Aging and Estimation of time to collision

Background Coincidence-anticipation timing (CAT) responses require individuals to determine the time at which an approaching object will arrive at (time to collision) or pass by (time to passage) the observer and to then make a response coincident with this time. Previous studies suggest that under some conditions time to collision estimates are more accurate when binocular and monocular cues are combined. The purpose of this study was to compare binocular and monocular coincidence anticipation timing responses with the Bassin Anticipation Timer, a device for testing and training CAT responses. Methods: Useable data were obtained from 20 participants. Coincidence-anticipation timing responses were determined using a Bassin Anticipation Timer over a range of approaching stimulus linear velocities of 5 to 40mph. Participants stood to the left side of the Bassin Anticipation track. The track was below eye height. The participants’ task was to push a button to coincide with arrival of the approaching stimulus at a location immediately adjacent to the participant. CAT responses were made under three randomized conditions: binocular viewing, monocular dominant eye viewing, and monocular non-dominant eye viewing. Results: Signed (constant), unsigned (absolute), and variable (standard deviation) CAT response errors were determined and compared across viewing conditions at each stimulus velocity. There were no significant differences in CAT errors between the conditions at any stimulus velocity, although the differences in signed and unsigned errors approached significance at 40mph. Conclusions: The addition of binocular cues did not result in a reduction in coincidence anticipation timing response errors compared to the monocular viewing conditions. There were no differences in CAT response errors between the monocular dominant eye viewing and monocular non-dominant eye viewing conditions.

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The Estimation of Time-Invariant Variables in Panel Analyses with Unit Fixed Effects

SSRN Electronic Journal ◽

10.2139/ssrn.565904 ◽

2004 ◽

Cited By ~ 21

Author(s):

Thomas Plümper ◽

Vera E. Troeger

Keyword(s):

Fixed Effects ◽

Time Invariant ◽

Estimation Of Time

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Using Traffic Disturbance Metrics to Estimate and Predict Freeway Traffic Breakdown and Safety Events

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211012422 ◽

2021 ◽

pp. 036119812110124

Author(s):

Leila Azizi ◽

Mohammed Hadi

Keyword(s):

Traffic Safety ◽

Management Strategies ◽

Connected Vehicles ◽

Freeway Traffic ◽

Transportation Management ◽

Time To Collision ◽

Decision Tools ◽

Traffic State ◽

Traffic Performance ◽

Traffic Disturbance

The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic metrics that can be used for better estimation and prediction of traffic performance. This study examines the use of disturbance metrics in combination with the macroscopic metrics usually used for the estimation of traffic safety and mobility. The disturbance metrics used are the number of oscillations and a measure of disturbance durations in the time exposed time to collision. The study investigates using the disturbance metrics in data clustering for better off-line categorization of traffic states. In addition, the study uses machine-learning based classifiers for the recognition and prediction of the traffic state and safety in real-time operations. The study also demonstrates that the disturbance metrics investigated are significantly related to crashes. Thus, this study recommends the use of these metrics as part of decision tools that support the activation of transportation management strategies to reduce the probability of traffic breakdown, ease traffic disturbances, and reduce the probability of crashes.

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Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance

Journal of Imaging ◽

10.3390/jimaging7040061 ◽

2021 ◽

Vol 7 (4) ◽

pp. 61

Author(s):

David Urban ◽

Alice Caplier

Keyword(s):

Neural Network ◽

Autonomous Vehicles ◽

Depth Estimation ◽

Video Camera ◽

Obstacle Detection ◽

Navigation Systems ◽

Time To Collision ◽

Static Data ◽

Monocular Depth ◽

Fully Connected

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

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