Spyridon Kousidis

Situationally Aware In-Car Information Presentation Using Incremental Speech Generation: Safer, and More Effective

Holding non-co-located conversations while driving is dangerous (Horrey and Wickens, 2006; Strayer et al., 2006), much more so than conversations with physically present, “situated” interlocutors (Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested a simple strategy for making in-car dialogue systems aware of the driving situation, by giving them the capability to interrupt themselves when a dangerous situation is detected, and resume when over. We show that this improves both driving performance and recall of system-presented information, compared to a non-adaptive strategy.

Better Driving and Recall When In-car Information Presentation Uses Situationally-Aware Incremental Speech Output Generation

It is established that driver distraction is the result of sharing cognitive resources between the primary task (driving) and any other secondary task. In the case of holding conversations, a human passenger who is aware of the driving conditions can choose to interrupt his speech in situations potentially requiring more attention from the driver, but in-car information systems typically do not exhibit such sensitivity. We have designed and tested such a system in a driving simulation environment. Unlike other systems, our system delivers information via speech (calendar entries with scheduled meetings) but is able to react to signals from the environment to interrupt when the driver needs to be fully attentive to the driving task and subsequently resume its delivery. Distraction is measured by a secondary short-term memory task. In both tasks, drivers perform significantly worse when the system does not adapt its speech, while they perform equally well to control conditions (no concurrent task) when the system intelligently interrupts and resumes.