| 1. |
- Bellone, Mauro, 1982, et al.
(författare)
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Electrification and Automation in Maritime Applications: Employing AI Techniques for Energy Optimization and Efficiency
- 2019
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Ingår i: IEEE Electrification Magazine. - 2325-5889 .- 2325-5897. ; 7:4, s. 22-31
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The region of Västra Götaland is an area in Western Sweden that has nearly 1.6 million inhabitants. In recent years, the number of trips made by the public transportation system has increased considerably, and everything indicates that it will continue to rise substantially in the coming years. Today, the region faces challenges in managing this expected increase in demand. A description of how public transportation is developing in Västra Götaland is given in the Transport Provision Program; it estimates that the number of journeys made by public transportation will double, a goal that is sought at both the national and local levels. The number of public transportation journeys made in the country would then be roughly 400 million by 2025. In response to this, capacity is expected to increase by 70% and the travel time is projected to decrease by 20-25%. Along with these developments, efforts are being made to transition to a more environmentally friendly means of transportation through the use of alternative fuels, electrification, and an increased level of automated systems (with the additional objective of reducing the number of accidents).
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| 2. |
- Bellone, Mauro, 1982, et al.
(författare)
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Learning Traversability from Point Clouds in Challenging Scenarios
- 2018
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 19:1, s. 296-305
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Tidskriftsartikel (refereegranskat)abstract
- This paper aims at evaluating the capabilities to detect road traversability in urban and extra-urban scenarios ofsupport vector machine-based classifiers that use local descriptors extracted from point cloud data. The evaluation of the proposed classifiers is carried out by using four different kernels and comparing five point descriptors obtained from geometric and appearance-based features. A comparison among the performance of descriptors individually has demonstrated that the normal vector-based descriptor achieves an accuracy of 88%, outperforming by about 6%–15% all the other considered ones. To further improve the interpretation capabilities, the space of features is augmented by merging the components of each point descriptor, reaching 92% classification accuracy. A set of test scenarios have been acquired during an extensive experimental campaign using an all-terrain vehicle. Tests on real data show high classification performance for road scenarios and rural environments; the generality of the method makes it applicable for different types of mobile robots including, but not limited to, autonomous vehicles.
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| 3. |
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| 4. |
- Caltagirone, Luca, 1983, et al.
(författare)
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LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
- 2019
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Ingår i: Robotics and Autonomous Systems. - : Elsevier BV. - 0921-8890. ; 111, s. 125-131
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Tidskriftsartikel (refereegranskat)abstract
- In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches. To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches.
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| 5. |
- Caltagirone, Luca, 1983, et al.
(författare)
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Lidar–camera semi-supervised learning for semantic segmentation
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:14
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Tidskriftsartikel (refereegranskat)abstract
- In this work, we investigated two issues: (1) How the fusion of lidar and camera data can improve semantic segmentation performance compared with the individual sensor modalities in a supervised learning context; and (2) How fusion can also be leveraged for semi-supervised learning in order to further improve performance and to adapt to new domains without requiring any additional labelled data. A comparative study was carried out by providing an experimental evaluation on networks trained in different setups using various scenarios from sunny days to rainy night scenes. The networks were tested for challenging, and less common, scenarios where cameras or lidars individually would not provide a reliable prediction. Our results suggest that semi-supervised learning and fusion techniques increase the overall performance of the network in challenging scenarios using less data annotations.
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| 6. |
- Torabi, Sina, 1990, et al.
(författare)
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Energy minimization for an electric bus using a genetic algorithm
- 2020
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Ingår i: European Transport Research Review. - : Springer Science and Business Media LLC. - 1867-0717 .- 1866-8887. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Background and methods: This paper addresses, in simulation, energy minimization of an autonomous electric minibus operating in an urban environment. Two different case studies have been considered, each involving a total of 10 different 2?km bus routes and two different average speeds. In the proposed method, the minibus follows an optimized speed profile, generated using a genetic algorithm. Results: In the first case study the vehicle was able to reduce its energy consumption by around 7 to 12% relative to a baseline case in which it maintains a constant speed between stops, with short acceleration and deceleration phases. In the second case study, involving mass variation (passengers entering and alighting) it was demonstrated that the number of round trips that can be completed on a single battery charge is increased by around 10% using the proposed method.
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| 7. |
- Virgolin, Marco, 1989, et al.
(författare)
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A Mobile Interactive Robot for Social Distancing in Hospitals
- 2021
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Ingår i: Proceedings - 2021 5th IEEE International Conference on Robotic Computing, IRC 2021. ; , s. 87-91
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Konferensbidrag (refereegranskat)abstract
- We introduce the multimodal interactive mobile robot ISOLDE, intended for use in hospitals, with the primary aim of helping healthcare staff to maintain social distancing during pandemics, such as the ongoing Covid-19 pandemic. ISOLDE also addresses the growing concern related to the use of black box models in artificial intelligence, especially in situations involving high-stakes decisions. Thus, ISOLDE's interactive capabilities have been implemented using a fully interpretable dialogue manager, making it easy to monitor and, if needed, correct the robot's actions, even for a non-expert. A use case is presented (in a laboratory setting) in which the robot successfully interacts with healthcare staff to carry out a requested transportation and delivery task, and also measuring a patient's temperature.
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| 8. |
- Wahde, Mattias, 1969, et al.
(författare)
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A method for real-time dynamic fleet mission planning for autonomous mining
- 2019
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Ingår i: Autonomous Agents and Multi-Agent Systems. - : Springer Science and Business Media LLC. - 1573-7454 .- 1387-2532. ; 33:5, s. 564-590
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces a method for dynamic fleet mission planning for autonomous mining (in loop-free maps), in which a dynamic fleet mission is defined as a sequence of static fleet missions, each generated using a modified genetic algorithm. For the case of static fleet mission planning (where each vehicle completes just one mission), the proposed method is able to reliably generate, within a short optimization time, feasible fleet missions with short total duration and as few stops as possible. For the dynamic case, in simulations involving a realistic mine map, the proposed method is able to generate efficient dynamic plans such that the number of completed missions per vehicle is only slightly reduced as the number of vehicles is increased, demonstrating the favorable scaling properties of the method as well as its applicability in real-world cases.
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