Aging and the Detection of Collision Events in Fog

Bian, Zheng; Ni, Rui; Guindon, Amy; Andersen, George J.

doi:10.17077/drivingassessment.1304

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Driving is one real world task in which improved or even maintained performance through this type of intervention could enable older adults to drive safely for longer periods of time than without completing such an intervention. Aspects of driving that place demands on speed of processing, divided attention, and visual-perceptual processing are most susceptible to age-related declines in performance, such as steering control (Ni, Andersen, McEvoy, & Rizzo 2005), staying a constant distance behind a lead car (Dashtrup, Lees, Dawson, Lee, & Rizzo, 2009) and collision detection, particularly at faster speeds or in fog (Anderson, Cisneros, Saidpour, & Atchley, 2000 & Bian, Ni, Guindon, & Andersen, 2009). Thus, a combination of cognitive training with driving simulations that focus on these driving skills may be most beneficial for older drivers.…”

Section: Discussionmentioning

confidence: 99%

The STEP model: Characterizing simultaneous time effects on practice for flight simulator performance among middle-aged and older pilots.

Kennedy¹,

Taylor²,

Noda³

et al. 2015

Psychology and Aging

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Understanding the possible effects of the number of practice sessions (practice) and time between practice sessions (interval) among middle-aged and older adults in real world tasks has important implications for skill maintenance. Prior training and cognitive ability may impact practice and interval effects on real world tasks. In this study, we took advantage of existing practice data from five simulated flights among 263 middle-aged and older pilots with varying levels of flight expertise (defined by FAA proficiency ratings). We developed a new STEP (Simultaneous Time Effects on Practice) model to: (1) model the simultaneous effects of practice and interval on performance of the five flights, and (2) examine the effects of selected covariates (age, flight expertise, and three composite measures of cognitive ability). The STEP model demonstrated consistent positive practice effects, negative interval effects, and predicted covariate effects. Age negatively moderated the beneficial effects of practice. Additionally, cognitive processing speed and intra-individual variability (IIV) in processing speed moderated the benefits of practice and/or the negative influence of interval for particular flight performance measures. Expertise did not interact with either practice or interval. Results indicate that practice and interval effects occur in simulated flight tasks. However, processing speed and IIV may influence these effects, even among high functioning adults. Results have implications for the design and assessment of training interventions targeted at middle-aged and older adults for complex real world tasks.

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Section: Discussionmentioning

confidence: 99%

The STEP model: Characterizing simultaneous time effects on practice for flight simulator performance among middle-aged and older pilots.

Kennedy¹,

Taylor²,

Noda³

et al. 2015

Psychology and Aging

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show abstract

“…Indeed, dramatic pile-ups often occur due to hard braking implied by reduced visibility in fog. In particular, elderly people are likely to have more accidents in fog than young people due, among other factors, to reduced contrast perception [21]. One ADAS partially dedicated to fog consists in adapting the speed of the vehicle with respect to the prevailing weather conditions as proposed in [22] so as to increase the safety margin of the driver.…”

Section: A Principlementioning

confidence: 99%

Vision Enhancement in Homogeneous and Heterogeneous Fog

Tarel

Hautiére

Caraffa

et al. 2012

IEEE Intell. Transport. Syst. Mag.

382

210

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Abstract-One source of accidents when driving a vehicle is the presence of fog. Fog fades the colors and reduces the contrasts in the scene with respect to their distances from the driver. Various camera-based Advanced Driver Assistance Systems (ADAS) can be improved if efficient algorithms are designed for visibility enhancement in road images. The visibility enhancement algorithm proposed in [1] is not optimized for road images. In this paper, we reformulate the problem as the inference of the local atmospheric veil from constraints. The algorithm in [1] thus becomes a particular case. From this new derivation, we propose to better handle road images by introducing an extra constraint taking into account that a large part of the image can be assumed to be a planar road. The advantages of the proposed local algorithm are the speed, the possibility to handle both color and gray-level images, and the small number of parameters. A new scheme is proposed for rating visibility enhancement algorithms based on the addition of several types of generated fog on synthetic and camera images. A comparative study and quantitative evaluation with other state-of-the-art algorithms is thus proposed. This evaluation demonstrates that the new algorithm produces better results with homogeneous fog and that it is able to deal better with the presence of heterogeneous fog. Finally, we also propose a model allowing to evaluate the potential safety benefit of an ADAS based on the display of defogged images.

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Driving Under Reduced Visibility Conditions for Older Adults

Ni¹

2012

Encyclopedia of Sustainability Science and Technology

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Aging and the Detection of Collision Events in Fog

Cited by 5 publications

References 13 publications

The STEP model: Characterizing simultaneous time effects on practice for flight simulator performance among middle-aged and older pilots.

The STEP model: Characterizing simultaneous time effects on practice for flight simulator performance among middle-aged and older pilots.

Vision Enhancement in Homogeneous and Heterogeneous Fog

Driving Under Reduced Visibility Conditions for Older Adults

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