## 4 Conclusions  
In this work, an experimental software development process for rapid prototyping of AD features in the automotive field is presented. Exploiting the functionalities of two different embedded systems--Raspberry Pi 4B board and Speedgoat Baseline real-time target machine, a trajectory planning method for an autonomous racing vehicle was implemented, developing two PIL complementary architectures. The investigated trajectory planning method proposed a custom realization of the RRT algorithm with Dubins curves able to compute a feasible trajectory, considering a 3-DOF linear bicycle vehicle model. The autonomous racing vehicle dynamics is controlled with a MPC that computes the front wheel steering angle \(\delta\) and longitudinal acceleration commands \(a_{x}\) in feedback loop with respect to the vehicle plant. The method was first tested in MIL configuration in a properly structured driving environment, that features multiple traffic cones representing non-crossable obstacles. The consistency of the planned trajectory was evaluated during different maneuvers, and it was also evaluated in terms of feasibility of the command signals with respect to the steering and acceleration actuators used in the retained vehicle. Subsequently, the PIL architecture is defined by splitting the components of the model in two groups: a first set running on the target computer with a compiled application, containing the motion planning and control, and another set, i.e., the vehicle model only, running on the host PC. Signals exchanged between target hardware and host PC are sent via UDP protocol. The first PIL architecture was built using the Raspberry Pi 4B board with the objective of testing the software efficiency and signals calculation precision. Afterwards, the PIL architecture was switched to the Speedgoat Baseline real-time target machine in order to consider the real-time constraints and control average TET. Subsequently, a quantitative analysis has been performed between MIL and PIL architectures to further prove the consistency of the two investigated layouts. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Raspberry**} & \multicolumn{3}{c}{**Speedgoat**} \\ \hline  & & **Straight** & **Left** & **Right** & **Straight** & **Left** & **Right** \\  & & **Turn** & **Turn** & **Turn** & **Turn** & **Turn** & **Turn** \\ \hline \multirow{2}{*}{\(a_{x}\)} & RMSE & 0.76 & 1.08 & 0.91 & 0.546 & 0.861 & 0.593 \\  & MAE & 0.49 & 0.58 & 0.56 & 0.28 & 0.43 & 0.35 \\ \multirow{2}{*}{\(\delta\)} & RMSE & \(1.70\times 10^{-5}\) & 0.03 & 0.08 & \(1.67\times 10^{-5}\) & 0.02 & 0.003 \\  & MAE & \(7.26\times 10^{-6}\) & 0.02 & 0.06 & \(6.89\times 10^{-6}\) & 0.09 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE and MAE values for the controlled variables \(a_{x}\) and \(\delta\) in both PIL architectures. The PIL procedures demonstrate that the proposed architectures provide relevant results within the framework of V-cycle development process, ensuring that new functionalities of self-driving vehicle can be rapidly deployed, tested, and validated on generic-purpose platforms. This approach considerably decreases the time to proceed towards the hardware-in-the-loop (HIL) stage. A further extensive experimental validation phase is needed before generalization of the approach to any automotive systems. Conceptualization, E.T., S.L., S.F. and A.B., methodology, E.T., S.L. and S.F. ; software, E.T., S.L. and S.F. ; validation, E.T., S.L. and S.F. ; investigation, E.T., S.L. and S.F. ; writing--original draft preparation, E.T., S.L. and S.F. ; writing--review and editing, E.T., S.L. and S.F. ; visualization, E.T., S.L. and S.F. ; supervision, A.B. and N.A. ; project administration, A.B. and N.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not available. Data Availability StatementData available on request due to restrictions.