Automated Vehicles and Pedestrian Safety: Exploring the Promise and Limits of Pedestrian Detection.

INTRODUCTION: U.S. pedestrian fatalities have risen recently, even as vehicles are equipped with increasingly sophisticated safety and crash avoidance technology. Many experts expect that advances in automated vehicle technology will reduce pedestrian fatalities substantially through eliminating crashes caused by human error. This paper investigates automated vehicles' potential for reducing pedestrian fatalities by analyzing nearly 5,000 pedestrian fatalities recorded in 2015 in the Fatality Analysis Reporting System, virtually reconstructing them under a hypothetical scenario that replaces involved vehicles with automated versions equipped with state-of-the-art (as of December 2017) sensor technology. METHODS: This research involved the following activities: (1) establish functional ranges of state-of-the-art pedestrian sensor technologies, (2) use data from the Fatality Analysis Reporting System to identify pedestrian fatalities recorded in each state in the U.S. and District of Columbia in 2015, and (3) assess the maximum numbers of pedestrian fatalities that could have been avoided had involved vehicles been replaced with autonomous versions equipped with the described sensors. The research was conducted from July to December 2017. RESULTS: Sensors' abilities to detect pedestrians in advance of fatal collisions vary from <30% to >90% of fatalities. Combining sensor technologies offers the greatest potential for eliminating fatalities, but may be unrealistically expensive. Furthermore, whereas initial deployment of automated vehicles will likely be restricted to freeways and select urban areas, non-freeway streets and rural settings account for a substantial share of pedestrian fatalities. CONCLUSIONS: Although technologies are being developed for automated vehicles to successfully detect pedestrians in advance of most fatal collisions, the current costs and operating conditions of those technologies substantially decrease the potential for automated vehicles to radically reduce pedestrian fatalities in the short term.

Evaluation of an augmented virtual reality and haptic control interface for psychomotor training.

This study investigated the design of a virtual reality (VR) simulation integrating a haptic control interface for motor skill training. Twenty-four healthy participants were tested and trained in standardized psychomotor control tasks using native and VR forms with their nondominant hands in order to identify VR design features that might serve to accelerate motor learning. The study was also intended to make preliminary observations on the degree of specific motor skill development that can be achieved with a VR-based haptic simulation. Results revealed significant improvements in test performance following training for the VR with augmented haptic features with insignificant findings for the native task and VR with basic haptic features. Although performance during training was consistently better with the native task, a correspondence between the VR training and test task interfaces led to greater improvement in test performance as reported by a difference between baseline and post-test scores. These findings support use of VR-based haptic simulations of standardized psychomotor tests for motor skill training, including visual and haptic enhancements for effective pattern recognition and discrete movement of objects. The results may serve as an applicable guide for design of future haptic VR features.

Comparison of Infant Car Seat grip orientations and lift strategies.

The rear-facing Infant Car Seat (ICS) is designed to meet federal requirements for transporting children less than 1 year old. Typical use includes transfer in and out of a vehicle, which is shown to be a difficult lift. Despite the frequency of this lift, manufacturers provide little guidance for users. Review of relevant literature suggested an ICS featuring an angled handle, promoting a neutral wrist posture, would increase grip stability and decrease lifting effort. Popular press suggested a foot-in-car stance for the ICS lift would do the same. An experiment was conducted in which wrist deviations from neutral posture were recorded along with lifting muscle activation levels (multiple flexor muscles and biceps brachii) and overall perceived exertion for straight versus a new bent handle design and conventional stance versus foot-in-car. Foot position was examined to test the recommendations in the popular press. Surprisingly, wrist deviation was not significantly affected by the new bent handle design (due to compensatory behavior with the straight handle) but was related to foot placement (p=0.04). Results revealed the bent handle to significantly reduce flexor activation compared with the straight handle (p=0.0003); however, the level of biceps activation increased. Biceps activation also significantly increased for foot-in-car stance (p=0.035) but not flexor activation. In general, the bent handle enabled the user to lift the ICS with a steadier grip and less effort.

Adaptive automation of human-machine system information-processing functions.

The goal of this research was to describe the ability of human operators to interact with adaptive automation (AA) applied to various stages of complex systems information processing, defined in a model of human-automation interaction. Forty participants operated a simulation of an air traffic control task. Automated assistance was adaptively applied to information acquisition, information analysis, decision making, and action implementation aspects of the task based on operator workload states, which were measured using a secondary task. The differential effects of the forms of automation were determined and compared with a manual control condition. Results of two 20-min trials of AA or manual control revealed a significant effect of the type of automation on performance, particularly during manual control periods as part of the adaptive conditions. Humans appear to better adapt to AA applied to sensory and psychomotor information-processing functions (action implementation) than to AA applied to cognitive functions (information analysis and decision making), and AA is superior to completely manual control. Potential applications of this research include the design of automation to support air traffic controller information processing.