arXiv subjects

Noah Goodall

Publications and source records attributed to Noah Goodall.

6 recordsLinked to original sources

arXiv2024

Methods to Estimate Advanced Driver Assistance System Penetration Rates in the United States

Advanced driver assistance systems (ADAS) are increasingly prevalent in the vehicle fleet, significantly impacting safety and capacity. Transportation agencies struggle to plan for these effects as ADAS availability is not tracked in vehicle registration databases. This paper examines methods to leverage existing public reports and databases to estimate the proportion of vehicles equipped with or utilizing Levels 1 and 2 ADAS technologies in the United States. Findings indicate that in 2022, between 8% and 25% of vehicles were equipped with various ADAS features, though actual usage rates were lower due to driver deactivation. The study proposes strategies to enhance estimates, including analyzing crash data, expanding event data recorder capabilities, conducting naturalistic driving studies, and collaborating with manufacturers to determine installation rates.

cs.CY↗

arXiv2024

Large-Area Emergency Lockdowns with Automated Driving Systems

Region-wide restrictions on personal vehicle travel have a long history in the United States, from riot curfews in the late 1960s, to travel bans during snow events, to the 2013 shelter-in-place "lockdown" during the search for the perpetrator of the Boston Marathon bombing. Because lockdowns require tremendous resources to enforce, they are often limited in duration or scope. The introduction of automated driving systems may allow governments to quickly and cheaply effect large-area lockdowns by jamming wireless communications, spoofing road closures on digital maps, exploiting a vehicle's programming to obey all traffic control devices, or coordinating with vehicle developers. Future vehicles may lack conventional controls, rendering them undrivable by the public. As travel restrictions become easier to implement, governments may enforce them more frequently, over longer durations and wider areas. This article explores the practical, legal, and ethical implications of lockdowns when most driving is highly automated, and provides guidance for the development of lockdown policies.

cs.CY↗

arXiv2023

A Note on Tesla's Revised Safety Report Crash Rates

Between June 2018 and December 2022, Tesla released quarterly safety reports citing average miles between crashes for Tesla vehicles. Prior to March 2021, crash rates were categorized as 1) with their SAE Level 2 automated driving system Autopilot engaged, 2) without Autopilot but with active safety features such as automatic emergency braking, and 3) without Autopilot and without active safety features. In January 2022, Tesla revised past reports to reflect their new categories of with and without Autopilot engaged, in addition to making small adjustments based on recently discovered double counting of reports and excluding previously recorded crashes that did not meet their thresholds of airbag or active safety restraint activation. The revisions are heavily biased towards no-active-safety-features$\unicode{x2014}$a surprising result given prior research showing that drivers predominantly keep most active safety features enabled. As Tesla's safety reports represent the only national source of Level 2 advanced driver assistance system crash rates, clarification of their methods is essential for researchers and regulators. This note describes the changes and considers possible explanations for the discrepancies.

cs.CY↗

arXiv2023

Evaluation of Crowdsourced Data on Unplowed Roads

Transportation agencies routinely collect weather data to support maintenance activities. With the proliferation of smartphones, many agencies have begun using crowdsourced data in operations. This study evaluates a novel unplowed roads dataset from the largest crowdsourced transportation data provider Waze. User-reported unplowed roads in Virginia were compared to national and state weather data for accuracy, and found 81% of reports were near known snow events, with false positives occurring at a regular rate of approximately 10 per day statewide. Reports were largely located on primary roads, limiting the usefulness for transportation agencies who may be most concerned with poorly monitored secondary roads. An effort to encourage unplowed road reporting in Waze through targeted messages on social media did not increase participation. Low reporting may be due to the feature's novelty, recent mild winters, or COVID-19 school and business closures.

cs.CY↗

arXiv2023

Comparability of Automated Vehicle Crash Databases

Introduction: This paper reviewed current driving automation (DA) and baseline human-driven crash databases and evaluated their comparability. Method: Five sources of DA crash data and three sources of human-driven crash data were reviewed for consistency of inclusion criteria, scope of coverage, and potential sources of bias. Alternative methods to determine vehicle automation capability using vehicle identification number (VIN) from state-maintained crash records were also explored. Conclusions: Evaluated data sets used incompatible or nonstandard minimum crash severity thresholds, complicating crash rate comparisons. The most widely-used standard was "police-reportable crash," which itself has different reporting thresholds among jurisdictions. Although low- and no-damage crashes occur at greater frequencies and have more statistical power, they were not consistently reported for automated vehicles. Crash data collection can be improved through collection of driving automation exposure data, widespread collection of crash data form electronic data recorders, and standardization of crash definitions. Practical Applications: Researchers and DA developers may use this analysis to conduct more thorough and accurate evaluations of driving automation crash rates. Lawmakers and regulators may use these findings as evidence to enhance data collection efforts, both internally and via new rules regarding electronic data recorders.

cs.CY↗

arXiv2020

Ethical Decision Making During Automated Vehicle Crashes

Automated vehicles have received much attention recently, particularly the DARPA Urban Challenge vehicles, Google's self-driving cars, and various others from auto manufacturers. These vehicles have the potential to significantly reduce crashes and improve roadway efficiency by automating the responsibilities of the driver. Still, automated vehicles are expected to crash occasionally, even when all sensors, vehicle control components, and algorithms function perfectly. If a human driver is unable to take control in time, a computer will be responsible for pre-crash behavior. Unlike other automated vehicles--such as aircraft, where every collision is catastrophic, and guided track systems, which can only avoid collisions in one dimension--automated roadway vehicles can predict various crash trajectory alternatives and select a path with the lowest damage or likelihood of collision. In some situations, the preferred path may be ambiguous. This study investigates automated vehicle crashing and concludes the following: (1) automated vehicles will almost certainly crash, (2) an automated vehicle's decisions preceding certain crashes will have a moral component, and (3) there is no obvious way to effectively encode complex human morals in software. A three-phase approach to developing ethical crashing algorithms is presented, consisting of a rational approach, an artificial intelligence approach, and a natural language requirement. The phases are theoretical and should be implemented as the technology becomes available.

cs.CY↗