| 1. |
- Abedin, Sarder, et al.
(författare)
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Data Freshness and Energy-Efficient UAV Navigation Optimization : A Deep Reinforcement Learning Approach
- 2021
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - 1524-9050 .- 1558-0016. ; 22:9, s. 5994-6006
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6 % and 3.13 % more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.
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| 2. |
- Ahlström, Christer, et al.
(författare)
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A gaze-based driver distraction warning system and its effect on visual behaviour
- 2013
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 14:2, s. 965-973
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Tidskriftsartikel (refereegranskat)abstract
- Driver distraction is a contributing factor to many crashes; therefore, a real-time distraction warning system should have the potential to mitigate or circumvent many of these crashes. The objective of this paper is to investigate the usefulness of a real-time distraction detection algorithm called AttenD. The evaluation is based on data from an extended field study comprising seven drivers who drove on an average of 4351 ± 2181 km in a naturalistic setting.Visual behavior was investigated both on a global scale and on a local scale in the surroundings of each warning. An increase in the percentage of glances at the rear-view mirror and a decrease in the amount of glances at the center console were found. The results also show that visual time sharing decreased in duration from 9.94 to 9.20 s due to the warnings, that the time from fully attentive to warning decreased from 3.20 to 3.03 s, and that the time from warning to full attentiveness decreased from 6.02 to 5.46 s. The limited number of participants does not allow any generalizable conclusions, but a trend toward improved visual behavior could be observed. This is a promising start for further improvements of the algorithm and the warning strategy.
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| 3. |
- Ahlström, Christer, 1977-, et al.
(författare)
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A Generalized Method to Extract Visual Time-Sharing Sequences From Naturalistic Driving Data
- 2017
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 18:11, s. 2929-2938
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Tidskriftsartikel (refereegranskat)abstract
- Indicators based on visual time-sharing have been used to investigate drivers' visual behaviour during additional task execution. However, visual time-sharing analyses have been restricted to additional tasks with well-defined temporal start and end points and a dedicated visual target area. We introduce a method to automatically extract visual time-sharing sequences directly from eye tracking data. This facilitates investigations of systems, providing continuous information without well-defined start and end points. Furthermore, it becomes possible to investigate time-sharing behavior with other types of glance targets such as the mirrors. Time-sharing sequences are here extracted based on between-glance durations. If glances to a particular target are separated by less than a time-based threshold value, we assume that they belong to the same information intake event. Our results indicate that a 4-s threshold is appropriate. Examples derived from 12 drivers (about 100 hours of eye tracking data), collected in an on-road investigation of an in-vehicle information system, are provided to illustrate sequence-based analyses. This includes the possibility to investigate human-machine interface designs based on the number of glances in the extracted sequences, and to increase the legibility of transition matrices by deriving them from time-sharing sequences instead of single glances. More object-oriented glance behavior analyses, based on additional sensor and information fusion, are identified as the next future step. This would enable automated extraction of time-sharing sequences not only for targets fixed in the vehicle's coordinate system, but also for environmental and traffic targets that move independently of the driver's vehicle.
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| 4. |
- Ahlström, Christer, et al.
(författare)
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Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets
- 2012
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; vol.13:no.2, s. pp.553-564
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Tidskriftsartikel (refereegranskat)abstract
- Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10 000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task.
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| 5. |
- Ahlström, Christer, 1977-, et al.
(författare)
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Real-Time Adaptation of Driving Time and Rest Periods in Automated Long-Haul Trucking : Development of a System Based on Biomathematical Modelling, Fatigue and Relaxation Monitoring
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1524-9050 .- 1558-0016. ; 23:5, s. 4758-4766
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Tidskriftsartikel (refereegranskat)abstract
- Hours of service regulations govern the working hours of commercial motor vehicle drivers, but these regulations may become more flexible as highly automated vehicles have the potential to afford periods of in-cab rest or even sleep while the vehicle is moving. A prerequisite is robust continuous monitoring of when the driver is resting (to account for reduced time on task) or sleeping (to account for the reduced physiological drive to sleep). The overall aims of this paper are to raise a discussion of whether it is possible to obtain successful rest during automated driving, and to present initial work on a hypothetical data driven algorithm aimed to estimate if it is possible to gain driving time after resting under fully automated driving. The presented algorithm consists of four central components, a heart rate-based relaxation detection algorithm, a camera-based sleep detection algorithm, a fatigue modelling component taking time awake, time of day and time on task into account, and a component that estimates gained driving time. Real-time assessment of driver fitness is complicated, especially when it comes to the recuperative value of in-cab sleep and rest, as it depends on sleep quality, time of day, homeostatic sleep pressure and on the activities that are carried out while resting. The monotony that characterizes for long-haul truck driving is clearly interrupted for a while, but the long-term consequences of extended driving times, including user acceptance of the key stakeholders, requires further research.
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| 6. |
- Ahlström, Christer, 1977-, et al.
(författare)
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Technologies for Risk Mitigation and Support of Impaired Drivers
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 23:5, s. 4736-4738
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This editorial serves as an extended introduction to the Special Issue on Technologies for Risk Mitigation and Support of Impaired Drivers. It gives the context to recent advances in assisted and automated driving and the new challenges that arise when modern technology meets human users. The Special Issue focuses on the development of robust sensors and detection algorithms for driver state monitoring of fatigue, stress, and inattention, and on the development of personalized multimodal, user-oriented, and adaptive information, warning, actuation, and handover strategies. A summary of more recent developments serves as a motivation for each article that follows.
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| 7. |
- Ahlström, Christer, 1977-, et al.
(författare)
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Towards a Context-Dependent Multi-Buffer Driver Distraction Detection Algorithm
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1524-9050 .- 1558-0016. ; 23:5, s. 4778-4790
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents initial work on a context-dependent driver distraction detection algorithm called AttenD2.0, which extends the original AttenD algorithm with elements from the Minimum Required Attention (MiRA) theory. Central to the original AttenD algorithm is a time buffer which keeps track of how often and for how long the driver looks away from the forward roadway. When the driver looks away the buffer is depleted and when looking back the buffer fills up. If the buffer runs empty the driver is classified as distracted. AttenD2.0 extends this concept by adding multiple buffers, thus integrating situation dependence and visual time-sharing behaviour in a transparent manner. Also, the increment and decrement of the buffers are now controlled by both static requirements (e.g. the presence of an on-ramp increases the need to monitor the sides and the mirrors) as well as dynamic requirements (e.g., reduced speed lowers the need to monitor the speedometer). The algorithm description is generic, but a real-time implementation with concrete values for different parameters is showcased in a driving simulator experiment with 16 bus drivers, where AttenD2.0 was used to ensure that drivers are attentive before taking back control after an automated bus stop docking and depot procedure. The scalability of AttenD2.0 relative to available data sources and the level of vehicle automation is demonstrated. Future work includes expanding the concept to real-world environments by automatically integrating situational information from the vehicles environmental sensing and from digital maps.
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| 8. |
- Ali, Mohammad, 1982, et al.
(författare)
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Predictive Prevention of Loss of Vehicle Control for Roadway Departure Avoidance
- 2013
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 14:1, s. 56-68
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we investigate predictive approaches to the problem of roadway departure prevention via automated steering and braking. We assume a sensing infrastructure detecting road geometry and consider a two-layer accident avoidance framework consisting of a threat assessment and an intervention layer. A novel active safety function for prevention of loss of vehicle control is proposed and implemented using the considered accident avoidance framework. Simulation and experimental results are presented, showing that the proposed approach effectively exploits road preview information to prevent the vehicle from operating in regions of the state space where standard electronic stability control systems are normally activated.
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| 9. |
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| 10. |
- Antoniou, Constantinos, et al.
(författare)
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Non–linear Kalman Filtering Algorithms for On–line Calibration of Dynamic Traffic Assignment Models
- 2007
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - 1524-9050 .- 1558-0016. ; 8:4, s. 661-670
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Tidskriftsartikel (refereegranskat)abstract
- An online calibration approach that jointly estimates demand and supply parameters of dynamic traffic assignment (DTA) systems is presented and empirically validated through an extensive application. The problem can be formulated as a nonlinear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter, and therefore, nonlinear extensions need to be considered. The following three extensions to the Kalman filtering algorithm are presented: 1) the extended Kalman filter (EKF); 2) the limiting EKF (LimEKF); and 3) the unscented Kalman filter. The solution algorithms are applied to the on-line calibration of the state-of-the-art DynaMIT DTA model, and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy that is comparable to that of the best algorithm but with vastly superior computational performance. The robustness of the approach to varying weather conditions is demonstrated, and practical aspects are discussed.
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