| 1. |
- Bellone, Mauro, 1982, et al.
(författare)
-
Learning Traversability from Point Clouds in Challenging Scenarios
- 2018
-
Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 19:1, s. 296-305
-
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.
|
|
| 2. |
- Benderius, Ola, 1985, et al.
(författare)
-
A simulation environment for analysis and optimization of driver models
- 2011
-
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
-
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.
|
|
| 3. |
- Benderius, Ola, 1985, et al.
(författare)
-
Driver behaviour in unexpected critical events and in repeated exposures – a comparison
- 2014
-
Ingår i: European Transport Research Review. - : Springer Science and Business Media LLC. - 1867-0717 .- 1866-8887. ; 6:1, s. 51-60
-
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.
|
|
| 4. |
- Caltagirone, Luca, 1983, et al.
(författare)
-
Fast LIDAR-based road detection using fully convolutional neural networks
- 2017
-
Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. ; , s. 1019-1024
-
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.
|
|
| 5. |
|
|
| 6. |
- Caltagirone, Luca, 1983, et al.
(författare)
-
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
- 2019
-
Ingår i: Robotics and Autonomous Systems. - : Elsevier BV. - 0921-8890. ; 111, s. 125-131
-
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.
|
|
| 7. |
- Caltagirone, Luca, 1983, et al.
(författare)
-
Lidar–camera semi-supervised learning for semantic segmentation
- 2021
-
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:14
-
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.
|
|
| 8. |
- Caltagirone, Luca, 1983, et al.
(författare)
-
Truck Platooning Based on Lead Vehicle Speed Profile Optimization and Artificial Physics
- 2015
-
Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2015. - 9781467365956 ; 2015 October, s. 394-399
-
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.
|
|
| 9. |
|
|
| 10. |
- Hallvig, D., et al.
(författare)
-
Sleepy driving on the real road and in the simulator - A comparison
- 2013
-
Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575 .- 1879-2057. ; 50, s. 44-50
-
Tidskriftsartikel (refereegranskat)abstract
- Sleepiness has been identified as one of the most important factors contributing to road crashes. However, almost all work on the detailed changes in behavior and physiology leading up to sleep related crashes has been carried out in driving simulators. It is not clear, however, to what extent simulator results can be generalized to real driving. This study compared real driving with driving in a high fidelity, moving base, driving simulator with respect to driving performance, sleep related physiology (using electroencephalography and electrooculography) and subjective sleepiness during night and day driving for 10 participants. The real road was emulated in the simulator. The results show that the simulator was associated with higher levels of subjective and physiological sleepiness than real driving. However, both for real and simulated driving, the response to night driving appears to be rather similar for subjective sleepiness and sleep physiology. Lateral variability was more responsive to night driving in the simulator, while real driving at night involved a movement to the left in the lane and a reduction of speed, both of which effects were absent in the simulator. It was concluded that the relative validity of simulators is acceptable for many variables, but that in absolute terms simulators cause higher sleepiness levels than real driving. Thus, generalizations from simulators to real driving must be made with great caution.
|
|
