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A destination prediction method using driving contexts and trajectory for car navigation systems

Published: 08 March 2009 Publication History

Abstract

Car navigation systems provide the best route to a destination quickly and effectively. However, during daily driving, this information is not necessary since drivers already know the route to the destination very well. In addition, it is time-consuming for drivers to input the destination. Thus, our research group has proposed a new car navigation system that provides information related to the destination by predicting the user's destination automatically. We propose the use of a new method that predicts the destination on the basis of the driving trajectory and the contexts in which the user drives. A system that uses our method knows the destination without user interaction and provides information related to the correct destination.

References

[1]
M. Kobayashi et al. The arrival place presumption mechanism applied to an information filtering system for in-vehicle navigation systems. Transactions of Information Processing Society of Japan, 45(12): 2688--2695, 2004.
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L. Liao et al. Learning and inferring transportation routines. Artif. Intell., 171(5--6): 311--331, 2007.
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D. J. Patterson et al. Inferring high-level behavior from low- level sensors. In Proc. of The Fifth International Conference on Ubiquitous Computing (UbiComp2003), pages 73--89, 2003.
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T. Terada et al. Design of a car navigation system that predicts user destination. In Proc. of Int'l Workshop on Tools and Applications for Mobile Contents (TAMC), pages 54--49, 2006.
[5]
M. Yoshioka and J. Ozawa. Destination entropy for arrival place presumption from car driving route history. Transactions of Information Processing Society of Japan, 46(12): 2973--2982, 2005.

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  • (2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
  • (2021)Vehicle Destination Prediction Using Bidirectional LSTM with Attention MechanismSensors10.3390/s2124844321:24(8443)Online publication date: 17-Dec-2021
  • (2020)“I’d like an Explanation for That!”Exploring Reactions to Unexpected Autonomous Driving22nd International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3379503.3403554(1-11)Online publication date: 5-Oct-2020
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cover image ACM Conferences
SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing
March 2009
2347 pages
ISBN:9781605581668
DOI:10.1145/1529282
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2009

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Author Tags

  1. car navigation system
  2. destination prediction

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  • Research-article

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SAC09
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SAC09: The 2009 ACM Symposium on Applied Computing
March 8, 2009 - March 12, 2008
Hawaii, Honolulu

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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Cited By

View all
  • (2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
  • (2021)Vehicle Destination Prediction Using Bidirectional LSTM with Attention MechanismSensors10.3390/s2124844321:24(8443)Online publication date: 17-Dec-2021
  • (2020)“I’d like an Explanation for That!”Exploring Reactions to Unexpected Autonomous Driving22nd International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3379503.3403554(1-11)Online publication date: 5-Oct-2020
  • (2020)Good or Mediocre? A Deep Reinforcement Learning Approach for Taxi Revenue Efficiency OptimizationIEEE Transactions on Network Science and Engineering10.1109/TNSE.2020.30098557:4(3018-3027)Online publication date: 1-Oct-2020
  • (2019)Proactive car navigationAdjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers10.1145/3341162.3343757(292-295)Online publication date: 9-Sep-2019
  • (2019)TDPProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331368(1177-1180)Online publication date: 18-Jul-2019
  • (2019)Forecasting Gathering Events through Trajectory Destination Prediction: a Dynamic Hybrid ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2937082(1-1)Online publication date: 2019
  • (2019)Destination Prediction A Deep Learning based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2932984(1-1)Online publication date: 2019
  • (2019)A Two-Stage Destination Prediction Framework of Shared Bicycles Based on Geographical Position RecommendationIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2018.288451711:1(42-47)Online publication date: Sep-2020
  • (2018)On Prediction of User Destination by Sub-Trajectory UnderstandingProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271708(1413-1422)Online publication date: 17-Oct-2018
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