While in-car navigation systems enhance situational awareness, they also increase drivers’ visual distraction and cognitive load. This project explores the efficacy of multi-modal route guidance cues for ‘safer’ driving. We studied the impact of modality combinations on driving performance and cognitive load for elder and younger drivers (Figure 3 Left).

Figure 3. Simulated driving test-bed with multisensory cues and wearable sensors.

This study highlighted a number of ‘safer’ navigation differences between elder and younger drivers. In general, the most useful way to reduce way-finding errors included the full combination of visual, auditory, and haptic feedback (Figure 3 Right). However, for elder drivers, adding more modalities strained their already high workload. Thus, we found that personalized navigation systems enhanced the benefit of auditory feedback for elder drivers without increasing the number of sensory feedbacks. In contrast, for younger drivers, adding haptic feedback to traditional audio and visual feedback led to more attentive driving. Furthermore, we found that safer navigation for younger drivers incorporates new non-visual feedback to minimize distractions caused by visual feedback. These results were demonstrated through task performance-based measures, eye-tracking measures, subjective workload measures (e.g., NASA-TLX), and objective workload measures based on psycho-physiological responses. We predicted a driver’s cognitive load in near real-time by using machine learning techniques.

A number of psycho-physiological responses significantly differ with the intervention of sensory augmentation (i.e., with haptic augmentation vs. without haptic augmentation). Additionally, many of them strongly interacted with specific types of intervention (i.e., replacement or addition) as well as age groups (i.e., younger or elder adults). This provides early evidence that psycho-physiological assessment may promise tracking of the expertise reversal effect in higher sampling rates during interaction with sensor augmentation systems, further detecting the inflection points of the reversal (e.g., changes from ‘benefits > costs’ to ‘costs > benefits’ or vice versa).