Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385657669> ?p ?o ?g. }
- W4385657669 endingPage "104272" @default.
- W4385657669 startingPage "104272" @default.
- W4385657669 abstract "Autonomous Vehicles (AVs) have made remarkable developments and are anticipated to replace human drivers. In transitioning from human-driven vehicles to fully AVs, one crucial task is to predict the trajectories of the subject vehicle and its surrounding vehicles in real time. Most existing methods of vehicle trajectory prediction on highways are based on physical models or purely data-driven models. However, they either yield unsatisfactory prediction performance or lack model interpretability and physical implications. This paper proposes a Physics-Informed Deep Learning framework that fully leverages the advantages of data-driven and physics-based models to go beyond the existing models. We use the Transformer neural network architecture with self-attention as Physics-Uninformed Neural Network (PUNN) and Intelligent Driver Model (IDM) as physical model to construct of Physics-Informed Transformer-Intelligent Driver Model (PIT-IDM). Extensive experiments have been conducted on two datasets with different traffic environments, i.e., Next Generation SIMulation (NGSIM) data in the US and the Ubiquitous Traffic Eyes (UTE) data in China, to verify model accuracy and efficiency. Compared with the three kinds of baselines by relative and absolute measures of effectiveness, the best performing PIT-IDM reduces longitudinal trajectory prediction errors for long horizons by 5%-50%, some even reduced up to 70%. Extensive empirical analyses have been carried out to verify its excellent spatio-temporal transferability and explore the physics-informed mechanism underlying this deep learning method. The training and inference time analysis indicates that although it takes longer to train PIT-IDM, it requires fewer calls and accumulates fewer errors with less computation time in real-world applications. The overall results further validate the efficacy of this Physics-Informed Deep Learning framework in enhancing model accuracy, interpretability, and transferability." @default.
- W4385657669 created "2023-08-09" @default.
- W4385657669 creator A5028513168 @default.
- W4385657669 creator A5030285078 @default.
- W4385657669 creator A5031896665 @default.
- W4385657669 creator A5063040451 @default.
- W4385657669 date "2023-09-01" @default.
- W4385657669 modified "2023-10-17" @default.
- W4385657669 title "A physics-informed Transformer model for vehicle trajectory prediction on highways" @default.
- W4385657669 cites W1965455100 @default.
- W4385657669 cites W1968719911 @default.
- W4385657669 cites W2016211524 @default.
- W4385657669 cites W2058570191 @default.
- W4385657669 cites W2089080831 @default.
- W4385657669 cites W2157331557 @default.
- W4385657669 cites W2344057403 @default.
- W4385657669 cites W2734024016 @default.
- W4385657669 cites W2755552418 @default.
- W4385657669 cites W2761568138 @default.
- W4385657669 cites W2784715585 @default.
- W4385657669 cites W2899283552 @default.
- W4385657669 cites W2920959147 @default.
- W4385657669 cites W2946476763 @default.
- W4385657669 cites W2963906196 @default.
- W4385657669 cites W2963945905 @default.
- W4385657669 cites W2966922041 @default.
- W4385657669 cites W2997848713 @default.
- W4385657669 cites W3008118574 @default.
- W4385657669 cites W3027664001 @default.
- W4385657669 cites W3035054225 @default.
- W4385657669 cites W3037376004 @default.
- W4385657669 cites W3083846300 @default.
- W4385657669 cites W3096831136 @default.
- W4385657669 cites W3114753236 @default.
- W4385657669 cites W3117049966 @default.
- W4385657669 cites W3117223116 @default.
- W4385657669 cites W3125437963 @default.
- W4385657669 cites W3155186850 @default.
- W4385657669 cites W3160050461 @default.
- W4385657669 cites W3169575318 @default.
- W4385657669 cites W3189632276 @default.
- W4385657669 cites W3196871031 @default.
- W4385657669 cites W4380318362 @default.
- W4385657669 doi "https://doi.org/10.1016/j.trc.2023.104272" @default.
- W4385657669 hasPublicationYear "2023" @default.
- W4385657669 type Work @default.
- W4385657669 citedByCount "0" @default.
- W4385657669 crossrefType "journal-article" @default.
- W4385657669 hasAuthorship W4385657669A5028513168 @default.
- W4385657669 hasAuthorship W4385657669A5030285078 @default.
- W4385657669 hasAuthorship W4385657669A5031896665 @default.
- W4385657669 hasAuthorship W4385657669A5063040451 @default.
- W4385657669 hasConcept C108583219 @default.
- W4385657669 hasConcept C119599485 @default.
- W4385657669 hasConcept C119857082 @default.
- W4385657669 hasConcept C121332964 @default.
- W4385657669 hasConcept C127413603 @default.
- W4385657669 hasConcept C1276947 @default.
- W4385657669 hasConcept C13662910 @default.
- W4385657669 hasConcept C154945302 @default.
- W4385657669 hasConcept C165801399 @default.
- W4385657669 hasConcept C2776214188 @default.
- W4385657669 hasConcept C2781067378 @default.
- W4385657669 hasConcept C41008148 @default.
- W4385657669 hasConcept C44154836 @default.
- W4385657669 hasConcept C50644808 @default.
- W4385657669 hasConcept C66322947 @default.
- W4385657669 hasConceptScore W4385657669C108583219 @default.
- W4385657669 hasConceptScore W4385657669C119599485 @default.
- W4385657669 hasConceptScore W4385657669C119857082 @default.
- W4385657669 hasConceptScore W4385657669C121332964 @default.
- W4385657669 hasConceptScore W4385657669C127413603 @default.
- W4385657669 hasConceptScore W4385657669C1276947 @default.
- W4385657669 hasConceptScore W4385657669C13662910 @default.
- W4385657669 hasConceptScore W4385657669C154945302 @default.
- W4385657669 hasConceptScore W4385657669C165801399 @default.
- W4385657669 hasConceptScore W4385657669C2776214188 @default.
- W4385657669 hasConceptScore W4385657669C2781067378 @default.
- W4385657669 hasConceptScore W4385657669C41008148 @default.
- W4385657669 hasConceptScore W4385657669C44154836 @default.
- W4385657669 hasConceptScore W4385657669C50644808 @default.
- W4385657669 hasConceptScore W4385657669C66322947 @default.
- W4385657669 hasFunder F4320321001 @default.
- W4385657669 hasFunder F4320322866 @default.
- W4385657669 hasFunder F4320335777 @default.
- W4385657669 hasFunder F4320336026 @default.
- W4385657669 hasFunder F4320338464 @default.
- W4385657669 hasLocation W43856576691 @default.
- W4385657669 hasOpenAccess W4385657669 @default.
- W4385657669 hasPrimaryLocation W43856576691 @default.
- W4385657669 hasRelatedWork W2806259446 @default.
- W4385657669 hasRelatedWork W2905433371 @default.
- W4385657669 hasRelatedWork W3094491777 @default.
- W4385657669 hasRelatedWork W3214715529 @default.
- W4385657669 hasRelatedWork W4206178588 @default.
- W4385657669 hasRelatedWork W4287635093 @default.
- W4385657669 hasRelatedWork W4310278675 @default.
- W4385657669 hasRelatedWork W4311431240 @default.