# A Deep Reinforcement Learning Approach for Efficient, Safe and Conformtable Driving +
## 5 Conclusions  
We proposed a deep reinforcement learning (DRL) approach to enhance the adaptive cruise control system in connected autonomous vehicles. The proposed strategy aims to achieve traffic efficiency, safety (including vehicle stability), and comfort by integrating and appropriately weighting headway, longitudinal slip, and jerk. In contrast to traditional ACC and cooperative ACC schemes, the proposed method offers much better overall performance. Importantly, the DRL approach outperforms the traditional CACC and ACC algorithms in headway performance by 36% in totality and by 47% during the lead vehicle's speed variation phases, thus resulting in higher traffic flow efficiency under both highway and urban conditions. In addition, the RMSE comparison reveals that the proposed method can achieve a good balance between safety, comfort, and efficiency, maximizing traffic efficiency and enhancing the overall driving experience. Importantly, these results have been obtained under a realistic model of vehicle dynamics and various difficult scenarios. Finally, we demonstrated that the information gathered via V2X communication concerning lead vehicle acceleration is a crucial component of the DRL-based ACC application, which yields significant performance improvements. As future work, we will focus on examining the effect of the vehicle state's estimation uncertainty on the performance of the DRL. Furthermore, an interesting research direction consists of incorporating our DRL algorithm in other frameworks aimed at maximizing fuel efficiency and reducing vehicles' carbon footprint.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**RMSE**} \\ \hline
**Metrics** & **Scenarios** & **DRL** & **ACC** & **CACC** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Headway \\ (Ideal = 1.3) \\ \end{tabular} } & Normal & 0.0278 & 0.0638 & 0.073 \\ \cline{2-5}  & Sharp Deceleration & 0.0629 & 0.5298 & 0.6561 \\ \cline{2-5}  & Traffic queuing & 0.0839 & 1.7594 & 2.2911 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Jerk \\ (Ideal = 0) \\ \end{tabular} } & Normal & 0.2111 & 0.1804 & 0.1693 \\ \cline{2-5}  & Sharp Deceleration & 1.846 & 2.471 & 2.5839 \\ \cline{2-5}  & Traffic queuing & 2.8181 & 1.1773 & 1.2699 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Slip \\ (Ideal = 0) \\ \end{tabular} } & Normal & 0.0028 & 0.0029 & 0.0028 \\ \cline{2-5}  & Sharp Deceleration & 0.0071 & 0.0581 & 0.0594 \\ \cline{2-5}  & Traffic queuing & 0.0202 & 0.01 & 0.0115 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Root Mean Square Error (RMSE) values.  
**Author Contributions:** Conceptualization, C.F.C., N.A. and F.D.; Data curation, D.C.S. and S.H.; Methodology, D.C.S., S.H., C.F.C., N.A. and F.D.; Project administration, C.F.C., N.A. and F.D.; Supervision, C.F.C., N.A. and F.D.; Validation, D.C.S. and S.H.; Writing--original draft, D.C.S.; Writing--review and editing, D.C.S., S.H., C.F.C., N.A. and F.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are not available due to restrictions.  
**Acknowledgments:** This work was developed in the framework of the activities of the Interdepartmental Center for Automotive Research and Sustainable Mobility (CARS) at Politecnico di Torino (www.cars.polito.it, accessed on 19 April 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.