# A Deep Reinforcement Learning Approach for Efficient, Safe and Conformtable Driving +
## 4 Performance Results
### Results  
We first assess in Figure 5 the system performance under the slippery road scenario. Figure 5 shows the velocity (top left) and acceleration (top right) trend of the ego vehicle and lead vehicle. The corresponding ego vehicle's headway (left) and jerk (right) trend is shown in the bottom plots of Figure 5, concerning the adopted DRL, CACC, and ACC algorithms. The plot also highlights the desired range within which the headway should remain (black lines), for the ego vehicle to follow the lead vehicle and improve road usage efficiency. It is evident that the DRL approach helps the ego vehicle to maintain the headway in the desirable range for a longer period of time compared to its alternatives (97% as opposed to 32%). Furthermore, it is essential to maintain the desired headway during the acceleration and deceleration phases of the lead vehicle to prevent a potential phantom traffic jam situation. Under such conditions, our DRL framework can preserve the headway in the preferred range at all times, while the existing alternatives do so only 30% of the time. In particular, the headway for the traditional ACC and CACC algorithms lies outside the optimal range over 60% of the time. It must be highlighted that the time-to-collision (TTC), a safety indicator, remains above the critical four-second threshold, regardless of whether the headway is within the desired range or not. Figure 4: Urban Scenario: Road network (**top**) and lead vehicle acceleration (**bottom**) in the traffic queuing scenario. The driving comfort is depicted in the right bottom plot of Figure 5. In the slippery road scenario, all algorithms consistently produce jerk metric values that fall within the desirable range, resulting in a pleasant driving experience (the latter being highlighted with black lines). To further investigate the vehicle's stability, Figure 6 depicts the trend of the longitudinal wheel slip. As shown in Figure 6, the longitudinal slip remains within the safe operating range (\(<\pm 0.2\)), guaranteeing the vehicle's stability. As one can see by looking at the plots, the better performance of the proposed DRL solution in terms of road usage efficiency does not come at the cost of any degradation in terms of the vehicle's stability. Figure 5: Slippery road scenario. Top: velocity (**left**) and acceleration (**right**). Bottom: headway (**left**) and jerk (**right**), under DRL, ACC, and CACC. Figure 6: Slippery road scenario: Wheel longitudinal slip with respect to DRL, ACC, and CACC. The top (bottom) left and right plots represent the front (rear) left and right wheels (respectively). Next, we focus on the sharp lead vehicle deceleration scenario. As we can notice from the lead vehicle's acceleration profile in Figure 3, this scenario reflects an emergency situation where the lead vehicle has to brake heavily under an unfavorable road or traffic condition. Figure 7 reveals that the lead vehicle velocity (top left) dropped from 15 m/s to 7 m/s in a span of 1.5 s. As a consequence, the ego vehicle has to brake suddenly and heavily, to avoid a collision. The performance of all three algorithms under this critical scenario are presented in the bottom plots of Figure 7 as well as in Figure 8. The headway performance (bottom left plot of Figure 7) reveals that the proposed DRL framework provides a dramatic improvement with respect to the traditional ACC and CACC algorithms. Figure 8: Sharp lead vehicle deceleration scenario: Wheel longitudinal slip with respect to DRL, ACC, and CACC. The top (bottom) left and right plots represent the front (rear) left and right wheels (respectively). Figure 7: Sharp lead vehicle deceleration scenario. Top: velocity (**left**) and acceleration (**right**). Bottom: headway (**left**) and jerk (**right**) under DRL, ACC, and CACC. The simulation results show that DRL can maintain the preferred headway for 55% of the time, which represents a 19% improvement with respect to traditional algorithms.