<p align=center>Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking<br>

<p align=center> This repo contains datasets and code files for Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking.<br>

Table of Contents

• TrajImpute Dataset
• Trajectory Imputataion Benchmarking
• Trajectory Prediction Benchmarking

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<p align=center> TrajImpute Dataset<br>

The dataset can be downloaded from Download Link. The structure of the TrajImpute dataset follows a dictionary format with specific keys as shown in /TrajImpute.png:

We use the same Dataloader for pedestrian trajectory generation as prior methods (referred from Social-GAN at https://github.com/agrimgupta92/sgan/blob/master/sgan/data/trajectories.py). Upon this data, we generate the missing observed trajectories. The missing value generation code is provided in <tt>data_generation.py</tt>.

python generate_data.py <data_test_file_path> <data_train_file_path> <data_val_file_path> <save_file_path>

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<p align=center> Trajectory Imputataion Benchmarking <br>

Results for Various Imputation Methods on Different Datasets

Results obtained for various imputation methods on the ETH-M, HOTEL-M, UNIV-M, ZARA1-M, and ZARA2-M subsets of TrajImpute with the easy protocol ($0 \leq \text{missing} \leq 4$) and the hard protocol ($4 \leq \text{missing} \leq 7$). The reported results show that SITES performs relatively better when imputing missing values. `M' refers to missing, indicating that the subset contains missing observed coordinates.

• Code for the Transformer [Code .]
• Code for the US-GAN [Code]
• Code for the BRITS [Code]
• Code for the M-RNN [Code]
• Code for the TimesNet [Code]
• Code for the SAITS [Code]

| Datasets | Methods | Metrics | Transformer | US-GAN | BRITS | M-RNN | TimesNet | SAITS | |----------|-----------------|---------|-------------|--------|--------|--------|----------|--------| | ETH-M | Easy-impute | MAE | 3.1318 | 0.6467 | 1.4287 | 5.2558 | 1.1353 | 0.5031 | | | | MSE | 19.4576 | 1.8055 | 4.7339 | 35.3738| 4.9441 | 0.9909 | | | | RMSE | 4.4111 | 1.3437 | 2.1758 | 5.9476 | 2.2235 | 0.9954 | | | | MRE | 0.5236 | 0.1081 | 0.2389 | 0.8787 | 0.1898 | 0.0841 | | ETH-M | Hard-impute | MAE | 3.2249 | 3.0451 | 3.0371 | 5.3309 | 1.3656 | 0.9965 | | | | MSE | 19.5948 | 18.0716| 17.9457| 35.5047| 4.9937 | 2.5934 | | | | RMSE | 4.7926 | 4.2511 | 4.2362 | 5.9965 | 2.5054 | 1.6104 | | | | MRE | 0.5734 | 0.5100 | 0.5087 | 0.8962 | 0.2287 | 0.1669 | | | | | | | | | | | | HOTEL-M | Easy-impute | MAE | 8.8847 | 2.6327 | 3.9033 | 3.2133 | 7.4037 | 2.1930 | | | | MSE | 91.5550 | 13.5993| 23.1058| 20.0857| 124.5438 | 8.7460 | | | | RMSE | 9.5684 | 3.6877 | 4.8068 | 4.4817 | 11.1599 | 2.9574 | | | | MRE | 2.9468 | 0.8732 | 1.2946 | 1.0658 | 2.4556 | 0.7274 | | HOTEL-M | Hard-impute | MAE | 8.9096 | 7.8833 | 7.6057 | 3.2443 | 7.9484 | 2.6050 | | | | MSE | 92.2607 | 75.9804| 72.0169| 20.2543| 106.7010 | 16.0168| | | | RMSE | 9.6478 | 8.7167 | 8.4863 | 4.5005 | 11.3296 | 4.0021 | | | | MRE | 2.8866 | 2.6127 | 2.5207 | 1.1686 | 2.6343 | 0.8634 | | | | | | | | | | | | UNIV-M | Easy-impute | MAE | 3.0410 | 0.9158 | 1.0171 | 6.8380 | 0.6713 | 0.1939 | | | | MSE | 14.0163 | 2.6297 | 2.9769 | 56.9715| 0.7631 | 0.0697 | | | | RMSE | 3.7438 | 1.6216 | 1.7254 | 7.5479 | 0.8736 | 0.2639 | | | | MRE | 0.3905 | 0.1176 | 0.1306 | 0.8780 | 0.0862 | 0.0249 | | UNIV-M | Hard-impute | MAE | 3.9795 | 1.9430 | 1.8028 | 6.9148 | 0.9421 | 0.6158 | | | | MSE | 15.4244 | 6.1815 | 5.4057 | 57.6533| 1.5827 | 0.6003 | | | | RMSE | 3.9639 | 2.4863 | 2.3250 | 7.7268 | 1.2581 | 0.7748 | | | | MRE | 1.0326 | 0.2495 | 0.2315 | 0.9751 | 0.1210 | 0.0791 | | | | | | | | | | | | ZARA1-M | Easy-impute | MAE | 2.6288 | 0.4832 | 0.7307 | 5.1152 | 0.3125 | 0.2054 | | | | MSE | 10.0109 | 0.8599 | 1.2306 | 34.9869| 0.1768 | 0.0775 | | | | RMSE | 3.1640 | 0.9273 | 1.1093 | 5.9150 | 0.4204 | 0.2784 | | | | MRE | 0.4326 | 0.0795 | 0.1202 | 0.8417 | 0.0514 | 0.0338 | |ZARA1-M | Hard-impute | MAE | 2.7532 | 2.2846 | 2.3140 | 5.1921 | 0.5699 | 0.6277 | | | | MSE | 10.1228 | 7.8216 | 8.0351 | 35.7821| 0.6327 | 0.8287 | | | | RMSE | 3.1816 | 2.7967 | 2.8346 | 5.9976 | 0.7955 | 0.9103 | | | | MRE | 0.4463 | 0.3756 | 0.3805 | 0.8673 | 0.0937 | 0.1032 | | | | | | | | | | | | ZARA2-M | Easy-impute | MAE | 2.1301 | 0.3861 | 0.5556 | 5.0905 | 0.2409 | 0.1314 | | | | MSE | 7.3276 | 0.6212 | 0.8292 | 31.5674| 0.1329 | 0.0385 | | | | RMSE | 2.7070 | 0.7882 | 0.9106 | 5.6185 | 0.3645 | 0.1963 | | | | MRE | 0.3524 | 0.0639 | 0.0919 | 0.8422 | 0.0399 | 0.0217 | | ZARA2-M | Hard-impute | MAE | 2.2840 | 1.8605 | 1.8051 | 5.1698 | 0.5031 | 0.3632 | | | | MSE | 7.6342 | 5.8511 | 5.5953 | 32.3531| 0.6525 | 0.4313 | | | | RMSE | 2.8630 | 2.4189 | 2.3654 | 5.8994 | 0.8077 | 0.6567 | | | | MRE | 0.3735 | 0.3041 | 0.2951 | 0.8465 | 0.0823 | 0.0593 |

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<p align=center> Trajectory Prediction Benchmarking <br>

Results obtained for various trajectory prediction methods on the imputed subsets of TrajImpute

We report the ADE/FDE for the trajectory prediction task on the clean, soft imputed, and hard imputed protocols. `Clean' refers to a subset with no missing coordinates. Performance degradation occurs when trajectory prediction is performed on the hard imputed subsets.

• Code for the GraphTern [Code]
• Code for the LBEBM-ET [Code]
• Code for the SGCN-ET [Code]
• Code for the EQmotion [Code]
• Code for the TUTR [Code]
• Code for the GPGraph [Code]

| Datasets | Baselines | GraphTern | LBEBM-ET | SGCN-ET | EQmotion | TUTR | GPGraph | |----------|-----------------|-----------|-----------|----------|-----------|----------|-----------| | ETH | Clean | 0.42/0.58 | 0.36/0.53 | 0.36/0.57| 0.40/0.61 | 0.40/0.61| 0.43/0.63 | | | Easy-impute | 0.77/0.74 | 0.37/0.55 | 0.42/0.71| 0.46/0.62 | 0.54/0.73| 0.45/0.75 | | | Hard-impute | 0.78/0.77 | 0.85/1.07 | 1.07/1.44| 0.47/0.63 | 1.12/1.53| 0.92/0.93 | | | | | | | | | | | Hotel | Clean | 0.14/0.23 | 0.12/0.19 | 0.13/0.21| 0.12/0.18 | 0.11/0.18| 0.18/0.30 | | | Easy-impute | 0.15/0.25 | 0.13/0.20 | 0.14/0.23| 0.65/0.68 | 1.31/1.66| 0.19/0.31 | | | Hard-impute | 1.68/1.42 | 3.31/4.13 | 3.21/3.92| 0.72/0.74 | 3.36/3.95| 1.89/1.70 | | | | | | | | | | | UNV | Clean | 0.26/0.45 | 0.24/0.43 | 0.24/0.43| 0.23/0.43 | 0.23/0.42| 0.24/0.42 | | | Easy-impute | 0.27/0.47 | 0.30/0.51 | 0.29/0.51| 0.37/0.61 | 0.31/0.49| 0.25/0.44 | | | Hard-impute | 0.50/0.51 | 0.64/1.01 | 0.77/1.21| 0.39/0.70 | 0.59/0.85| 0.53/0.50 | | | | | | | | | | | ZARA1