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Title: Detection of Vehicle Approach in the Presence of Additional Motion and Simulated Observer Motion at Road Junctions

Organisation: University of London (Royal Holloway)
Date uploaded: 16th September 2014
Date published/launched: June 2013

In this research, computer generated scenes with photorealistic vehicle images, and a psychophysical staircase methodology, were used to explore drivers' ability to detect the approach of both motorcycles and cars within a contextually rich city scene.

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One of the key contributory factors for accident involvement is the misjudgment of vehicle approach.

Past research has indicated that individuals can use the rate of visual "looming" in order to judge the time to arrival of approaching vehicles. Although a large number of road traffic collisions occur at roadside junctions, very little research has focused on individuals' abilities to detect the onset of visual looming within a complex road scene at junction scenarios.

In this research, computer generated scenes were used to explore drivers' ability to detect the approach of both motorcycles and cars within a contextually rich city scene. Across three experiments the effect of additional vehicular and observer motion on driver detection of vehicle approach was assessed.

Results showed that individuals were significantly poorer at detecting the approach of the motorcycle stimulus compared with the car stimulus. Results also showed that additional vehicular motion within the scene had a negative effect on detection thresholds for the car stimulus.

Finally, the results showed that introducing lateral global motion of the scene, such as might occur if the observer was moving steadily forward from a junction, negatively affected detection thresholds.

The theoretical implications of the findings are discussed, including how vehicles travelling at high speed are often below the threshold for detecting visual looming. Practical implications for road design and layout are discussed that address the perceptual errors noted.

For more information contact:
Professor John Wann

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