| 1. |
- Ali, Mohammad, 1982, et al.
(author)
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Real-time Implementation of a Novel Safety Function for Prevention of Loss of Vehicle Control
- 2011
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In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. - 9781457721984 ; , s. 1427-1432
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Conference paper (peer-reviewed)abstract
- We present a novel safety function for prevention of vehicle control loss. The safety function overcomes some of the limitations of conventional Electronic Stability Control (ESC) systems. Based on sensor information about the host vehicle's state and the road ahead, a threat assessment algorithm predicts the future evolution of the vehicle's state. If the vehicle motion, predicted over a finite time horizon violates safety constraints, autonomous deceleration is activated in order to prevent vehicle loss of control. The safety function has been implemented in real-time. Experimental results indicate that the safety function relies less on the driver's skills than conventional ESC systems and that a more controllable and comfortable vehicle motion can be acquired when the function is active.
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| 2. |
- Jonsson, Patrik, 1968-
(author)
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Road Condition Discrimination : using Weather Data and Camera Images
- 2011
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In: 2011 14TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE conference proceedings. - 9781457721984 ; , s. 1616-1621
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Conference paper (peer-reviewed)abstract
- An intelligent way of determining the road condition is needed to perform an effective road maintenance that results in high accessibility of the road network and high traffic safety. The hypothesis is that data from existing meteorological sensors and camera images from Road Weather information Systems (RWiS) could be used to improve the road condition classification. Previous research has found that an image analysis alone can estimate the road condition. This paper aims to evaluate if an extensive dataset retrieved from a RWiS site is sufficient to give a more accurate road condition classification than one obtained with an image analysis alone. The study reveals that RWiS data gives additional information for discrimination of the road conditions compared to image analysis only.
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| 3. |
- Sandberg, David, 1980
(author)
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The performance of driver sleepiness indicators as a function of interval length
- 2011
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In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC (ITSC 2011; Washington, DC; 5 October 2011 through 7 October 2011). - 9781457721984 ; :Article number 6082939, s. 1735-1740
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Conference paper (peer-reviewed)abstract
- Research on driver sleepiness, often aimed towards devising driver sleepiness detection systems, involves the computation of driver sleepiness indicators. Two examples of such, which are often studied in the literature, are the standard deviation of the vehicle's lateral position and the driver's averaged blink duration. How good such measures actually indicate driver sleepiness may depend on the length of the time series from which the indicators are computed. However, the question of optimal interval length when studying driver sleepiness indicators seems to be largely ignored in the literature. Instead, the specific interval length used in most papers appears rather arbitrarily chosen, or much influenced by the design of the study. Interval lengths of five minutes are often used, but much shorter intervals (e.g. 60 s, 30 s or even shorter) as well as longer intervals (e.g. 10 or 30 minutes) have been used in some studies. The present work aims to improve the situation by analyzing the performance of six indicators of driver sleepiness as a function of interval length. The findings have implications on driver sleepiness research, especially research aimed at devising a system for driver sleepiness detection. The results indicate that interval lengths of 60 s or more generally give better results than shorter intervals (10-30 s) when computing driver sleepiness indicators, but also that the difference between 60 s and even longer intervals (120-900 s) seems small.
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| 4. |
- Trigueiro Baptista, Arthur, et al.
(author)
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Towards building an uncertainty-aware personal journey planner
- 2011
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In: 14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011. - 9781457721984 ; , s. 378-383
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Conference paper (peer-reviewed)abstract
- Public transit, specially the bus, are usually considered unreliable as arrival times do not often match with the scheduled times. Current journey planners do not take this uncertainty into account, for example do not account for the risk of missing a transfer and of delays due to traffic conditions. This paper deals with analytics for building a multi modal journey planner, by estimating arrival and travel times for a certain trip involving transfers, and taking into account the delay of buses and risk of missing connections. We develop a system to compute probability distributions of end-to-end travel times, taking into account uncertain departure, travel and transfer times. We tested the system using real historical data for the whole bus system in Dublin, and showed the uncertainty of scheduled travel times with respect to the stochasticity of real traffic conditions. This is a first step towards building an uncertainty-aware multi-modal journey planner.
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| 5. |
- Özkan, Emre, 1980-, et al.
(author)
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A Bayesian Approach to Jointly Estimate Tire Radii and Vehicle Trajectory
- 2011
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In: Proceedings of the International IEEE Conference on Intelligent Transportation Systems. - Washington DC, USA : IEEE conference proceedings. - 9781457721984 ; , s. 1-6
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Conference paper (peer-reviewed)abstract
- High-precision estimation of vehicle tire radii is considered, based on measurements on individual wheel speeds and absolute position from a global navigation satellite system (GNSS). The wheel speed measurements are subject to noise with time-varying covariance that depends mainly on the road surface. The novelty lies in a Bayesian approach to estimate online the time-varying radii and noise parameters using a marginalized particle filter, where no model approximations are needed such as in previously proposed algorithms based on the extended Kalman filter. Field tests show that the absolute radius can be estimated with millimeter accuracy, while the relative wheel radius on one axle is estimated with submillimeter accuracy.
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