Matches in SemOpenAlex for { <https://semopenalex.org/work/W2970236430> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W2970236430 abstract "Highly automated driving systems are required to make robust decisions in many complex driving environments, such as urban intersections with high traffic. In order to make as informed and safe decisions as possible, it is necessary for the system to be able to predict the future maneuvers and positions of other traffic agents, as well as to provide information about the uncertainty in the prediction to the decision making module. While Bayesian approaches are a natural way of modeling uncertainty, recently deep learning-based methods have emerged to address this need as well. However, balancing the computational and system complexity, while also taking into account agent interactions and uncertainties, remains a difficult task. The work presented in this paper proposes a method of producing predictions of other traffic agents' trajectories in intersections with a singular Deep Learning module, while incorporating uncertainty and the interactions between traffic participants. The accuracy of the generated predictions is tested on a simulated intersection with a high level of interaction between agents, and different methods of incorporating uncertainty are compared. Preliminary results show that the CVAE-based method produces qualitatively and quantitatively better measurements of uncertainty and manage to more accurately assign probability to the future occupied space of traffic agents." @default.
- W2970236430 created "2019-09-05" @default.
- W2970236430 creator A5038453402 @default.
- W2970236430 creator A5068942914 @default.
- W2970236430 date "2019-06-01" @default.
- W2970236430 modified "2023-09-23" @default.
- W2970236430 title "Incorporating Uncertainty in Predicting Vehicle Maneuvers at Intersections With Complex Interactions" @default.
- W2970236430 cites W1421062931 @default.
- W2970236430 cites W1959608418 @default.
- W2970236430 cites W2153323685 @default.
- W2970236430 cites W2964059111 @default.
- W2970236430 cites W3101609372 @default.
- W2970236430 doi "https://doi.org/10.1109/ivs.2019.8814159" @default.
- W2970236430 hasPublicationYear "2019" @default.
- W2970236430 type Work @default.
- W2970236430 sameAs 2970236430 @default.
- W2970236430 citedByCount "1" @default.
- W2970236430 countsByYear W29702364302021 @default.
- W2970236430 crossrefType "proceedings-article" @default.
- W2970236430 hasAuthorship W2970236430A5038453402 @default.
- W2970236430 hasAuthorship W2970236430A5068942914 @default.
- W2970236430 hasBestOaLocation W29702364302 @default.
- W2970236430 hasConcept C107673813 @default.
- W2970236430 hasConcept C119857082 @default.
- W2970236430 hasConcept C124101348 @default.
- W2970236430 hasConcept C127413603 @default.
- W2970236430 hasConcept C146978453 @default.
- W2970236430 hasConcept C154945302 @default.
- W2970236430 hasConcept C201995342 @default.
- W2970236430 hasConcept C2780451532 @default.
- W2970236430 hasConcept C32230216 @default.
- W2970236430 hasConcept C33724603 @default.
- W2970236430 hasConcept C41008148 @default.
- W2970236430 hasConcept C47822265 @default.
- W2970236430 hasConcept C64543145 @default.
- W2970236430 hasConceptScore W2970236430C107673813 @default.
- W2970236430 hasConceptScore W2970236430C119857082 @default.
- W2970236430 hasConceptScore W2970236430C124101348 @default.
- W2970236430 hasConceptScore W2970236430C127413603 @default.
- W2970236430 hasConceptScore W2970236430C146978453 @default.
- W2970236430 hasConceptScore W2970236430C154945302 @default.
- W2970236430 hasConceptScore W2970236430C201995342 @default.
- W2970236430 hasConceptScore W2970236430C2780451532 @default.
- W2970236430 hasConceptScore W2970236430C32230216 @default.
- W2970236430 hasConceptScore W2970236430C33724603 @default.
- W2970236430 hasConceptScore W2970236430C41008148 @default.
- W2970236430 hasConceptScore W2970236430C47822265 @default.
- W2970236430 hasConceptScore W2970236430C64543145 @default.
- W2970236430 hasLocation W29702364301 @default.
- W2970236430 hasLocation W29702364302 @default.
- W2970236430 hasOpenAccess W2970236430 @default.
- W2970236430 hasPrimaryLocation W29702364301 @default.
- W2970236430 hasRelatedWork W2777944184 @default.
- W2970236430 hasRelatedWork W2794590770 @default.
- W2970236430 hasRelatedWork W2796657115 @default.
- W2970236430 hasRelatedWork W2893907158 @default.
- W2970236430 hasRelatedWork W2895935953 @default.
- W2970236430 hasRelatedWork W2904202425 @default.
- W2970236430 hasRelatedWork W2946380658 @default.
- W2970236430 hasRelatedWork W2949805829 @default.
- W2970236430 hasRelatedWork W2962719371 @default.
- W2970236430 hasRelatedWork W2962728865 @default.
- W2970236430 hasRelatedWork W2967390659 @default.
- W2970236430 hasRelatedWork W2972853172 @default.
- W2970236430 hasRelatedWork W2975429944 @default.
- W2970236430 hasRelatedWork W2983586452 @default.
- W2970236430 hasRelatedWork W2992209396 @default.
- W2970236430 hasRelatedWork W3034955524 @default.
- W2970236430 hasRelatedWork W3105115779 @default.
- W2970236430 hasRelatedWork W3118228674 @default.
- W2970236430 hasRelatedWork W3158528237 @default.
- W2970236430 hasRelatedWork W3208007362 @default.
- W2970236430 isParatext "false" @default.
- W2970236430 isRetracted "false" @default.
- W2970236430 magId "2970236430" @default.
- W2970236430 workType "article" @default.