## 5 Results and Discussion
### Effectiveness of KA-DDPG
Figure 10b shows the tracking errors of the longitudinal speed in the cornering scenario. The maximum  
Figure 8: Tracking performance in the straight scenario: (**a**) longitudinal speed tracking; (**b**) longitudinal speed error; (**c**) yaw rate tracking; and (**d**) heading. Figure 9: Torque distributions in the straight scenario: (**a**) baseline and (**b**) KA-DDPG. error of the baseline exceeded 0.2 m/s, while the maximum error of the KA-DDPG method was kept within 0.2 m/s. The yaw rate tracking performance of different methods is shown in Figure 10c, while Figure 10d shows the tracking errors of the yaw rate in the cornering scenario. The KA-DDPG method kept the yaw rate error within 0.2 rad/s, which was significantly smaller than that of the baseline. In the cornering scenario, the tracking performance of the KA-DDPG method was still better than that of the baseline, which is the same as the conclusion in the straight scenario, indicating that the tracking performance did not deteriorate in a more complex scenario. Figure 11 shows the torque distribution curves, for each wheel in the cornering scenario. The torque distributions of the different methods in the cornering scenario showed the same trend as in the straight scenario, and the driving torques of the KA-DDPG method changed more quickly than those of the baseline method. To quantitatively illustrate the tracking performance more specifically, Equation (23) was introduced, to evaluate the tracking performance of the longitudinal speed and yaw rate. The quantitative evaluation results for the simulations in the straight scenario and the cornering scenario discussed above are displayed in Figure 12.  
Figure 11: Torque distributions in the cornering scenario: (**a**) baseline and (**b**) KA-DDPG. Figure 10: Tracking performance in the cornering scenario: (**a**) longitudinal speed tracking; (**b**) longitudinal speed error; (**c**) yaw rate tracking; and (**d**) yaw rate error. Figure 12a shows the quantitative evaluation in the straight scenario. In 500 s of straight driving, \(J_{1}\) in the KA-DDPG method was reduced by 71.36%, compared to the baseline. Similarly, \(J_{2}\) of the KA-DDPG method was reduced by 87.33%, compared to the baseline. The quantitative evaluation demonstrated that the KA-DDPG method has better tracking performance for the longitudinal speed and yaw rate than the baseline in the straight scenario, consistent with the results of the above analysis. The quantitative evaluation in the cornering scenario is shown in Figure 12b. Compared with the evaluation results of the baseline, in the simulation under the cornering scenario, \(J_{1}\) of the KA-DDPG method was reduced by 67.87%, and \(J_{2}\) was reduced by 80.99%, when compared to the baseline. These quantitative evaluations in different scenarios demonstrate that the KA-DDPG method has better desired value tracking performance than the baseline. Based on the analysis of the simulation results, the tracking performance of skid-steering vehicles, based on the KA-DDPG method, was investigated. Compared with the baseline, the KA-DDPG-based torque distribution method showed better tracking performance in different scenarios. Although the baseline is a low-fidelity controller, it was sufficient to illustrate that the KA-DDPG method can be successfully applied to the torque distribution problem of skid-steering vehicles.