built into it must be error\textendash free, or at least exclude \q{false positives}, so that the vehicle does not accelerate unintentionally through manipulated traffic signs\cite{sicherheitsbedenken} or steer into oncoming traffic due to misleading points on the road\cite{teslaSecurity}.

The calculations of the software that help to control the vehicle must be performed in real\textendash time to ensure safety.
The latencies and thus the reaction speeds remain at a low level.

\subsubsection{Production Costs}\label{subsubsec:production-costs}
Despite high safety requirements, the price of autonomous cars must be affordable.
That is one of the reasons why Tesla, for example, does not use costly \ac{LIDAR} sensors.
According to Tesla boss Elon Musk, \ac{LIDAR} sensors are too expensive, and all vision tasks are also accomplishable with cameras and low\textendash cost \ac{RADAR} sensors.
Cameras provide significantly more information after resolving the three\textendash dimensional perception, and humans also only use visible light for navigation.\cite{elonMusk}

\vspace{1cm}
\begin{chapquote}{Elon Musk, 2019\cite{elonMusk}}
    \q{Lidar is a fool's errand, and anyone relying on lidar is doomed \textemdash  expensive sensors that are unnecessary. It is like having a whole bunch of expensive appendices. Like, one appendix is bad, well now you have a whole bunch of them, it is ridiculous.}
\end{chapquote}

\subsection{Requirements for Traffic Light Sensor Detection}\label{subsec:requirements-for-traffic-light-sensor-detection}
In order to make the progress of automation objective and also to enable legal discussions, the five levels of driving automation are described in the standard J3016 of the SAE (Society of Automotive Engineers).\cite{J3016B}
To achieve an autonomous vehicle of level 5, Full Driving Automation, or already of autonomy level 3, Conditional Driving Automation, when used in city traffic, the car must recognize traffic light heads correctly.
The current approach of traffic light detection with radio\textendash controlled boxes at the roadside, as in the SAFARI project, works.
However, it has the decisive disadvantage that all traffic lights need a retrofit.
Temporary intersections, such as road works, might not be detected or deviate from the given plan due to flexible modifications.
Road traffic, as it exists today, is designed for visual contact.
Humans navigate by sight alone, except for acoustic support.

Besides, this approach should not use LIDAR. These are, as already mentioned \myref{subsubsec:production-costs}, expensive and do not offer any particular added value since they work like cameras in the visible light spectrum or at the edge of it.\cite{hecht2018lidar}
Thus both have the same problems of particle penetration in snow, rain, dust, or hail.
RADAR does not have these problems, but due to its long wavelength of at least 4 mm, it can have a high inaccuracy -\textemdash  but a more extended range.\cite{rohling2001waveform}

Another advantage of using cameras is the recognition of the color of traffic lights.
Visual recognition alone can already detect the state.
For these reasons, this approach is based entirely on cameras as sensors.

The images received by the camera can be processed either by conventional image processing methods or by neural networks.
Traditional methods require a given algorithm to extract the required information.
A detailed description by algorithms is difficult due to the many different shapes and positions that light signal heads can take on and the danger of confusion with other objects such as the upper brake lights of trucks.
With neural networks, a determination of an algorithm is not necessary.
Adding a neural network can extract this information from the given annotated example data.
There are already many large datasets available for the detection of objects in road traffic.\cite{DBLP:journals/corr/CordtsORREBFRS16,DBLP:conf/icra/FreginMKD18}
Therefore, the use of such a network at this point makes sense.

The YOLO neural network allows the segmentation of images into bounding boxes and classification of those with a precision of 57.9mAP in 51 milliseconds (approximately 20 \ac{FPS}) on a Titan X\@.\cite{DBLP:journals/corr/abs-1804-02767}
In comparison, RetinaNet, a neural network with comparable functionalities, achieves the same precision in about four times the time.
That makes YOLO more suitable for use in real-time environments, such as in autonomous vehicles.

To ensure safety and correctly detect all traffic lights, the network is optimized to precision.
For this purpose, the neural network is newly trained in custom classes.
The implementation will be explained in the following chapters.
