Driver behavior and performance have been studied extensively in the last decades. Researchers have developed theories and models of driver situational awareness, driver decision making and errors, information processing, and mental workload. Meanwhile, other scholars have focused on creating and optimizing in-vehicle technology-based interventions to increase driver safety. In recent years, two innovative technology-based tracks have emerged:
The first is the driver state monitoring track. Nowadays, in-vehicle sensors can detect useful indices as head position, eyelid, gaze, respiration rate, heart rate, and skin conductance to infer about fatigue, mental workload, alcohol consumption, distraction and risk perception. These data can serve as useful information for researchers, feedback to drivers, monitoring tools for fleet managers and insurers, and as input for automated systems that consider the driver state in their decisions.
The second track is Advanced Driver Assistance Systems (ADAS), which offer timely advice and feedback, and can even actively take (or give) control of the vehicle. Along with the potential of ADAS to promote safety, there are still challenges. ADAS may cause deterioration of driving skills, encourage the diversion of attention from the driving task to other stimuli, and impair risk perception. Specifically, ADAS with autonomous components require drivers to develop capabilities and strategies for monitoring the operation of the system, to be able to quickly respond when technology fails, and to effectively switch between levels of automation. Some of the challenges from the ADAS track can be met by applying developments from the driver monitoring track (e.g., automation that considers the driver state) while improvements in the ADAS track can impact the driver state (e.g., by reducing fatigue and mental workload) as measured in the driving monitoring track.
In light of the recent innovative technology-based tracks, new challenges for driver behavior and performance studies should be met; existing models should be reevaluated, technology-based interventions should be rechecked and measures to evaluate driver performance should be developed.
The aim of this Research Topic is to form a body of quality scientific research papers on driver behavior and performance that are based on analyses of sensor data about the driver state, driver behavior, and driver performance. We encourage researchers from different disciplines to submit empirical studies exploring new methods for evaluating driver performance when operating/monitoring highly automated systems, develop advanced driver decision-making models, and rethink about technology-based interventions to encourage safer behaviors. We also welcome reviews, meta-analyses, and commentaries on these issues.
Specific topics include but are not limited to:
- Reevaluation or development of new models of driver performance and/or behavior
- Technology-based interventions to improve driver performance and/or behavior
- Using sensor-based methods (e.g., physiological sensors, contact-free sensing systems) to monitor driver's states.
- Challenges in driver-ADAS interaction.
- Trust and acceptance of ADAS and of feedback from in-vehicle driver monitoring technologies.
- Naturalistic driving studies with ADAS and/or in-vehicle driver monitoring technologies.
By sharing new empirical findings on driver capacities and decision making, the operation of ADAS, and innovative tools to monitor and infer the driver state, we hope to promote a stimulating and enriching exchange of ideas between researchers, agents of change, practitioners, and other interested parties in these fields.
Driver behavior and performance have been studied extensively in the last decades. Researchers have developed theories and models of driver situational awareness, driver decision making and errors, information processing, and mental workload. Meanwhile, other scholars have focused on creating and optimizing in-vehicle technology-based interventions to increase driver safety. In recent years, two innovative technology-based tracks have emerged:
The first is the driver state monitoring track. Nowadays, in-vehicle sensors can detect useful indices as head position, eyelid, gaze, respiration rate, heart rate, and skin conductance to infer about fatigue, mental workload, alcohol consumption, distraction and risk perception. These data can serve as useful information for researchers, feedback to drivers, monitoring tools for fleet managers and insurers, and as input for automated systems that consider the driver state in their decisions.
The second track is Advanced Driver Assistance Systems (ADAS), which offer timely advice and feedback, and can even actively take (or give) control of the vehicle. Along with the potential of ADAS to promote safety, there are still challenges. ADAS may cause deterioration of driving skills, encourage the diversion of attention from the driving task to other stimuli, and impair risk perception. Specifically, ADAS with autonomous components require drivers to develop capabilities and strategies for monitoring the operation of the system, to be able to quickly respond when technology fails, and to effectively switch between levels of automation. Some of the challenges from the ADAS track can be met by applying developments from the driver monitoring track (e.g., automation that considers the driver state) while improvements in the ADAS track can impact the driver state (e.g., by reducing fatigue and mental workload) as measured in the driving monitoring track.
In light of the recent innovative technology-based tracks, new challenges for driver behavior and performance studies should be met; existing models should be reevaluated, technology-based interventions should be rechecked and measures to evaluate driver performance should be developed.
The aim of this Research Topic is to form a body of quality scientific research papers on driver behavior and performance that are based on analyses of sensor data about the driver state, driver behavior, and driver performance. We encourage researchers from different disciplines to submit empirical studies exploring new methods for evaluating driver performance when operating/monitoring highly automated systems, develop advanced driver decision-making models, and rethink about technology-based interventions to encourage safer behaviors. We also welcome reviews, meta-analyses, and commentaries on these issues.
Specific topics include but are not limited to:
- Reevaluation or development of new models of driver performance and/or behavior
- Technology-based interventions to improve driver performance and/or behavior
- Using sensor-based methods (e.g., physiological sensors, contact-free sensing systems) to monitor driver's states.
- Challenges in driver-ADAS interaction.
- Trust and acceptance of ADAS and of feedback from in-vehicle driver monitoring technologies.
- Naturalistic driving studies with ADAS and/or in-vehicle driver monitoring technologies.
By sharing new empirical findings on driver capacities and decision making, the operation of ADAS, and innovative tools to monitor and infer the driver state, we hope to promote a stimulating and enriching exchange of ideas between researchers, agents of change, practitioners, and other interested parties in these fields.
