...(Richardson Green, 2000). is concern, however; that using such devices may overload drivers and increase crash risk. This includes visual-manual interfaces that capture driver attention and induce drivers to look at the road less often (e.g., Wierwille & Tijerina, 1998) and auditory-speech interfaces, such as phone systems, for which the cognitive demands of conversation reduce awareness of the driving situation (e.g., Strayer, Drews, & Johnston, 2003).

Of the telematic tasks of concern during driving, entering a street address into a navigation system is often mentioned, and manual entry has been the topic of several studies (e.g., Chiang, Brooks, & Weir, 2001 ; Farber et al., 2000: Nowakowski, Utsui, & Green, 2000; see Green & Shah, 2003, for a review.)

Speech input is believed to be the ideal modality for information entry because it should present less competition for visual resources. However, speech interfaces with auditory feedback are not without cost. Lee, Caven, Haake, and Brown (2001), for example, found a significant delay in response time to braking events of a lead vehicle when drivers interacted with an auditory-speech system. In one of the few studies comparing interlace modalities for destination entry, Tijerina, Parmer, and Goodman (1998) found that a particular character recognition voice interface had shorter task times, required fewer glances, and had shorter mean glance durations than did several other manual interfaces.

Speech interfaces based on word recognition and the effect of recognition accuracy on driver performance have not been examined. At first thought, word-based recognition would seem ideal. In real systems, however, errors will occur and feedback needs to be provided to the driver. Often, for reasons of cost, speed, and technology, feedback is provided visually, leading to unknown visual and cognitive demands that compete with the primary task of driving.

To make cost-benefit engineering decisions regarding interface design, these aspects of speech recognition systems should be quantified. The current experiment considered them by comparing speech recognition of addresses with entry of the same addresses using a touch-screen keyboard. Speech recognition was split into word-based and character-based recognition. The latter method imitates keyboard entry using speech instead of key presses, allowing for a better comparison of the difference between speech input and motor input. Recognition accuracy was experimentally controlled and fixed across participants to provide some insights regarding the effects of errors on performance. Finally, driving performance was evaluated at the vehicle control level. For that kind of analysis, using curves of varying curvatures has been shown to be an effective method to keep the visual demands of the driving task steady (Tsimhoni & Green, 2001).

The objective of this experiment was to compare the effects of manual and voice entry methods on task performance and vehicle control as a function of several controlled levels of visual demand of driving. (Note: See Tsimhoni, Smith, & Green, 2001, the report on which this paper was based,)

METHOD

Participants

Twenty-four licensed drivers participated in this experiment, 12 younger (age 20-29 years, mean = 24) and 12 older (age 65-72 years, mean = 69), with equal numbers of men and women in each age group. Participants were recruited via an advertisement in the local newspaper and were paid $40. All participants had far visual acuity of 20/40 or better. All had midrange (80 cm) visual acuity of 20/70 or better and no color deficiencies. All older participants were retired but had maintained active lifestyles. Prescreening of all participants ensured they had good driving records and were physically healthy.

Experimental Design

Each participant used three entry methods (word-based speech recognition, character-based speech recognition, and typing on a touch-screen keyboard) combined with four levels of driving workload (parked, straight, moderate curves, and sharp curves). Because of a lack of effects observed in pilot tests and to keep the duration of the experiment reasonable, 5 of the 12 combinations were not tested: word-based speech recognition on straight sections and on moderate curves, and character-based speech recognition on moderate curves.

Participants in each age-gender subgroup were randomly assigned to one of two groups, which performed either the keyboard task followed by the speech recognition task or vice versa. Within each of these groups of 3 participants, the order of curvature was manipulated following a Latin square design so that each of the three road curvatures appeared first, second, or third for exactly 1 participant.

Test Materials and Equipment

Driving simulator. The experiment was conducted in the second-generation University of Michigan Transportation Research Institute (UMTRI) driver interface research simulator, a fixed-based driving simulator based on a network of Macintosh computers (UMTRI, 2001). The simulator consisted of...

NOTE: All illustrations and photos have been removed from this article.



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