Brief Report: Driving Hazard Perception in Autism

Sheppard, Elizabeth; Ropar, Danielle; Underwood, Geoffrey; Loon, Editha van

doi:10.1007/s10803-009-0890-5

Cited by 76 publications

(68 citation statements)

References 15 publications

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“…In addition to these core deficit areas, recent evidence suggests that adolescents and young adults with ASD have difficulty in learning safe driving skills [3–5]. In particular, compared with their typically developed peers, individuals with ASD demonstrated unsafe gaze patterns and higher levels of anxiety when operating a driving simulator [6], [7], responded slower during steering, identified fewer social hazards, and showed problematic multi-tasking ability [8], [9]. In the US, driving plays a critical role in everyday life and is essential for achieving adult independence.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

Fan

Wade

Key

et al. 2018

IEEE Trans. Biomed. Eng.

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objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.

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Section: Introductionmentioning

confidence: 99%

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

Fan

Wade

Key

et al. 2018

IEEE Trans. Biomed. Eng.

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“…Studies with older children (around 5 years old) verified that while neurotypical children prefer hearing the voice of their mothers (social stimuli), children with an autism spectrum disorder (ASD) prefer hearing sounds that are not related to persons (non social stimuli) (Klin, 1991;. Similar conclusions were obtained by Mongillo et al (2008) and by Sheppard et al (2010). Attempting to explain this preference we can say that the social world appears to be too confusing and difficult to interpret for people with AS.…”

Section: Social Perceptionmentioning

confidence: 68%

Lost in the Social World: How Social Cognitive Deficits Affect Social Functioning of People with Asperger Syndrome

Figueira¹,

Fuentes²,

Ruíz³

2012

Mental Illnesses - Understanding, Prediction and Control

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“…The limited studies on driving in autism have reported that individuals with autism spectrum disorders perform worse than typically developed individuals. For example, the study of Sheppard and colleagues [28] found slower reaction time for reporting road hazards. They reasoned that the autism spectrum group had slower responses to hazards due to the complexity of judging the situation [9] or had difficulties in planning and producing movements to respond [12].…”

Section: Discussionmentioning

confidence: 99%

“…Sheppard and colleagues [28] examined detection of driving hazards and found that individuals on the autism spectrum were less accurate at reporting hazards arising from social activity and overall had slower reactions times in reporting both social and nonsocial events. Using a driving simulator, in a small study of 7 teenagers on the autism spectrum, it was reported that the ASD teenagers compared to TD were involved in a greater number of accidents such as off-road crashes, collisions and centre-line crossings [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluating multimodal driver displays of varying urgency for drivers on the autistic spectrum

Shim

Politis

et al. 2015

Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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This paper examines the response of 10 typical and 10 autism spectrum participants in their reaction to a set of uniand multisensory warning signals designed to indicate different levels of urgency. The warnings were composed of auditory, visual and tactile signals that were presented alone or in combination. Two experiments were conducted, a first that examined perceived urgency and annoyance with the warnings and a second that used a driving simulator scenario to explore recognition of the level of urgency and the speed of response. Results of Experiment 1 showed that there was no difference between groups in the perceived urgency of the warning signals, though the autism spectrum group reported less annoyance with the signals. Results of Experiment 2 showed that while both groups showed high accuracy in correctly reporting urgency level, the autism spectrum group performed better. Moreover, the fastest overall reaction times obtained were by the autism spectrum group when the warning included a visual component, with vision alone producing the quickest response. These results provide novel empirical insights on behaviour of drivers with autism when exposed to multimodal driver displays. They also highlight how consideration of characteristics of individual differences can contribute to the design of effective warning signals.

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Brief Report: Driving Hazard Perception in Autism

Cited by 76 publications

References 15 publications

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

Lost in the Social World: How Social Cognitive Deficits Affect Social Functioning of People with Asperger Syndrome

Evaluating multimodal driver displays of varying urgency for drivers on the autistic spectrum

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