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Current finite element (FE) human body models (HBMs) generally only represent young and mid-size male occupants and do not account for body shape and composition variations among the population. Because it generally takes several years to build a whole-body HBM, a method to rapidly develop HBMs with a wide range of human attributes (size, age, obesity level, etc.) is critically needed. Therefore, the objective of this study was to evaluate the feasibility of using a mesh morphing method to rapidly generate skeleton and whole-body HBMs based on statistical geometry targets developed previously. THUMS V4.01 mid-size male model jointly developed by Toyota Motor Corporation and Toyota Central R&D Labs was used in this study as the baseline HBM to be morphed. Radial basis function (RBF) was used to morph the baseline model into the target geometries.

To help predict the injury responses of child pedestrians and occupants in traffic incidents, finite element (FE) modeling has become a common research tool. Until now, there was no whole-body FE model for 10-year-old (10 YO) children. This paper introduces the development of two 10 YO whole-body pediatric FE models (named CHARM-10) with a standing posture to represent a pedestrian and a seated posture to represent an occupant with sufficient anatomic details. The geometric data was obtained from medical images and the key dimensions were compared to literature data. Component-level sub-models were built and validated against experimental results of post mortem human subjects (PMHS). Most of these studies have been mostly published previously and briefly summarized in this paper. For the current study, focus was put on the late stage model development.

Model Predictive Control (MPC) design methods are becoming popular among automotive control researchers because they explicitly address an important challenge faced by today’s control designers: How does one realize the full performance potential of complex multi-input, multi-output automotive systems while satisfying critical output, state and actuator constraints? Nonlinear MPC (NMPC) offers the potential to further improve performance and streamline the development for those systems in which the dynamics are strongly nonlinear. These benefits are achieved in the MPC framework by using an on-line model of the controlled system to generate the control sequence that is the solution of a constrained optimization problem over a receding horizon.

The effectiveness of Forward Collision Warning (FCW) or similar crash warning/mitigation systems is highly dependent on driver acceptance. If a FCW system delivers the warning too early, it may distract or annoy the driver and cause them to deactivate the system. In order to design a system activation threshold that more closely matches driver expectations, system designers must understand when drivers would normally apply the brake. One of the most widely used metrics to establish FCW threshold is Time to Collision (TTC). One limitation of TTC is that it assumes constant vehicle velocity. Enhanced Time to Collision (ETTC) is potentially a more accurate metric of perceived collision risk due to its consideration of vehicle acceleration. This paper compares and contrasts the distribution of ETTC and TTC at brake onset in normal car-following situations, and presents probability models of TTC and ETTC values at braking across a range of vehicle speeds.

As part of active safety systems for reducing bicyclist fatalities and injuries, Bicyclist Pre-Collision System (BPCS), also known as Bicyclist Autonomous Emergency Braking System, is being studied currently by several vehicles manufactures. This paper describes the development of a surrogate bicyclist which includes a surrogate bicycle and a surrogate bicycle rider to support the development and evaluation of BPCS. The surrogate bicycle is designed to represent the visual and radar characteristics of real bicyclists in the United States. The size of bicycle surrogate mimics the 26 inch adult bicycle, which is the most popular adult bicycle sold in the US. The radar cross section (RCS) of the surrogate bicycle is designed based on RCS measurement of the real adult sized bicycles.

In 2010, 32,855 fatalities and over 2.2 million injuries occurred in automobile crashes, not to mention the immense economic impact on our society. Two of the four most frequent types of crashes are rear-end and lane departure crashes. In 2011, rear-end crashes accounted for approximately 28% of all crashes while lane departure crashes accounted for approximately 9%. This paper documents a study on the NADS-1 driving simulator to support the development of driver behavior modeling. Good models of driver behavior will support the development of algorithms that can detect normal and abnormal behavior, as well as warning systems that can issue useful alerts to the driver. Several scenario events were designed to fill gaps in previous crash research. For example, previous studies at NADS focused on crash events in which the driver was severely distracted immediately before the event. The events in this study included a sample of undistracted drivers.

This paper presents real-world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real-world driving cycle for assessing potential energy savings for connected and automated vehicle (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge.

Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles.

Lane departure warning (LDW) systems can detect an impending road departure and deliver an alert to allow the driver to steer back to the lane. LDW has great potential to reduce the number of road departure crashes, but the effectiveness is highly dependent upon driver acceptance. If the driver perceives there is little danger after receiving an alert, the driver may become annoyed and deactivate the system. Most current LDW systems rely heavily upon distance to lane boundary (DTLB) in the decision to deliver an alert. There is early evidence that in normal driving DTLB may be only one of a host of other cues which drivers use in lane keeping and in their perception of lane departure risk. A more effective threshold for LDW could potentially be delivered if there was a better understanding of this normal lane keeping behavior. The objective of this paper is to investigate the lane keeping behavior of drivers in normal driving.