| 1. |
- Sandberg, David, 1980, et al.
(författare)
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Detecting driver sleeepiness using optimized non-linear combinations of sleepiness indicators
- 2011
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 12:1, s. 97-108
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the problem of detecting sleepiness in car drivers. First, a variety of sleepiness indicators (based on driving behavior) proposed in the literature were evaluated. These indicators were then subjected to parametric optimization using stochastic optimization methods. To improve performance, the functional form of some of the indicators was generalized before optimization. Next, using a neural network, the best performing sleepiness indicators were combined with a mathematical model of sleepiness, i.e., the sleep/wake predictor (SWP). The analyses were based on data obtained from a study that involved 12 test subjects at the moving-base driving simulator at the Swedish National Road and Transportation Research Institute (VTI), Linkping, Sweden. The data were derived from 12 1-h driving sessions for each test subject, with varying degrees of sleepiness. The performance measure (range [0,1]) for indicators was taken as the average of sensitivity and specificity. Starting with indicators proposed in the literature, the best such indicator, i.e., the standard deviation of the yaw angle, reached a performance score of 0.72 on previously unseen test data. It was found that indicators based on a given signal gave essentially equal performance after parametric optimization, but in no case was it better than 0.72. The best generalized indicator (the generic variability indicator) obtained a performance score of 0.74. SWP achieved a score of 0.78. However, by nonlinearly combining SWP with the generic variability indicator, a score of 0.83 was obtained. Thus, the results imply that a nonlinear combination of a measure based on driving behavior with a model of sleepiness significantly improves driver sleepiness detection.
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| 2. |
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| 3. |
- 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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| 5. |
- Radun, Igor, et al.
(författare)
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Sleepy drivers on a slippery road : A pilot study using a driving simulator
- 2022
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Ingår i: Journal of Sleep Research. - : John Wiley & Sons. - 0962-1105 .- 1365-2869. ; 31:2
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Tidskriftsartikel (refereegranskat)abstract
- Sleepy drivers have problems with keeping the vehicle within the lines, and might often need to apply a sudden or hard corrective steering wheel movement. Such movements, if they occur while driving on a slippery road, might increase the risk of ending off road due to the unforgiving nature of slippery roads. We tested this hypothesis. Twelve young men participated in a driving simulator experiment with two counterbalanced conditions; dry versus slippery road x day (alert) versus night (sleepy) driving. The participants drove 52.5 km on a monotonous two-lane highway and rated their sleepiness seven times using the Karolinska Sleepiness Scale. Blink durations were extracted from an electrooculogram. The standard deviation of lateral position and the smoothness of steering events were measures of driving performance. Each outcome variable was analysed with mixed-effect models with road condition, time-of-day and time-on-task as predictors. The Karolinska Sleepiness Scale increased with time-on-task (p < 0.001) and was higher during night drives (p < 0.001), with a three-way interaction suggesting a small increased sleepiness with driving time at night with slippery road conditions (p = 0.012). Blink durations increased with time-on-task (p < 0.01) with an interaction between time-of-day and road condition (p = 0.040) such that physiological sleepiness was lower for sleep-deprived participants in demanding road conditions. The standard deviation of lateral position increased with time-on-task (p = 0.026); however, during night driving it was lower on a slippery road (p = 0.025). The results indicate that driving in demanding road condition (i.e. slippery road) might further exhaust already sleepy drivers, although this is not clearly reflected in driving performance.
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| 6. |
- Sandberg, David, 1980, et al.
(författare)
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The Characteristics of Sleepiness During Real Driving at Night - A Study of Driving Performance, Physiology and Subjective Experience
- 2011
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Ingår i: Sleep. - 1550-9109 .- 0161-8105. ; 34:10, s. 1317-1325
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Tidskriftsartikel (refereegranskat)abstract
- Study Objectives: Most studies of sleepy driving have been carried out in driving simulators. A few studies of real driving are available, but these have used only a few sleepiness indicators. The purpose of the present study was to characterize sleepiness in several indicators during real driving at night, compared with daytime driving. Design: Participants drove 55 km (at 90km/h) on a 9-m-wide rural highway in southern Sweden. Daytime driving started at 09: 00 or 11: 00 (2 groups) and night driving at 01: 00 or 03: 00 (balanced design). Setting: Instrumented car on a real road in normal traffic. Participants: Eighteen participants drawn from the local driving license register. Interventions: Daytime and nighttime drives. Measurement and Results: The vehicle was an instrumented car with video monitoring of the edge of the road and recording of the lateral position and speed. Electroencephalography and electrooculography were recorded, together with ratings of sleepiness every 5 minutes. Pronounced effects of night driving were seen for subjective sleepiness, electroencephalographic indicators of sleepiness, blink duration, and speed. Also, time on task showed significant effects for subjective sleepiness, blink duration, lane position, and speed. Sleepiness was highest toward the end of the nighttime drive. Night driving caused a leftward shift in lateral position and a reduction of speed. The latter two findings, as well as the overall pattern of sleepiness indicators, provide new insights into the effects of night driving. Conclusion: Night driving is associated with high levels of subjective, electrophysiologic, and behavioral sleepiness.
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| 7. |
- Sandberg, David, 1980, et al.
(författare)
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The impact of sleepiness on lane positioning in truck driving
- 2013. - 1
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Ingår i: Driver Distraction and Inattention. - Farnham : Ashgate. - 9781409425854 - 9781315578156 ; 1, s. 405-416
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Bokkapitel (refereegranskat)abstract
- This chapter concerns the detection of sleepiness in truck drivers. Data obtained from a driver sleepiness study involving real-world driving are used in order to analyse the performance of several sleepiness indicators based on driving behavior; such as, for example, variability in lateral position and heading angle. Contrary to the results obtained for passenger cars, for heavy trucks it is found that indicators based on variability provide little or no information; their performance does not rise significantly above chance levels.However, the data indicate that there is a significant difference in the average lane position for sleepy and alert drivers, respectively, such that a sleepy driver generally places the vehicle closer (by about 0.2 m) to the centre of the road than an alert driver. The analysis also shows a significant, monotonous, increase in average lateral position (measured from the right, outer, lane boundary towards the lane centre) between the four cases of (i) daytime alert driving, (ii) daytime sleepy driving, (iii) night-time alert driving and (iv) nighttime sleepy driving.
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| 8. |
- Torabi, Sina, 1990, et al.
(författare)
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Fuel consumption optimization of heavy-duty vehicles using genetic algorithms
- 2017
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Ingår i: 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings. - 9781509046010 ; , s. 29-36
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Konferensbidrag (refereegranskat)abstract
- The performance of a method for reducing the fuel consumption of a heavy duty vehicle (HDV) is described and evaluated both in simulation and using a real HDV. The method, which involves speed profile optimization using a genetic algorithm, was applied to a set of road profiles (covering sections of 10 km), resulting in average fuel savings of 11.5% and 10.2% (relative to standard cruise control), in the simulation and the real HDV, respectively. Here, a compact representation of road profiles in the form of composite Bézier curves has been used, thus reducing the search space for speed profile optimization, compared to an earlier approach. In addition to outperforming MPC-based methods commonly found in the literature by at least 3 percentage points (in similar settings), the results also show that our simulations are sufficiently accurate to be transferred directly to a real HDV. In cases where the allowed range of speed variation was restricted, the proposed method outperformed standard predictive cruise control (PCC) by an average of around 3 percentage points as well, over the same road profiles.
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| 9. |
- Torabi, Sina, 1990, et al.
(författare)
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Fuel-Efficient Driving Strategies for Heavy-Duty Vehicles: A Platooning Approach Based on Speed Profile Optimization
- 2018
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Ingår i: Journal of Advanced Transportation. - : Hindawi Limited. - 0197-6729 .- 2042-3195. ; 2018
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Tidskriftsartikel (refereegranskat)abstract
- A method for reducing the fuel consumption of a platoon of heavy-duty vehicles (HDVs) is described and evaluated in simulations for homogeneous and heterogeneous platoons. The method, which is based on speed profile optimization and is referred to as P-SPO, was applied to a set of road profiles of 10 km length, resulting in fuel reduction of 15.8% for a homogeneous platoon and between 16.8% and 17.4% for heterogeneous platoons of different mass configurations, relative to the combination of standard cruise control (for the lead vehicle) and adaptive cruise control (for the follower vehicle). In a direct comparison with MPC-based approaches, it was found that P-SPO outperforms the fuel savings of such methods by around 3 percentage points for the entire platoon, in similar settings. In P-SPO, unlike most common platooning approaches, each vehicle within the platoon receives its own optimized speed profile, thus eliminating the intervehicle distance control problem. Moreover, the P-SPO approach requires only a simple vehicle controller, rather than the two-layer control architecture used in MPC-based approaches.
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| 10. |
- Torabi, Sina, 1990, et al.
(författare)
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Road grade and vehicle mass estimation for heavy-duty vehicles using feedforward neural networks
- 2019
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Ingår i: 4th International Conference on Intelligent Transportation Engineering, ICITE 2019. ; , s. 316-321
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Konferensbidrag (refereegranskat)abstract
- In this paper, a neural network approach is presented for solving the problem of estimating road grade and vehicle mass, for the case of simulated heavy-duty vehicles (HDVs) driving on highways. After training, and using only signals normally available in HDVs, the (feedforward) neural network provides road grade estimates with an average root mean square (RMS) error of around 0.10 to 0.14 degrees, and mass estimates with an average RMS error of around 1%, when applied to two different test data sets (one with synthetic roads and one based on a real road), not used during the training phase. The estimates obtained outperform road grade and mass estimates obtained with other approaches.
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