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.

Related papers

• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction [3.031375888004876]
  We propose a novel model named Graph Recurrent Attentive Neural Process (GRANP) for vehicle trajectory prediction. GRANP contains an encoder with deterministic and latent paths, and a decoder for prediction. We show that GRANP achieves state-of-the-art results and can efficiently quantify uncertainties.
  arXiv  Detail & Related papers  (2024-04-09T05:51:40Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety [3.3659635625913564]
  We have developed a dataset for short-term trajectory prediction tasks using the CARLA simulator. This dataset is extensive and incorporates what is considered complex scenarios - pedestrians crossing the road, vehicles overtaking. An end-to-end short-term trajectory prediction model using convolutional neural networks (CNN) and long short-term memory (LSTM) networks has also been developed.
  arXiv  Detail & Related papers  (2023-07-11T14:28:33Z)
• Human Trajectory Prediction via Neural Social Physics [63.62824628085961]
  Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. We propose a new method combining both methodologies based on a new Neural Differential Equation model. Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters.
  arXiv  Detail & Related papers  (2022-07-21T12:11:18Z)
• Bayesian Optimization and Deep Learning forsteering wheel angle prediction [58.720142291102135]
  This work aims to obtain an accurate model for the prediction of the steering angle in an automated driving system. BO was able to identify, within a limited number of trials, a model -- namely BOST-LSTM -- which resulted, the most accurate when compared to classical end-to-end driving models.
  arXiv  Detail & Related papers  (2021-10-22T15:25:14Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)
• Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network [6.065344547161387]
  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 proposed model is computationally less intensive and has a simple architecture as compared to other deep learning models that utilize LSTMs and GRUs.
  arXiv  Detail & Related papers  (2021-02-24T05:02:19Z)
• SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving [63.87272273293804]
  We propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs) Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. We present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels.
  arXiv  Detail & Related papers  (2021-02-19T11:56:44Z)
• GAKP: GRU Association and Kalman Prediction for Multiple Object Tracking [8.559199703957393]
  Multiple Object Tracking (MOT) has been a useful yet challenging task in many real-world applications such as video surveillance, intelligent retail, and smart city. We propose a novel tracking method that integrates the auto-tuning Kalman method for prediction and the Gated Recurrent Unit (GRU) and achieves a near-optimum with a small amount of training data.
  arXiv  Detail & Related papers  (2020-12-28T15:52:24Z)
• Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment [8.586891288891263]
  In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest. Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
  arXiv  Detail & Related papers  (2020-04-17T11:07:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.