Matches in SemOpenAlex for { <https://semopenalex.org/work/W2970201749> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W2970201749 abstract "Preceding target vehicle (PTV) motion recognition play a pivotal role in autonomous vehicles. Motion states such as yaw rate, longitudinal and lateral velocity are critical for ego vehicle decision-making and control. However, lateral states of a PTV can hardly be measured directly by common onboard sensors and the PTV lateral state estimation has been seldom addressed in existing literatures. In this paper, a novel estimation scheme based on multiple neural network ensemble is proposed for PTV lateral state estimation. First, PTV lateral kinematics is presented based on vehicle-road relationship and a novel PTV lateral motion model is constructed to interpret the PTV lateral motion. Then, neural network observer with the PTV lateral kinematics as prior knowledge is designed and training data are collected in simulation environment. The neural network observer is trained using Levenberg-Marquardt backpropagation with Bayesian regularization (LMBR) to improve the generalization capability. Finally, to further improve the performance of the neural network estimation method, multiple neural network observers are integrated by weighted averaging strategy. The effectiveness of proposed approach is verified through hardware-in-the-Ioop (HiL) experiments conducted in designed verification scenarios, and compared with model-based method and other three learning methods. The experiment results reveal that the proposed method outperforms other typical methods and achieves accurate estimation of the PTV lateral states." @default.
- W2970201749 created "2019-09-05" @default.
- W2970201749 creator A5001505881 @default.
- W2970201749 creator A5017489989 @default.
- W2970201749 creator A5045085749 @default.
- W2970201749 creator A5055420452 @default.
- W2970201749 creator A5066425232 @default.
- W2970201749 creator A5089229323 @default.
- W2970201749 date "2019-06-01" @default.
- W2970201749 modified "2023-09-24" @default.
- W2970201749 title "Lateral State Estimation of Preceding Target Vehicle Based on Multiple Neural Network Ensemble" @default.
- W2970201749 cites W1520813427 @default.
- W2970201749 cites W1565010366 @default.
- W2970201749 cites W1969484013 @default.
- W2970201749 cites W2045531847 @default.
- W2970201749 cites W2068752967 @default.
- W2970201749 cites W2107093743 @default.
- W2970201749 cites W2108938091 @default.
- W2970201749 cites W2155482699 @default.
- W2970201749 cites W2344396459 @default.
- W2970201749 cites W2397349486 @default.
- W2970201749 cites W2585528949 @default.
- W2970201749 cites W2769912520 @default.
- W2970201749 cites W2800703096 @default.
- W2970201749 cites W2963489164 @default.
- W2970201749 doi "https://doi.org/10.1109/ivs.2019.8814232" @default.
- W2970201749 hasPublicationYear "2019" @default.
- W2970201749 type Work @default.
- W2970201749 sameAs 2970201749 @default.
- W2970201749 citedByCount "2" @default.
- W2970201749 countsByYear W29702017492020 @default.
- W2970201749 countsByYear W29702017492022 @default.
- W2970201749 crossrefType "proceedings-article" @default.
- W2970201749 hasAuthorship W2970201749A5001505881 @default.
- W2970201749 hasAuthorship W2970201749A5017489989 @default.
- W2970201749 hasAuthorship W2970201749A5045085749 @default.
- W2970201749 hasAuthorship W2970201749A5055420452 @default.
- W2970201749 hasAuthorship W2970201749A5066425232 @default.
- W2970201749 hasAuthorship W2970201749A5089229323 @default.
- W2970201749 hasConcept C121332964 @default.
- W2970201749 hasConcept C127413603 @default.
- W2970201749 hasConcept C154945302 @default.
- W2970201749 hasConcept C171146098 @default.
- W2970201749 hasConcept C206831581 @default.
- W2970201749 hasConcept C2775924081 @default.
- W2970201749 hasConcept C39920418 @default.
- W2970201749 hasConcept C41008148 @default.
- W2970201749 hasConcept C47446073 @default.
- W2970201749 hasConcept C50644808 @default.
- W2970201749 hasConcept C74650414 @default.
- W2970201749 hasConceptScore W2970201749C121332964 @default.
- W2970201749 hasConceptScore W2970201749C127413603 @default.
- W2970201749 hasConceptScore W2970201749C154945302 @default.
- W2970201749 hasConceptScore W2970201749C171146098 @default.
- W2970201749 hasConceptScore W2970201749C206831581 @default.
- W2970201749 hasConceptScore W2970201749C2775924081 @default.
- W2970201749 hasConceptScore W2970201749C39920418 @default.
- W2970201749 hasConceptScore W2970201749C41008148 @default.
- W2970201749 hasConceptScore W2970201749C47446073 @default.
- W2970201749 hasConceptScore W2970201749C50644808 @default.
- W2970201749 hasConceptScore W2970201749C74650414 @default.
- W2970201749 hasLocation W29702017491 @default.
- W2970201749 hasOpenAccess W2970201749 @default.
- W2970201749 hasPrimaryLocation W29702017491 @default.
- W2970201749 hasRelatedWork W2025619264 @default.
- W2970201749 hasRelatedWork W211394977 @default.
- W2970201749 hasRelatedWork W2115939623 @default.
- W2970201749 hasRelatedWork W2135870897 @default.
- W2970201749 hasRelatedWork W2350505018 @default.
- W2970201749 hasRelatedWork W2354440638 @default.
- W2970201749 hasRelatedWork W2471592402 @default.
- W2970201749 hasRelatedWork W2809234301 @default.
- W2970201749 hasRelatedWork W2895170916 @default.
- W2970201749 hasRelatedWork W2911485153 @default.
- W2970201749 hasRelatedWork W2978744705 @default.
- W2970201749 hasRelatedWork W2982890463 @default.
- W2970201749 hasRelatedWork W3013141475 @default.
- W2970201749 hasRelatedWork W3028104568 @default.
- W2970201749 hasRelatedWork W3091668501 @default.
- W2970201749 hasRelatedWork W3101552969 @default.
- W2970201749 hasRelatedWork W3105554217 @default.
- W2970201749 hasRelatedWork W3175531468 @default.
- W2970201749 hasRelatedWork W3177039037 @default.
- W2970201749 hasRelatedWork W3195621212 @default.
- W2970201749 isParatext "false" @default.
- W2970201749 isRetracted "false" @default.
- W2970201749 magId "2970201749" @default.
- W2970201749 workType "article" @default.