Journal Article10.1109/tiv.2023.3266446
Incorporating Driving Knowledge in Deep Learning Based Vehicle Trajectory Prediction: A Survey
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TL;DR: In this paper , the authors systematically investigated the research status of DL-based VTP, and summarized the application methods and application stages of driving knowledge in DL-Based VTP.
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Abstract: Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous driving. In recent years, more researchers have tried applying Deep Learning methods and techniques to VTP tasks. However, due to the black-box nature of Deep Learning, it cannot meet the interpretability and safety requirements of autonomous driving systems. Researchers have tried alleviating this problem by introducing driving knowledge in Deep Learning-based VTP. From the perspective of introducing driving knowledge, this paper systematically investigates the research status of DL-based VTP. First of all, this paper summarizes the research on VTP under three different problem formulations; secondly, this paper summarizes the application methods and application stages of driving knowledge in DL-based VTP; finally, this paper investigates and analyzes the VTP datasets and evaluation, and summarizes the knowledge contained in the datasets and its usage. Through the investigation and summary of problem formulation, knowledge usage, datasets, and evaluation of DL-based VTP, this paper analyzes the challenges and open questions of existing VTP research. It puts forward an outlook on future research directions.
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References
nuScenes: A Multimodal Dataset for Autonomous Driving
Holger Caesar,Varun Bankiti,Alex H. Lang,Sourabh Vora,Venice Erin Liong,Qiang Xu,Anush Krishnan,Yu Pan,Giancarlo Baldan,Oscar Beijbom +9 more
- 14 Jun 2020
TL;DR: nuScenes as discussed by the authors is the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alexandre Alahi,Kratarth Goel,Vignesh Ramanathan,Alexandre Robicquet,Li Fei-Fei,Silvio Savarese +5 more
- 27 Jun 2016
TL;DR: This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar,Farhad Pourpanah,Sadiq Hussain,Dana Rezazadegan,Li Liu,Mohammad Ghavamzadeh,Paul Fieguth,Xiaochun Cao,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +13 more
TL;DR: This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
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Argoverse: 3D Tracking and Forecasting With Rich Maps
Ming-Fang Chang,Deva Ramanan,James Hays,John Lambert,Patsorn Sangkloy,Jasvinder A. Singh,Slawomir Bak,Andrew Hartnett,De Wang,Peter W. Carr,Simon Lucey +10 more
- 15 Jun 2019
TL;DR: Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D tracking annotations, 300k extracted interesting vehicle trajectories, and rich semantic maps, which contain rich geometric and semantic metadata which are not currently available in any public dataset.
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Namhoon Lee,Wongun Choi,Paul Vernaza,Christopher Choy,Philip H. S. Torr,Manmohan Chandraker +5 more
- 14 Apr 2017
TL;DR: The proposed Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes significantly improves the prediction accuracy compared to other baseline methods.
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