Advanced driving assistance systems (ADAS) are designed to reduce potential crash risks and enhance driving safety. However, drivers’ interactions with ADAS may vary depending on their individual driving styles and characteristics. This study proposes a novel approach to classifying driving styles and explores how age and gender affect interactions with ADAS. The study utilized two naturalistic driving data sets comprising 148 drivers from four age groups: teens; younger adults; middle-aged adults; and older adults. Data were collected during two periods: baseline (without ADAS); and treatment (with ADAS). First, the K-means clustering algorithm was employed to divide trips into one conservative and two aggressive groups based on three driving behavior metrics: tailgating; speeding; and lane-changing. The aggressive-trip ratios were then calculated for each driver during each of the two periods. The Bayesian Gaussian mixture model was applied to determine the threshold values of the aggressive-trip ratios to classify drivers as conservative, moderate, or aggressive during each period. This allowed for identifying changes in driving style upon the activation of ADAS. The subsequent multinomial logistic regression model results showed that driving styles vary across age groups, with teens being the most aggressive drivers. Certain changes in driving style were observed, with some conservative drivers becoming aggressive or moderate and some aggressive drivers becoming conservative or moderate, but these differences were statistically non-significant. The findings of this study indicate that warning-based ADAS may not elicit significant changes in driving style, particularly among teenage drivers who are consistently the most aggressive drivers.

Electrocardiography (ECG) is widely recognized as the gold standard for measuring heart rate (HR) and heart rate variability (HRV). However, photoplethysmography (PPG) presents notable advantages in terms of wearability, affordability, and ease of integration into consumer devices, despite its susceptibility to motion artifacts and the absence of standardized processing protocols. In this study, we review current ECG and PPG signal processing methods and propose a signal quality assessment and reconstruction pipeline tailored for dynamic, in-vehicle environments. This pipeline was evaluated using data gathered from participants riding in an automated vehicle. Our findings demonstrate that while blood volume pulse (BVP) derived from PPG can provide reliable heart rate estimates and support extraction of certain HRV features, its utility in accurately capturing high-frequency HRV components remains constrained due to motion-induced noise and signal distortion. These results underscore the need for caution in interpreting PPG-derived HRV, particularly in mobile or ecologically valid contexts, and highlight the importance of establishing best practices and robust preprocessing methods to enhance the reliability of PPG sensing for field-based physiological monitoring.

A third of large truck crashes are associated with driver-related factors, especially speeding. This study aimed to examine the impact of behavior-based safety (BBS) programs on speeding. Speeding data were examined from a trucking fleet that had incorporated a BBS program using in-vehicle data recorders (IVDR) and post hoc feedback. Speeding events were examined over 37 weeks in two stages—an initial 4-week period (Stage 1), and the final 30 weeks (Stage 2). In Stage 1, data were collected without any feedback. In Stage 2, a subset of the drivers received feedback. A cluster analysis was performed based on the speeding event rate from Stage 1. The analysis yielded two clusters per group based on risk. The higher-risk cluster contained fewer drivers and showed a greater reduction in speeding with the BBS program, compared to the lower-risk cluster. Both clusters showed significant decreases in speeding across Stage 2. The BBS program was associated with reduced speeding, with a more pronounced reduction for the higher-risk drivers, highlighting the role of BBS programs in trucking and underscoring the importance of driver sub-groups. Targeted safety approaches may be more efficient and yield higher safety benefits than a one-size fits all approach.

Automated vehicles (AVs) are of great potential to reduce crashes on the road. However, it is still challenging to eliminate all the possible crashes, especially those with vulnerable road users (VRUs). Thus, in this paper, we conducted a systematic review of challenging scenarios involving AVs and VRUs. We identified 39 relevant papers in the literature and categorized the challenging scenarios into three categories: human factors, environmental factors, and vehicle factors. For each category, we summarized the development, challenges, and potential solutions from the literature. Our findings aim to enhance the safety of VRUs when interacting with AVs, and multiple stakeholders must collaborate to 1) improve AI and sensor technologies and vehicle automation, 2) redesign the current transportation infrastructure, 3) design effective communication technologies and interfaces between vehicles, and between vehicles and VRUs, and 4) design effective simulation and testing methods to support and evaluate both infrastructure and technologies.

A common maneuver drivers perform and experience on the road is changing lanes. Autonomous vehicles are required to engage a lane change safely and to react to the other road users’ lane changes. To develop autonomous vehicles that change lanes or respond to the lead vehicle’s lane changes in a safe and human-like way, one should investigate the factors that affect human driver responses. By reviewing the literature to identify potential factors, this study extracted these factors from a naturalistic driving data set and associated them with driver deceleration and acceleration responses to the lead vehicle’s cut-in and cut-out to develop predictive models for the impact of the events on traffic flow. After the events were verified as accurate, the variables associated with the events, including range, range rate, speed, lateral position in the lane, and average acceleration were analyzed using logistic regression, support vector machines (SVM), and two forms of decision trees. In total, 799 cut-in events and 684 cut-out events with the necessary variables were applied for analysis. The significant variables influencing driver behavior were found, and using these variables, the predictive models achieved around 80% accuracy for cut-ins, and 73% accuracy for cut-outs on test data. These results will assist in the future design of autonomous vehicle control to minimize detrimental effects on traffic when changing lanes and safe longitudinal control when responding to a lead vehicle’s lane changes, allowing for safe integration with human drivers, and better design of driver assistance systems.