Safe Driving Distance and Speed for Collision Avoidance in Connected Vehicles

Modelling attention allocation and takeover performance in two-stage takeover system via a cognitive computational model: considering the role of multiple monitoring requests

Studies have demonstrated two-stage takeover systems' feasibility and advantages. However, existing cognitive models mainly focus on simulating drivers' performance in single-stage takeover systems, with limited insights into cognitive modelling of effects of monitoring requests (MRs) within two-stage takeover systems. This study constructed a cognitive computational model for two-stage takeover systems based on queueing network-adaptive control of thought rational (QN-ACTR) architecture. Our model aims to capture variations in drivers' attention allocation and takeover performance resulting from different MR experiences. Five components, representing distinct cognitive processes, were designed to closely align with drivers' behavioural patterns. This model was validated through an experiment using metrics such as percentage time in road-centre and takeover time. Results revealed significant concordance between the model predictions and experimental data, with R-squared ≥ 0.76, RMSE ≤ 0.41, and MAPE ≤ 15%. The findings of this work extended beyond the two-stage takeover system investigation to include human factor modelling.

Knowledge Development Trajectory of the Internet of Vehicles Domain Based on Main Path Analysis

The Internet of vehicles (IoV) is an Internet-of-things-based network in the area of transportation. It comprises sensors, network communication, automation control, and data processing and enables connectivity between vehicles and other objects. This study performed main path analysis (MPA) to investigate the trajectory of research regarding the IoV. Studies were extracted from the Web of Science database, and citation networks among these studies were generated. MPA revealed that research in this field has mainly covered media access control, vehicle-to-vehicle channels, device-to-device communications, layers, non-orthogonal multiple access, and sixth-generation communications. Cluster analysis and data mining revealed that the main research topics related to the IoV included wireless channels, communication protocols, vehicular ad hoc networks, security and privacy, resource allocation and optimization, autonomous cruise control, deep learning, and edge computing. By using data mining and statistical analysis, we identified emerging research topics related to the IoV, namely blockchains, deep learning, edge computing, cloud computing, vehicular dynamics, and fifth- and sixth-generation mobile communications. These topics are likely to help drive innovation and the further development of IoV technologies and contribute to smart transportation, smart cities, and other applications. On the basis of the present results, this paper offers several predictions regarding the future of research regarding the IoV.

Dashcam video footage-based analysis of microsleep-related behaviors in truck collisions attributed to falling asleep at the wheel

OBJECTIVE

With the rapid spread of dashcams, many car accidents have been recorded; however, behavioral approaches using these dashcam video footage have not been sufficiently examined. We employed dashcam video footage to evaluate microsleep-related behaviors immediately prior to real-world truck collisions in professional drivers to explore a new solution to reduce collisions attributed to falling asleep at the wheel.

METHODS

In total, 3,120 s of video footage (60 s/case × 52 cases) from real-world truck collisions of 52 professional drivers obtained from interior and exterior dashcams were used and visually analyzed in a second-by-second manner to simultaneously evaluate any eye changes and microsleep-related behaviors (the driver's anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior) during driving.

RESULTS

Assessment of the frequency of occurrence of each item of microsleep-related behavior in the 52 collisions revealed that the item "touching" in terms of anti-sleepiness behavior, "absence of body movement" in terms of behavioral signs of microsleep, and "inappropriate line crossing" in terms of abnormal vehicle behavior were observed at the highest rate in all drivers (46.2%, 75.0%, and 78.8%, respectively). Decreases in anti-sleepiness behavior coincided with increases in behavioral signs of microsleep and abnormal vehicle behavior, with collisions occurring within approximately 40 s of these changes. Collisions were more common among young people and in the early morning and evening.

CONCLUSION

Our dashcam video footage-based analysis in truck collisions attributed to falling asleep at the wheel revealed the process of changes in microsleep-related driver and vehicle behaviors, classified as anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior. Based on these findings, to prevent collisions caused by falling asleep at the wheel, it is crucial to monitor not only the driver's eyes, but also the driver's whole body and vehicle behavior simultaneously to reliably detect microsleep-related behaviors.