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2. Conclusion

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
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9. See Through System

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
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17. Background of IoV

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
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18. State of the Art

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
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19. Future Directions

Author

Liu, Kai, Dai, Penglin, Lee, Victor C. S., Ng, Joseph Kee-Yin, Son, Sang Hyuk, Liu, Kai, Dai, Penglin, Lee, Victor C.S., Ng, Joseph Kee-Yin, and Son, Sang Hyuk

Published
2024
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22. State of the Art

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
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24. Conclusion

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
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25. See Through System

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
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29. Background of IoV

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
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34. Future Directions

Author

Liu, Kai, primary, Dai, Penglin, additional, Lee, Victor C. S., additional, Ng, Joseph Kee-Yin, additional, and Son, Sang Hyuk, additional

Published
2023
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39. Online Ride-Hitching in UAV Travelling

Author

Li, Songhua, Li, Minming, Duan, Lingjie, and Lee, Victor C. S.

Subjects
Computer Science - Data Structures and Algorithms
Abstract

The unmanned aerial vehicle (UAV) has emerged as a promising solution to provide delivery and other mobile services to customers rapidly, yet it drains its stored energy quickly when travelling on the way and (even if solar-powered) it takes time for charging power on the way before reaching the destination. To address this issue, existing works focus more on UAV's path planning with designated system vehicles providing charging service. However, in some emergency cases and rural areas where system vehicles are not available, public trucks can provide more feasible and cost-saving services and hence a silver lining. In this paper, we explore how a single UAV can save flying distance by exploiting public trucks, to minimize the travel time of the UAV. We give the first theoretical work studying online algorithms for the problem, which guarantees a worst-case performance. We first consider the offline problem knowing future truck trip information far ahead of time. By delicately transforming the problem into a graph satisfying both time and power constraints, we present a shortest-path algorithm that outputs the optimal solution of the problem. Then, we proceed to the online setting where trucks appear in real-time and only inform the UAV of their trip information some certain time $\Delta t$ beforehand. As a benchmark, we propose a well-constructed lower bound that an online algorithm could achieve. We propose an online algorithm MyopicHitching that greedily takes truck trips and an improved algorithm $\Delta t$-Adaptive that further tolerates a waiting time in taking a ride. Our theoretical analysis shows that $\Delta t$-Adaptive is asymptotically optimal in the sense that its ratio approaches the proposed lower bounds as $\Delta t$ increases., Comment: A preliminary version of this paper is to appear at COCOON 2021

Published
2021

40. Online Maximum $k$-Interval Coverage Problem

Author

Li, Songhua, Li, Minming, Duan, Lingjie, and Lee, Victor C. S.

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Discrete Mathematics
Abstract

We study the online maximum coverage problem on a line, in which, given an online sequence of sub-intervals (which may intersect among each other) of a target large interval and an integer $k$, we aim to select at most $k$ of the sub-intervals such that the total covered length of the target interval is maximized. The decision to accept or reject each sub-interval is made immediately and irrevocably (no preemption) right at the release timestamp of the sub-interval. We comprehensively study different settings of this problem regarding both the length of a released sub-interval and the total number of released sub-intervals. We first present lower bounds on the competitive ratio for the settings concerned in this paper, respectively. For the offline problem where the sequence of all the released sub-intervals is known in advance to the decision-maker, we propose a dynamic-programming-based optimal approach as the benchmark. For the online problem, we first propose a single-threshold-based deterministic algorithm SOA by adding a sub-interval if the added length exceeds a certain threshold, achieving competitive ratios close to the lower bounds, respectively. Then, we extend to a double-thresholds-based algorithm DOA, by using the first threshold for exploration and the second threshold (larger than the first one) for exploitation. With the two thresholds solved by our proposed program, we show that DOA improves SOA in the worst-case performance. Moreover, we prove that a deterministic algorithm that accepts sub-intervals by multi non-increasing thresholds cannot outperform even SOA., Comment: An extended abstract of this full version is to appear in COCOA 2020

Published
2020

42. Trip-Vehicle Assignment Algorithms for Ride-Sharing

Author

Li, Songhua, Li, Minming, Lee, Victor C. S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wu, Weili, editor, and Zhang, Zhongnan, editor

Published
2020
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43. Online Maximum k-Interval Coverage Problem

Author

Li, Songhua, Li, Minming, Duan, Lingjie, Lee, Victor C. S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wu, Weili, editor, and Zhang, Zhongnan, editor

Published
2020
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44. Car-Sharing: Online Scheduling k Cars Between Two Locations

Author

Li, Songhua, Zheng, Leqian, Lee, Victor C. S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Li, Minming, editor

Published
2020
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50. A Cooperative Vehicle Localization and Trajectory Prediction Framework Based on Belief Propagation and Transformer Model

Author

Jin, Feiyu, Liu, Kai, Liu, Chunhui, Cheng, Tongtong, Zhang, Hao, and Lee, Victor C. S.

Abstract

The advancement of sensing, transmission, and computation technologies has transformed modern vehicles into ubiquitous consumer electronics. This paper presents a cooperative vehicle localization and trajectory prediction framework for enabling Intelligent Transportation Systems (ITS) applications. Specifically, the proposed framework consists of a Belief Propagation based Location Approximation (BPLA) algorithm for cooperative vehicle localization and a Transformer-based Vehicle trajectory prediction model called VFormer. The BPLA algorithm first establishes a factor graph based on the sensor measurements transmitted by vehicles, and then adopts a modified belief propagation procedure to approximate the posterior distribution of vehicles. On this basis, VFormer extracts the hidden features from historical positions estimated by BPLA and vehicle motion data to model long-term and short-term motion patterns of vehicles. Moreover, the multi-head attention layer in the VFormer is utilized to learn the spatial-temporal dependencies between the target vehicle and its surrounding vehicles at different timestamps to improve prediction accuracy. A comprehensive performance evaluation has been conducted based on the public vehicle trajectory dataset and the real-world system prototype. Experiment results demonstrate the superiority of the proposed framework on improving vehicle localization and trajectory prediction performance.

Published
2024
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