| 1. |
- Ahrnbom, Martin, et al.
(författare)
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Generalized Urban Traffic Surveillance (GUTS) : World-Coordinate Tracking for Traffic Safety Applications
- 2022
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Ingår i: 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022. - 9781665468800 ; 2022-October, s. 3813-3818
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Konferensbidrag (refereegranskat)abstract
- We present a new world-coordinate tracking algorithm for road users seen from static surveillance cameras, denoted GUTS. It is based upon the previously published UTS method but simplifies and replaces parts allowing association logic to work in world coordinates, by using a novel convolutional neural network denoted SAMHNet to convert every detection into world coordinates. Experimental evaluation on synthetic data shows a MOTA increase of 41 % or 153% depending on distance metric, compared to UTS. Furthermore, the system is verified to work on a real-world recording. We further introduce a synthetic dataset denoted UTOCS which is the first of its kind to be standardized and made publicly available, allowing fair comparison between methods.
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| 2. |
- Chen, Lei, et al.
(författare)
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System of Systems for emergency response : the case with CAVs on highways
- 2022
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. Volume 2022-October, 2022, Pages 839-844. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665468800 ; , s. 839-844
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Konferensbidrag (refereegranskat)abstract
- Emergency response system is a complex system of systems (SoS). The introduction of connected and autonomous vehicles (CAVs) introduces an extra dimension into the complexity. Future emergency response must be able to take into account of the autonomous vehicles with different automation levels and leverage the increasing connectivity and automation for efficient emergency response. Architecture frameworks have long been used for system engineering for large complex systems. The emerging unified architecture framework converges previous architecture frameworks for a unified one towards both military and civilian use. Based on the scenario of emergency response with CAVs on highways, this paper motivates an enterprise architecture for emergency response system of systems (ERSoS) with identification of the key challenges and opportunities in addition to a proposal of required capabilities. The work is a first iteration of an enterprise architecture for ERSoS with CAVs and forms part of the overall ERSoS architecture development process.
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| 3. |
- Tiong, KahYong, et al.
(författare)
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Real-time Train Arrival Time Prediction at Multiple Stations and Arbitrary Times
- 2022
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Ingår i: 2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). ; 2022-October, s. 793-798
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Konferensbidrag (refereegranskat)abstract
- Real-time prediction of train arrivals is important for proactive traffic control and information provision in passenger rails. Despite many studies in predicting arrival times or delays at stations, they are essentially the next-step time series prediction problem which may limit their applications in practice. For example, passengers on the trains or waiting on platforms may have different destinations and need the predicted train arrival times for any downstream stations rather than only the next station. The paper aims to formulate a real-time train arrival times prediction problem at multiple stations and arbitrary times. We develop multi-output machine learning models and systematically evaluate their performance using train operation data in Sweden. The direct multi-output regression models with different regression functions are tested, including LightGBM, linear regression, random forest regression, and gradient boosting regression models. The hyperparameters are optimized using random grid search and five-fold cross-validation methods. The results show that the Direct Multi-Output LightGBM significantly outperformed other models in terms of accuracy. The predictions at downstream stations improve as the train moves along given more real-time information is observed.
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