## 6 Experiments and Results
### Visualization  
To visually inspect how well our model performs, we provide a visualization tool, which takes the predicted accelerations and raw camera images as the input, and generates a video as the output. In the three data plots of Figure 7, the horizontal axis represents the frame index of the segment, while the vertical axis means the acceleration value. The ranges of the vertical axes are all limited to \([-2.0,2.0]\) to prevent misinterpretations. The generated video shows both predicted and ground-truth values (shown as \"_prediction_\" and \"_ground truth_\" in the legends) for longitudinal \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **MAE X** & **MAE Y** \\ \hline NN & 0.4014 & 0.4312 \\ CNN & 0.3272 & 0.3123 \\ NN + CNN & 0.2985 & 0.2802 \\ XGBoost & 0.3502 & 0.3537 \\ Light Gradient Boosting & 0.3459 & 0.3444 \\ Stacked Linear Regressor & 0.3623 & 0.3222 \\ \hline LSTM with 12 features & 0.3179 & 0.3088 \\ LSTM with front camera & 0.1379 & **0.1278** \\ LSTM with all cameras & **0.1327** & 0.1363 \\ \hline \hline \end{tabular} The minimum loss is made bold.  
\end{table} 
Table 2: MAE comparison. Compared with the values along the \(x\)-axis and the \(y\)-axis, the values along the \(z\)-axis are small enough to be ignored; thus, the MAE result along the \(z\)-axis is omitted. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Approximated GPU Training Time** \\ \hline LSTM with 12 features & 1.8 h \\ LSTM with front camera & 9.0 h \\ LSTM with all cameras & 37.8 h \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Approximate training time of different models. and lateral accelerations (marked as \"_x acc_\" and \"_y acc_\", respectively). Combining the data plot (left) and actual video frames (right) gives us an intuitive way to inspect prediction deviation within a continuous spatial and temporal range. In terms of driving situations, we analyze visualized results of three common scenarios. * **The \"car-following\" scenario: In the dataset, the \"car-following\" is the most common driving scenario, where the AV maintains a safe distance from the front car and moves at a relative constant  
Figure 7: Key frames representing three different scenarios, all are extracted from segment-10289507859301986274 from tar validation_0001. (**a**) In the first frame of the video, this is a typical “car-following” scenario: the AV follows the truck steadily, and the initial value of the acceleration is around zero; (**b**) In the 108 th frame of the video, the truck turns left and, at this moment, there is no detected front car and the AV accelerates. The ground-truth and the prediction acceleration curves climb simultaneously; (**c**) In the last frame of the video, the declining acceleration curves show that the AV brakes accordingly as the front car stops due to the traffic ahead. speed. As shown in Figure 7a, the current AV follows the truck in front steadily. The acceleration trend changes accordingly with the speed variation of the truck. Comparing with other scenarios, \"car-following\" is the scenarios where the prediction curve is closest to the ground-truth curve. * **The \"front car-turning\" scenario**: In the 108 th frame, the front car makes a turn, the current AV accelerates since there is no leading vehicle blocking the lane ahead. Both the ground-truth and predicted accelerations climbs, while the prediction curve climbs to a lower maximum value. The corresponding visualization is in Figure 7b. * **The \"deceleration\" scenario**: The deceleration happens when the AV detects that the front car decelerates or stops due to the traffic. The model generates negative acceleration values to maintain the safe distance. Figure 7c shows the corresponding visualization. When it comes to lighting conditions, there are other complicated scenarios such as nighttime, rainy days, and so on.