DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways

• URL: http://arxiv.org/abs/2108.00505v1
• Date: Sun, 1 Aug 2021 17:33:04 GMT
• Title: DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways
• Authors: Mohammadreza Baharani, Vinit Katariya, Nichole Morris, Omidreza Shoghli, Hamed Tabkhi
• Abstract summary: This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction in highways. DeepTrack achieves comparable accuracy to state-of-the-art trajectory prediction models but with smaller model sizes and lower computational complexity.
• Score: 0.47248250311484113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle trajectory prediction is an essential task for enabling many intelligent transportation systems. While there have been some promising advances in the field, there is a need for new agile algorithms with smaller model sizes and lower computational requirements. This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction in highways. In contrast to previous methods, the vehicle dynamics are encoded using Agile Temporal Convolutional Networks (ATCNs) to provide more robust time prediction with less computation. ATCN also uses depthwise convolution, which reduces the complexity of models compared to existing approaches in terms of model size and operations. Overall, our experimental results demonstrate that DeepTrack achieves comparable accuracy to state-of-the-art trajectory prediction models but with smaller model sizes and lower computational complexity, making it more suitable for real-world deployment.

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