| 1. |
- Cebecauer, Matej, et al.
(författare)
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Spatio-Temporal Partitioning of Large Urban Networks for Travel Time Prediction
- 2018
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Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 1390-1395
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Konferensbidrag (refereegranskat)abstract
- The paper explores the potential of spatiotemporal network partitioning for travel time prediction accuracy and computational costs in the context of large-scale urban road networks (including motorways/freeways, arterials and urban streets). Forecasting in this context is challenging due to the complexity, heterogeneity, noisy data, unexpected events and the size of the traffic network. The proposed spatio-temporal network partitioning methodology is versatile, and can be applied for any source of travel time data and multivariate travel time prediction method. A case study of Stockholm, Sweden considers a network exceeding 11,000 links and uses taxi probe data as the source of travel times data. To predict the travel times the Probabilistic Principal Component Analysis (PPCA) is used. Results show that the spatio-temporal network partitioning provides a more appropriate bias-variance tradeoff, and that prediction accuracy and computational costs are improved by considering the proper number of clusters towards robust large-scale travel time prediction.
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| 2. |
- Chugh, Tushar, 1989, et al.
(författare)
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Design of Haptic Feedback Control for Steer-by-Wire
- 2018
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728103235 ; 21, s. 1737-1744
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Konferensbidrag (refereegranskat)abstract
- This paper illustrates a comparison of different haptic feedback control strategies; primarily focusing on open and closed-loop methods for a Force-Feedback Steer-by-Wire system. Due to shortcomings caused by the feedback motor impedance in the open loop architecture, the tracking performance is deteriorated. Consequently it is shown that the closed-loop solutions provide an improved response within the desired steering excitation range. The closed-loop possibilities, torque and position control, are designed and objectively compared in terms of performance and stability. The controller objectives are inertia compensation and reference tracking. For a given reference, the stability constraint between the controller gains responsible for the two objectives is contrasting in both the methods. Higher bandwidth is achieved for torque controller, whereas the driver arm inertia limits the position control performance. The linear system analysis is supported by the experimental results.
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| 3. |
- Čičić, Mladen, 1991-, et al.
(författare)
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Traffic regulation via individually controlled automated vehicles : a cell transmission model approach
- 2018
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Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 766-771
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Konferensbidrag (refereegranskat)abstract
- The advent of automated, infrastructure-controlled vehicles offers new opportunities for traffic control. Even when the number of controlled vehicles is small, they can significantly affect the surrounding traffic. One way of regulating traffic is by using the automated vehicles as controlled moving bottlenecks. We present an extension of the cell transmission model that includes the influence of moving bottlenecks, consistently with the corresponding PDE traffic model. Based on this model, a control strategy is derived for traffic jam resolution. The strategy is tested in simulations, and shown to reduce the average travel time of surrounding vehicles, while also helping dissipate the traffic jam faster and ensuring the controlled vehicle avoids it.
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| 4. |
- Jenelius, Erik, 1980-
(författare)
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Car-Specific Metro Train Crowding Prediction Based on Real-Time Load Data
- 2018
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Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 78-83
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Konferensbidrag (refereegranskat)abstract
- The paper formulates the car-specific metro train crowding prediction problem based on real-time load data and evaluates the performance of several prediction methods (stepwise regression, lasso, and boosted tree ensembles). The problem is studied for multiple stations along a metro line in Stockholm, Sweden. Prediction accuracy is evaluated with respect to absolute passenger loads and predefined discrete crowding levels. When available, predictions with real-time load data significantly outperform historical averages, with accuracy improvements varying in magnitude across target stations and prediction horizons.
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| 5. |
- Johansson, Alexander, et al.
(författare)
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Multi-Fleet Platoon Matching : A Game-Theoretic Approach
- 2018
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Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 2980-2985
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Konferensbidrag (refereegranskat)abstract
- We consider the platoon matching problem for a set of trucks with the same origin, but different destinations. It is assumed that the vehicles benefit from traveling in a platoon for instance through reduced fuel consumption. The vehicles belong to different fleet owners and their strategic interaction is modeled as a non-cooperative game where the vehicle actions are their departure times. Each truck has a preferred departure time and its utility function is defined as the difference between its benefit from platooning and the cost of deviating from its preferred departure time. We show that the platoon matching game is an exact potential game. An algorithm based on best response dynamics is proposed for finding a Nash equilibrium of the game. At a Nash equilibrium, vehicles with the same departure time are matched to form a platoon. Finally, the total fuel reduction at the Nash equilibrium is studied and compared with that of a cooperative matching solution where a common utility function for all vehicles is optimized.
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| 6. |
- Lima, Pedro F., 1990-, et al.
(författare)
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Progress Maximization Model Predictive Controller
- 2018
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Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 1075-1082
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of progress maximization (i.e., traveling time minimization) along a given path for autonomous vehicles. Progress maximization plays an important role not only in racing, but also in efficient and safe autonomous driving applications. The progress maximization problem is formulated as a model predictive controller, where the vehicle model is successively linearized at each time step, yielding a convex optimization problem. To ensure real-time feasibility, a kinematic vehicle model is used together with several linear approximations of the vehicle dynamics constraints. We propose a novel polytopic approximation of the 'g-g' diagram, which models the vehicle handling limits by constraining the lateral and longitudinal acceleration. Moreover, the tire slip angles are restricted to ensure that the tires of the vehicle always operate in their linear force region by limiting the lateral acceleration. We illustrate the effectiveness of the proposed controller in simulation, where a nonlinear dynamic vehicle model is controlled to maximize the progress along a track, taking into consideration possible obstacles.
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| 7. |
- Martinsson, John, et al.
(författare)
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Clustering Vehicle Maneuver Trajectories Using Mixtures of Hidden Markov Models
- 2018
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Ingår i: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 2153-0017. - 9781728103235 ; 2018-November, s. 3698-3705
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Konferensbidrag (refereegranskat)abstract
- The safety of autonomous vehicles needs to be verified and validated by rigorous testing. It is expensive to test autonomous vehicles in the field, and therefore virtual testing methods are needed. Generative models of maneuvers such as cut-ins, overtakes, and lane-keeping are needed to thoroughly test the autonomous vehicle in a virtual environment. To train such models we need ground truth maneuver labels and obtaining such labels can be time-consuming and costly. In this work, we use a mixture of hidden Markov models to find clusters in maneuver trajectories, which can be used to speed up the labeling process. The maneuver trajectories are noisy, asynchronous and of uneven length, which make hidden Markov models a good fit for the data. The method is evaluated on labeled data from a test track consisting of cut-ins and overtakes with favorable results. Further, it is applied to natural data where many of the clusters found can be interpreted as driver maneuvers under reasonable assumptions. We show that mixtures of hidden Markov models can be used to find motion patterns in driver maneuver data from highways and country roads.
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