## 7 Conclusions and Future Work  
From the experimental results, it is observed that the LSTM-based model has better performance than the baseline models, which also supports our assumption that the acceleration prediction is a time-series problem. Inspired by the architecture of LSTM, the \"encoder-decoder\" structure is adopted in all LSTM-based models. At first, only the basic numeric features such as velocity and distance between the front car and the current AV are used to train the model. Such an LSTM-based model has already outperformed the baseline models without using CNN or those considering time-dependency in the data. To further increase the model performance, camera images are introduced to help the  
Figure 8: Model prediction with synchronized frames captured by the front-camera on the right.  
model output a more accurate result. The result shows that the LSTM-based model with an image has significant improvement. However, the model using the front camera only shares similar performance with the model using all five cameras while the latter took a much longer time to train. Therefore, we conclude that the LSTM-based model with the front camera images is an effective and efficient model for acceleration prediction.  
This work can be extended in several ways. One limitation of the experiments we carried out is that, in the current training and testing process, the \"online approach\" takes the ground truth relative distance and velocities as input to the network after predicting the first frame after the initial 10 frames. However, in the scenario of the real world, the input should be taken from (or accumulated from) the prediction of previous frames. Therefore, our current approach may cause the loss to accumulate continually. To solve this problem, our future work is to generate predictions of one frame immediately after the initial ten frames, and then compute the loss between that specific frame and the ground truth data. Another future direction is to train a more generalizable model for other traffic scenarios than car-following--for example, lane-changing, merging and diverging.  
Conceptualization, X.D. and R.S.; Methodology, R.S. and X.D.; experimental design, Z.L., Z.G. and R.S.; software, Z.L. and Z.G.; analysis, Z.L., Z.G. and R.S.; writing, original draft preparation, Z.L. and Z.G.; writing, review and editing, R.S. and X.D.; supervision, X.D.; and funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.  
We would like to express our great gratitude to other collaborators who have contributed to this work. We would like to thank [PERSON], [PERSON], and [PERSON] for featuring engineering at an early stage, coming up with the definitions of the \"12 features\" on which the models were trained on, as well as developing and testing several baseline models such as the XGBoost method and so on. We also appreciate explorations made by [PERSON], [PERSON], and [PERSON] about including camera images into the training data, which greatly broadened our scope of mind. In addition, we thank [PERSON] and [PERSON] for implementing other neural network baseline models for comparison purposes.  
The authors declare no conflict of interest.