## 2 Background
### Complex Dynamics of Urban Environment
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**SAE Level of Automation**} & \multicolumn{1}{c}{**Testing Requirements**} \\ \hline
**Level (L)** & **Description** & **Example** & \\ \hline \multirow{4}{*}{**L0**} & **No Automation:** There should always be a human driver in the vehicle performing the dynamic driving task. Only warnings and temporary assistance are offered as features. & \multirow{4}{*}{Blind spot warning} & \multirow{4}{*}{Simulation of traffic flow, multiple road types, radar and camera sensors} \\  & **Driver Assistance:** It is necessary to have a driver at all times. Steering or brake/acceleration control is provided through features. & & \\ \hline \multirow{4}{*}{**L1**} & **Partial Driving Automation:** the system is in charge of longitudinal and lateral vehicle motion within a constrained operational design domain. Features include both steering and brake/acceleration control. & \multirow{4}{*}{Adaptive Cruise Control (ACC) \&} & All of the above (AoB) in addition to simulation of vehicle dynamics and ultrasonic sensors \\ \hline \multirow{4}{*}{**L2**} & **Partial Driving Automation:** the system is in charge of longitudinal and lateral vehicle motion within a constrained operational design domain. Features include both steering and brake/acceleration control. & \multirow{4}{*}{ACC \& lane centering at the same time.} & \multirow{4}{*}{AoB and the simulation of a driver monitoring system and machine–human interaction} \\ \cline{1-1} \cline{3-4}  & **Conditional Automation:** in case of any failure, the system can request the human intervention. & & \\ \hline \multirow{4}{*}{**L4**} & **High Automation:** the automated driving system is in charge of detecting, observing, and reacting to events. Features can operate the vehicle in a few limited scenarios. & \multirow{4}{*}{High Driving Automation} & \multirow{4}{*}{AoB and the simulation of traffic infrastructure and dynamic objects} \\ \cline{1-1} \cline{3-4}  & **Full Automation:** L5 Avs will be able to navigate through complicated environments and deal with unforeseen circumstances without human interaction. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Testing and Simulation Requirements to meet SAE Automation Levels. vehicle (V2V) communication, and creating short-term platoons. The collaboration would result in increased safety and traffic flow at the roundabout. _(iii) Scene 3 with Congestion Settings_--the road segments that are usually in the city centers with a lot of traffic and reduced speed. In this scenario, the local perception of the environment is not enough for the AV to navigate safely and timely in these congested settings. Therefore, realizing the solution of autonomous driving for such settings requires enhanced vehicular collaboration and platooning format for very short time quanta. _(iv) Scene 4 with Sharp Turin and Vulnerable Road Users_--the challenge is obvious for vehicles that solely rely on onboard sensors. These classical vehicles would not be able to detect pedestrians due to manifold reasons: (1) due to the limited local perception of the vehicle; (2) occlusion and deformation; (3) due to weather conditions. It is claimed that the realization of the collaboration using V2V and vehicle-to-infrastructure (V2I) communication would provide AVs with an extended perception of the environment as a result, accurately predicting the pedestrians and vulnerable road users. _(v) Scene 5 with Sudden Speed Change Zones_--the road segments with a sudden change in the speed zones force the vehicles to react unprecedentedly. The AV equipped with heterogeneous sensors (camera, LiDAR, Radar, etc.) is capable of detecting road signs, however, the conventional decision making with a narrow environmental understanding and short lifespan of decisions in terms of time and distance would compel the AVs to make myopic decisions, resulting in erroneous decisions, injuries, fatalities, etc.