# A Deep Reinforcement Learning Approach for Efficient, Safe and Conformtable Driving +
## 4 Performance Results
### Results
Similarly, DRL outperforms the (C)ACC algorithms during the transient phase, keeping the headway in its desired range 28% of the time--a considerable improvement over the 13% achieved by traditional algorithms. Furthermore, the DRL's headway trend persists around the desired region for a longer period of time than its alternatives. In terms of comfort (bottom right plot of Figure 7), the jerk values occasionally exceed the preferred region for all algorithms, whereas the DRL approach can significantly reduce the intensity and duration of jerk spikes. We observe that, in such a critical scenario, the TTC briefly falls below 4 s (between 5.9 s and 7.0 s) when the velocity of the lead vehicle decreases abruptly for the DRL-based scheme; however, the fact that the headway remains above 1 s indicates that the situation is not particularly dire. In addition, the DRL model is intended to prioritize safety over comfort, as the jerk is only considered for TTCs greater than 4 s (as demonstrated by Equation (10)). In terms of vehicle stability, Figure 8 shows that traditional ACC-based approaches have longitudinal slip values outside the desired range, resulting in the ego vehicle's poor performance and compromising the driver's and passengers' safety. On the contrary, our DRL-based solution achieves the desired performance level, demonstrating that it can provide an excellent trade-off between the objectives and achieve better road usage efficiency and comfort. In the traffic queuing scenario, as shown by the lead vehicle acceleration trend in Figure 4, the lead vehicle slows down at a deceleration rate of 3 m/s\({}^{2}\) in order to align with the velocity of the urban traffic. This situation requires a similar response from the ego vehicle to avert a potential collision and keeps the traffic flow smooth. Therefore, the DRL agent that runs on the following vehicle (i.e., the ego vehicle) has to learn the behavior of the lead vehicle to avoid the collision while sustaining a good and nearly constant headway to ensure traffic efficiency. Since we are dealing with a low-velocity situation, we calculate the headway based on the saturated ego vehicle velocity, as reported in Equation (5). The top plots of Figure 9 depict the acceleration and velocity trend: the lead vehicle's velocity profile demonstrates a rapid deceleration from 12 m/s to 1 m/s in just 5 s, with subsequent changes in velocity to keep up with the traffic. The headway plot (bottom left plot of Figure 9) reveals that DRL is able to sustain the headway near the desired region more often than the traditional algorithms: the conventional approaches indeed leave a wider space between the vehicles, resulting in a decrease in traffic flow efficiency at lower speeds. In terms of traffic flow efficiency, the DRL framework outperforms other approaches, as it can sustain the preferred headway for a longer amount of time, while (C)ACC algorithms can only sustain it briefly when the lead vehicle is cruising at a steady speed. Additionally, during the transient phase, the DRL can sustain the desirable headway 50% of the time, which is substantially better than the alternatives' 4%. The Root Mean Square Error (RMSE) comparison, which is presented in Table 1, further highlights the (C)ACC algorithm's significant deviation from the desired region. In terms of comfort (bottom right plot of Figure 9), jerk values are out of the ideal range occasionally for all algorithms. However, given the gravity of the situation, achieving the desired headway and ensuring safety are critical, surpassing the need for passengers' comfort. In the DRL framework, the maximum value of jerk is observed as the vehicle approaches zero velocity. This is due to the usage of the saturated ego vehicle velocity in the headway calculation, as stated in Equation (5), resulting in a lesser headway, which activates the heavy braking action. Using the saturated velocity, however, is a crucial step in controlling the vehicle at lower speeds.