Combining vehicle, physiological, visual data to provide driver feedback
The mental workload that drivers are under can have an effect on their ability to drive safely. When drivers are under low workload, they may become bored, inattentive, and fatigued, but under high workload, they may become distracted and slow to respond to roadway events. Smart vehicles can monitor driver state and take action to keep drivers from these dangerous levels of workload. We are developing algorithms to detect workload using vehicle (e.g., steering wheel input), physiological (e.g., heart rate measured through on-body sensors) and vision-based (e.g., facial expressions captured by an in-car camera) data.
Enhancing voice-activation technologies to improve driving behaviour
Voice-activated infotainment systems are becoming increasingly common in today’s automobiles, especially as an alternative to the more dangerous and often illegal option of hand-held cell phone usage. However, the voice-activated systems are not without their shortcomings. In the presence of loud background noise, such as talking passengers or music, voice recognition accuracy decreases. Working with an industry leader in voice control technologies, this project aims to evaluate how drivers interact with and feel about a novel voice control system which can still work effectively under heavy background noise.
