This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing.

Traumatic brain injuries are among the most common severely disabling injuries in the United States. Construction helmets are considered essential personal protective equipment for reducing traumatic brain injury risks at work sites. In this study, we proposed a practical finite element modeling approach that would be suitable for engineers to optimize construction helmet design. The finite element model includes all essential anatomical structures of a human head (i.e. skin, scalp, skull, cerebrospinal fluid, brain, medulla, spinal cord, cervical vertebrae, and discs) and all major engineering components of a construction helmet (i.e. shell and suspension system). The head finite element model has been calibrated using the experimental data in the literature. It is technically difficult to precisely account for the effects of the neck and body mass on the dynamic responses, because the finite element model does not include the entire human body. An approximation approach has been developed to account for the effects of the neck and body mass on the dynamic responses of the head–brain. Using the proposed model, we have calculated the responses of the head–brain during a top impact when wearing a construction helmet. The proposed modeling approach would provide a tool to improve the helmet design on a biomechanical basis.

It is important to rigorously and comprehensively evaluate the safety of Automated Vehicles (AVs) before their production and deployment. A popular AV evaluation approach is Naturalistic-Field Operational Test (N-FOT) which tests prototype vehicles directly on public roads. Due to the low exposure to safety-critical scenarios, N-FOTs is time-consuming and expensive to conduct. Computer simulations can be used as an alternative to N-FOTs, especially in terms of generating motions of the surrounding traffic. In this paper, we propose an accelerated evaluation approach for AVs. Human-controlled vehicles (HVs) were modeled as disturbance to AVs based on data extracted from the Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behavior, which amplifies riskier testing scenarios while reserves its statistical information so that the safety benefits of AV in non-accelerated cases can be accurately estimated. An AV model based on a production vehicle was tested. Results show that the proposed method can accelerate the evaluation process by at least 100 times.

The finite element (FE) method has been widely used to investigate the mechanism of traumatic brain injuries (TBIs), because it is technically difficult to quantify the responses of the brain tissues to the impact in experiments. One of technical challenges to build a FE model of a human head is the modeling of the cerebrospinal fluid (CSF) of the brain. In the current study, we propose to use membrane elements to construct the CSF layer. Using the proposed approach, we demonstrate that a head model can be built by using existing meshes available in commercial databases, without using any advanced meshing software tool, and with the sole use of native functions of the FE package Abaqus. The calculated time histories of the intracranial pressures at frontal, posterior fossa, parietal, and occipital positions agree well with the experimental data and the simulations in the literature, indicating that the physical effects of the CSF layer have been accounted for in the proposed modeling approach. The proposed modeling approach would be useful for bioengineers to solve practical problems.