Pedal misapplication (PM) is an error in which a driver unintentionally presses the wrong pedal. When drivers mistake the accelerator pedal for the brake pedal, the vehicle experiences a sudden unintended acceleration, and the consequences can be severe. A brief history of PM is covered, and several novel studies of PM are described. The goals of these studies were as follows:
1. Identify and analyze multiple samples of PM crashes from a variety of data sources using both established and novel methods to gain new insight into the characteristics and frequency of PM crashes.
2. Use the confirmed, real-world PM crash data to develop a custom vehicle dynamics simulation and evaluate the overall potential safety benefit of a theoretical PM advanced driver assistance system.
Using an established keyword search identification method and two unique crash datasets, a PM crash frequency of approximately 0.2% of all crashes was found. These PM crashes were typically rear-end or road departure crashes in moderate- to low-speed commercial or residential areas. Female drivers and elderly drivers were more often involved in these PM crashes, which generally featured slightly lower injury severities and often involved inattention or fatigue. Anecdotally, PM crash narratives contained repeated evidence of unexpected events, driver inexperience, distraction, shoe-malfunction, extreme stress, and medical conditions/emergencies. A novel PM crash identification algorithm was developed to detect PMs from time-series pre-crash data. This algorithm was applied to a sample of crashes with event data recorder data available, and a frequency of 4.3% of eligible crashes were found to have exhibited PM behavior, suggesting that PM crashes may be more prevalent than previously thought. While the data from these crashes suggested that a PM occurred, this dataset lacked sufficient data regarding driver intention, which is necessary to confirm each crash as PMs. The characteristics of these PM-like crashes were analyzed and found to be largely similar to those of previous samples, with notable exceptions for higher proportions of male drivers, higher travel speeds, and higher maximum injury severities. More robust data from a naturalistic driving study (NDS) was acquired, and the novel algorithm was applied to all of the sample’s eligible crashes. Because the NDS data contained more data elements such as driver-facing video, crashes that exhibited PM behavior were individually inspected to confirm PM. This produced a PM crash frequency of 1.1%. The characteristics of these confirmed PM crashes were investigated, but a small sample size limits the generalizability of the results. Lastly, crash data from confirmed, real-world PM crashes was used to inform a custom vehicle dynamics model into which a theoretical PM advanced driver assistance system was simulated. The effect of the accelerator suppression system on crash avoidance and mitigation was evaluated to assess its potential safety benefit, which was found to be highly dependent on system threshold values and largely underwhelming in the absence of supplemental braking. The results indicated that a system that detected PM, suppressed acceleration, and applied braking could provide a substantially higher safety benefit. / M.S. / Pedal misapplication (PM) occurs when a driver presses the wrong pedal. When drivers mistake the accelerator pedal for the brake pedal, the vehicle experiences a sudden unintended acceleration, and the consequences can be severe. A history of the controversial subject of PM is covered, and several novel studies of PM are described. In these studies, PM crashes are identified among documented real-world crashes. This is done in three phases: (1) using narratives written by law-enforcement officers or crash investigators, (2) using event data recorders, or “black boxes,” that store vehicle data prior to crashes, and (3) using naturalistic driving study data, including video recordings of subjects during daily driving. These data are analyzed to develop the understanding of how often PM crashes occur and what factors are common among them. It is discovered that the frequency of PM crashes may be an order of magnitude greater than previously estimated. In the final study, real-world PM crash data is used to virtually reconstruct PM crashes and apply an advanced driver assistance system designed to detect PM, suppress the accelerator input, and reduce the severity of the crash or prevent it altogether. By simulating a wide range of system variations, we develop a sense of the feasibility of such a system’s implementation and overall safety benefit.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/113053 |
| Date | January 2022 |
| Creators | Smith, Colin P. |
| Contributors | Biomedical Engineering and Mechanics, Doerzaph, Zachary R., Perez, Miguel A., Riexinger, Luke E., Gabler, Hampton C. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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