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The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial. Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving.

Field testing of Automatic Emergency Braking (AEB) systems using real actual heavy trucks and buses is unavoidably limited by the dangers and expenses inherent in crash-imminent scenarios. For this paper, a heavy vehicle is defined as having a gross vehicle weight rating (GVWR) that exceeds 4536 kg (10,000 lbs.). High fidelity Hardware-in-the-Loop (HiL) simulation systems have the potential to enable safe and accurate laboratory testing and evaluation of heavy vehicle AEB systems. This paper describes the setup and experimental validation of such a HiL simulation system. An instrumented Volvo tractor-trailer equipped with a Bendix Wingman Advanced System, including the FLR20 forward looking radar and AEB system, was put through a battery of different types of track tests to benchmark the AEB performance.

The objective of this study was to determine response characteristics and injury of the shoulder due to lateral impacts. The need for this data was heightened in the 1990s with increasing interest in harmonization of side impact standards, and questions regarding the measurement capabilities of dummies used in evaluating side impacts. A pneumatic impacting ram was employed in carrying out twenty-two lateral impacts to eleven unembalmed human cadavers at the level of the glenohumeral joint. Velocity of the ram at the time of impact was varied throughout the impacts from 3.5 to 7.0 m/sec, in an attempt to determine injury threshold. The cadavers were instrumented with tri-axial accelerometer blocks at ten locations in the shoulder region. Bony structures instrumented included the sternum, the first thoracic vertebra (T1), clavicles and scapulae. Output from the accelerometers was utilized to calculate impact forces and to examine the movement of the instrumented structures.

This research focused on developing a methodology for a vehicle dynamics model of a passenger vehicle outfitted with an aftermarket Automated Driving System software package using only literature and track based results. This package consisted of Autoware.AI (Autoware ®) operating on Robot Operating System 1 (ROS™) with C++ and Python ®. Initial focus was understanding the basics of ROS and how to implement test scenarios in Python to characterize the control systems and dynamics of the vehicle. As understanding of the system continued to develop, test scenarios were adapted to better fit system characterization goals with identification of system configuration limits. Trends from on-track testing were identified and paired with first-order linear systems to simulate physical vehicle responses to given command inputs. Sub-models were developed and simulated in MATLAB ® with command inputs from on-track testing.