Predicting perceived risk of traffic scenes using computer vision

Perceived risk, or subjective risk, is an important concept in the field of traffic psychology and automated driving. In this paper, we investigate whether perceived risk in images of traffic scenes can be predicted from computer vision features that may also be used by automated vehicles (AVs). We conducted an international crowdsourcing...

Towards the detection of driver–pedestrian eye contact

Non-verbal communication, such as eye contact between drivers and pedestrians, has been regarded as one way to reduce accident risk. So far, studies have assumed rather than objectively measured the occurrence of eye contact. We address this research gap by developing an eye contact detection method and testing it in an indoor experiment with...

Redesigning Today’s Driving Automation Toward Adaptive Backup Control With Context-Based and Invisible Interfaces

Objective: We investigated a driver monitoring system (DMS) designed to adaptively back up distracted drivers with automated driving. Background: Humans are likely inadequate for supervising today’s on-road driving automation. Conversely, backup concepts can use eye-tracker DMS to retain the human as the primary driver and use computerized...

Graded auditory feedback based on headway: An on-road pilot study

Auditory feedback produced by driver assistance systems can benefit safety. However, auditory feedback is often regarded as annoying, which may result in disuse of the system. An auditory headway feedback system was designed with the aim to improve user acceptance and driving safety. The algorithm used a graded approach, which means that it...