Trajectory Prediction for Autonomous Driving Cars

Here, we propose a graph-based trajectory prediction solution for autonomous driving cars. The following figure explains our scheme at a high level.


Framework

Framework


Experimental Results

1. Datasets

We evaluate our scheme on two well-known trajectory prediction datasets:

2. Comparison Results

We consider the following baseline:

Our model predicts all observed objects simultaneously, while others only predict the future trajectory for a single traffic agent (the one in the central location) each time.

Root Mean Square Error (RMSE) for trajectory prediction on NGSIM I-80 and US-101 datasets. Data are converted into the meter unit. All results except ours are extracted from [5]. The smaller the value, the better.

Pred. 1(s) Pred. 2(s) Pred. 3(s) Pred. 4(s) Pred. 5(s)
CV 0.73 1.78 3.13 4.78 6.68
V-LSTM 0.68 1.65 2.91 4.46 6.27
C-VGMM + VIM [3] 0.66 1.56 2.75 4.24 5.99
GAIL-GRU [4] 0.69 1.51 2.55 3.65 4.71
CS-LSTM(M) [5] 0.62 1.29 2.13 3.20 4.52
CS-LSTM [5] 0.61 1.27 2.09 3.10 4.37
Ours
(Compared to CS-LSTM)
0.37
(40%↑ -0.24)
0.86
(32%↑ -0.41)
1.45
(31%↑ -0.64)
2.21
(29%↑ -0.89)
3.16
(28%↑ -1.21)

3. Visualization of Prediction Results

In the following figure, we visualize several prediction results in mild, moderate, and congested traffic conditions (from left to right).

visualized_results

PS: More details and experimental results are coming soon.


References

  1. J. Colyar and J. Halkias, “Us highway 80 dataset,” Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-07-030, 2007.
  2. J. Colyar and J. Halkias, “Us highway 101 dataset,” Federal Highway Administration (FHWA), Tech. Rep. FHWA-HRT-07-030, 2007.
  3. N. Deo, A. Rangesh, and M. M. Trivedi, “How would surround vehicles move? a unified framework for maneuver classification and motion prediction,” IEEE Transactions on Intelligent Vehicles, vol. 3, no. 2, pp. 129–140, 2018.
  4. A. Kuefler, J. Morton, T. Wheeler, and M. Kochenderfer, “Imitating driver behavior with generative adversarial networks,” in 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017, pp. 204–211.
  5. N. Deo and M. M. Trivedi, “Convolutional social pooling for vehicle trajectory prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 1468– 1476.