Rao, N and Sundaram, S (2021) Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network. In: 2021 International Joint Conference on Neural Networks, 18 - 22 July 2021, Shenzhen.
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Abstract
Prognostication of vehicle trajectories in unknown environments is intrinsically a challenging and difficult problem to solve. The behavior of such vehicles is highly influenced by surrounding traffic, road conditions, and rogue participants present in the environment. Moreover, the presence of pedestrians, traffic lights, stop signs, etc., makes it much harder to infer the behavior of various traffic agents. This paper attempts to solve the problem of spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network. The Memory Neuron Network (MNN) attempts to capture the input-output relationship between the past positions and the future positions of the traffic agents. The proposed prediction model is computationally less intensive and has a simple architecture as compared to other deep learning models that utilize LSTMs and GRUs. It is then evaluated on the publicly available NGSIM dataset and its performance is compared with several state-of-art algorithms. Additionally, the performance is also evaluated on a custom synthetic dataset generated from the CARLA simulator. It is seen that the proposed model outperforms the existing state-of-art algorithms. Finally, the model is integrated with the CARLA simulator to test its robustness in real-time traffic scenarios.
| Item Type: | Conference Paper |
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| Publication: | Proceedings of the International Joint Conference on Neural Networks |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Forecasting; Traffic signs; Trajectories; Vehicles, Memory neuron networks; Neural-networks; Pedestrian traffic; Performance; Road condition; Spatio-temporal; Traffic agents; Trajectory prediction; Unknown environments; Vehicle trajectories, Recurrent neural networks |
| Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 16 May 2023 09:18 |
| Last Modified: | 16 May 2023 09:18 |
| URI: | https://eprints.iisc.ac.in/id/eprint/81658 |
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