| 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. ; 4:1, 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. |
- Benderius, Ola, 1985, et al.
(författare)
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A simulation environment for analysis and optimization of driver models
- 2011
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642217982 ; 6777, s. 453-462
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Konferensbidrag (refereegranskat)abstract
- A simulation environment for evaluation and optimization of driver models is introduced and described. The simulation environment features models of vehicles and drivers, as well as a representation of the traffic environment (roads, buildings etc.). In addition, an optimization framework based on stochastic optimization algorithms has been implemented as an integral part of the simulation environment. Given observed (time series) data of driver behavior and, possibly, vehicle dynamics, the optimization framework can be used for inferring driver model parameters. The simulation environment has been evaluated in two scenarios, one involving emergency braking and one involving a double lane change.
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| 4. |
- Benderius, Ola, 1985, et al.
(författare)
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Driver behaviour in unexpected critical events and in repeated exposures – a comparison
- 2014
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Ingår i: European Transport Research Review. - : Springer Science and Business Media LLC. - 1867-0717 .- 1866-8887. ; 6:1, s. 51-60
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Tidskriftsartikel (refereegranskat)abstract
- PurposeThis paper aims to determine how truck driver steering behaviour seen in repeated exposures to acritical event correlates to the behaviour resulting from an unexpected exposure to the same event.MethodsTest subjects were exposed to an unexpected critical event in a high-fidelity driving simulator. Next, a slightly modified version of the scenario was repeated several times for each subject. The driver behaviour was then analysed using standard statistical tests.ResultsIt was found that, in general, drivers keep most of their steering behaviour characteristics between test settings (unexpected and repeated). This is particularly interesting sincea similar kind of behaviour preservation is generally not found in the case of braking behaviour. In fact, onlyone significant difference was found between the two test settings, namely regarding time-to-collision at steering initiation.ConclusionsIn experiments involving both an unexpected event and several repeated events one can,at least in some cases, design the repeated event such that behavioural data collected from that setting can beused along with data from the unexpected setting. Using this procedure, one can significantly increase the amount of collected data, something that can strongly benefit, for example, driver modelling.
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| 5. |
- Caltagirone, Luca, 1983, et al.
(författare)
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Fast LIDAR-based road detection using fully convolutional neural networks
- 2017
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. ; , s. 1019-1024
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Konferensbidrag (refereegranskat)abstract
- In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-Time applications.
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| 6. |
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| 7. |
- 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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| 8. |
- 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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| 9. |
- Caltagirone, Luca, 1983, et al.
(författare)
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Truck Platooning Based on Lead Vehicle Speed Profile Optimization and Artificial Physics
- 2015
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2015. - 9781467365956 ; 2015 October, s. 394-399
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Konferensbidrag (refereegranskat)abstract
- - Several approaches to truck platooning over varying road topographies are introduced, evaluated, and compared. A simple, stochastic optimization procedure was applied to the lead vehicle speed profiles (covering sections of 10 km), resulting in average lead vehicle fuel savings of around 15.4% relative to standard cruise control. Moreover, several models involving artificial physics were evaluated for the actual platooning, i.e. for the motion of the vehicles following the lead vehicle, with the aim of minimizing the total fuel consumption of the entire platoon. One such model, based on modified artificial gravity, was found to slightly outperform a more standard approach involving adaptive cruise control, while maintaining safety and coherence of the platoon.
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| 10. |
- Carpatorea, Iulian, 1982-
(författare)
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Methods to quantify and qualify truck driver performance
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Fuel consumption is a major economical component of vehicles, particularly for heavy-duty vehicles. It is dependent on many factors, such as driver and environment, and control over some factors is present, e.g. route, and we can try to optimize others, e.g. driver. The driver is responsible for around 30% of the operational cost for the fleet operator and is therefore important to have efficient drivers as they also inuence fuel consumption which is another major cost, amounting to around 40% of vehicle operation. The difference between good and bad drivers can be substantial, depending on the environment, experience and other factors.In this thesis, two methods are proposed that aim at quantifying and qualifying driver performance of heavy duty vehicles with respect to fuel consumption. The first method, Fuel under Predefined Conditions (FPC), makes use of domain knowledge in order to incorporate effect of factors which are not measured. Due to the complexity of the vehicles, many factors cannot be quantified precisely or even measured, e.g. wind speed and direction, tire pressure. For FPC to be feasible, several assumptions need to be made regarding unmeasured variables. The effect of said unmeasured variables has to be quantified, which is done by defining specific conditions that enable their estimation. Having calculated the effect of unmeasured variables, the contribution of measured variables can be estimated. All the steps are required to be able to calculate the influence of the driver. The second method, Accelerator Pedal Position - Engine Speed (APPES) seeks to qualify driver performance irrespective of the external factors by analyzing driver intention. APPES is a 2D histogram build from the two mentioned signals. Driver performance is expressed, in this case, using features calculated from APPES.The focus of first method is to quantify fuel consumption, giving us the possibility to estimate driver performance. The second method is more skewed towards qualitative analysis allowing a better understanding of driver decisions and how they affect fuel consumption. Both methods have the ability to give transferable knowledge that can be used to improve driver's performance or automatic driving systems.Throughout the thesis and attached articles we show that both methods are able to operate within the specified conditions and achieve the set goal.
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