## 5 Experiment and Analyses
### Prediction Performances under Different Scenarios  
Vehicle lane-keeping and lane-changing maneuvers are the two most common scenarios in highway driving. To illustrate the prediction performance and demonstrate the advantage of applying the potential field method to describe a priori information of vehicle-vehicle interactions, we introduced another prediction model for comparison, which has the same network architecture as the proposed model in this paper but without the potential field module. Comparisons of the prediction performances for these two models under these two key scenarios are shown in Figures 5 and 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Prediction** & \multirow{2}{*}{**1**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} & \multirow{2}{*}{**Average**} \\
**Horizons (s)** & & & & & & & \\ \hline CV & 0.7 & 1.78 & 3.13 & 4.78 & 6.68 & 3.42 \\ IA-KNN & 0.62 & 1.03 & 1.97 & 2.93 & 4.12 & 2.13 \\ S-LSTM & 0.65 & 1.31 & 2.16 & 3.25 & 4.55 & 2.81 \\ GR-LSTM & 0.68 & 1.17 & 1.74 & 2.64 & 3.32 & 1.91 \\ S-GAN & 0.57 & 1.32 & 2.22 & 3.26 & 4.4 & 2.35 \\ GRIP++ & 0.38 & 0.89 & 1.45 & 2.14 & 2.94 & 1.56 \\ DAM & 0.5 & 1.11 & 1.78 & 2.69 & 3.93 & 2.0 \\ Ours & 0.42 & 0.79 & 1.32 & 2.03 & 2.64 & 1.45 \\ \hline Comparison & +10.5\% & \(-11\%\) & \(-9\%\) & \(-5.1\%\) & \(-10.2\%\) & \(-5.9\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the prediction performances of different models. Figure 5: Trajectory prediction for lane-keeping maneuver. Figure 6: Trajectory prediction for lane-changing maneuver. Figure 5 illustrates the prediction results for the lane-keeping maneuver. The gray and blue dots denote the surrounding vehicles and the target vehicle, respectively. The gray solid line is the historical trajectory of the target and surrounding vehicles. The pink dashed line is the future ground truth trajectory. The purple solid line is the predicted trajectory for the proposed model in this paper and the blue solid line is the predicted trajectory generated by the prediction model without the potential field module. Figure 5a is a congested traffic scenario. The vehicles are accelerating at a low initial speed. Note that there are six moving obstacles around the target vehicle. Although the speed of the front vehicle is slower and blocks the target vehicle, there are surrounding vehicles in both the left and right adjacent lanes, which move side-by-side with the target vehicle within the observation horizon. The free space for lane changing is insufficient. Therefore, both prediction models judge that the vehicle will sustain lane keeping. The model without a potential field module is not sensitive enough to predict the change in the vehicle speed, which leads to a relatively large final distance error (FDE). In contrast, our approach analyzes the collision threat of the front vehicle to the target vehicle more accurately, enabling a better FDE performance. In Figure 5b, the traffic flow is still congested but vehicles are moving steadily. During the observation horizon, no obvious acceleration, deceleration, or lane-changing maneuver occurs, meaning the safety of the object vehicle is not threatened. So, the vehicle trajectory prediction results are continual lane keeping for both prediction models. Similar to Figure 5a, the proposed algorithm predicts the future speed of the vehicle more accurately and the predicted trajectory is closer to the ground truth. In Figure 6a, there are three obstacle vehicles around the object vehicle. Due to its slower speed, the target vehicle is gradually approaching the leading vehicle. But at this time, the right lane is relatively empty in front, with no obstacles. And the historical trajectory of the target vehicle has a tendency of moving to the right. So, it is predicted that the vehicle will execute a lane change action.