Browsing by Author "Li, Eric"

Now showing 1 - 6 of 6
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    Autonomous Delivery Vehicle as a Disruptive Technology: How to Shape the Future with a Focus on Safety?
    Das, Subasish; Tsapakis, Ioannis; Wei, Zihang; Elgart, Zachary; Kutela, Boniphace; Vierkant, Valerie; Li, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-09)
    The National Highway Traffic Safety Administration recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs) on roadways. Although the mobility of ADVs is limited to low-speed roads and these vehicles are occupantless, frequent stops and mobility among residential neighborhoods cause safety- related concerns. There is consequently a need for a comprehensive safety impact analysis of ADVs. This study examined the safety implications and safety impacts of ADVs by using novel approaches. This research prepared several datasets such as fatal crash data, aggregated ADV trips and trajectories, and real-world crash data from the scenario design for an ADV-related operational design domain. Association rules mining was applied to the datasets to identify significant patterns. This study generated a total of 80 association rules that provide risk patterns associated with ADVs. The rules can be used as prospective benchmarks to examine how rule-based risk patterns can be reduced by ADVs that replace human-driven trips.
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    Safety and Crash Risks with Vehicle Strings
    Li, Eric; Gibbons, Ronald B.; Kim, Bumsik (National Surface Transportation Safety Center for Excellence, 2022-11-03)
    In the course of many previous studies on nighttime safety, the research team came to believe that there is a potential safety benefit of cars traveling in stabilized strings during free-flow conditions. Recent technical advances in vehicle-to-vehicle communications open the opportunity for vehicle safety systems to share information about driving conditions that can be used to improve safety. This project seeks to understand the safety implications of vehicles operating in strings to inform how effective cooperative driving might be at improving driving safety. In terms of driver behavior, the study revealed the following by comparing vehicles with and without a leading vehicle. For driver behavioral differences at freeway ramp locations, overall, the study showed that drivers following other vehicles tended to travel at lower speeds than those who did not follow another vehicle. In addition to the lower speeds, however, vehicles with a leading vehicle frequently showed higher acceleration activity, seemingly suggesting that they were adjusting speed relative to the leading vehicles. In addition, the study also revealed that, during daytime, significant driver behavioral differences between vehicles with leading vehicles and those without leading vehicles were more evident at entrance ramp locations. Significant differences during nighttime tended to be more evident at both entrance and exit ramp locations but only for the analysis segments that are farther away from the ramp junction. For driver behavior differences at intersections, similarly, the intersection analysis showed that drivers following other vehicles tended to travel slower, but with higher acceleration variance and jerk. These behaviors are likely due to the drivers needing to adjust speed more when following other vehicles. Drivers might also switch lanes and/or turn faster at intersections due to greater confidence about the roadway condition and/or fewer interactions with other vehicles when traveling in strings.
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    Safety Countermeasures at Unsignalized Intersections – A Toolbox Approach
    Li, Eric; Medina, Alejandra; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2020-06-23)
    In 2015, approximately 8,000 intersection and intersection-related fatal crashes occurred on the nation’s highway system, resulting in more than 8,400 fatalities. That death toll represented about 24% of the traffic-related deaths across the country. Combining fatalities and injuries, intersection and intersection-related crashes represent more than 50% of the traffic-related injuries across the nation. Unsignalized intersections are of particular concern. Between 2010 and 2014, unsignalized intersections were responsible for more than 70% of the intersection and intersection-related fatalities. This report documents 83 suitable safety countermeasures that can be used at unsignalized intersections to mitigate crash risks. A number of these have potential for cost-effective, systemic implementation, including LED-enhanced Stop signs, retroreflective panels on sign posts, center line pavement markings in a median crossing, center line pavement markings on the minor road approach, and installation of intersection lighting.
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    Traffic Sign Characteristics for Machine Vision Safety Benefits
    Kassing, Andrew; Gibbons, Ronald B.; Li, Eric; Palmer, Matthew; Hamen, Johann; Medina, Alejandra (National Surface Transportation Safety Center for Excellence, 2024-07-03)
    Machine vision has become a central technology for the development of automated driving systems and advanced driver assistance systems. To support safe navigation, machine vision must be able to read and interpret roadway signs, which provide regulatory, warning, and guidance information for all road users. Complicating this task, transportation agencies use a large variety of signs, which can have significantly different shapes, sizes, contents, installation methods, and retroreflectivity levels. Additionally, many environmental factors, such as precipitation, fog, dew, and lighting, also affect the visibility and legibility of roadway signs. Understanding how environmental factors and sign conditions affect machine vision performance will be important for transportation agencies to maximize the technology’s safety benefits. Research began by conducting a literature review cataloguing current research concerning roadway sign and visual performance, vehicle vision systems, and sign significance for automated driving. Information and insight gained during the literature review process informed the design and system development of data collection systems. Field data collection was then performed over the course of 3 months in late spring to early summer in 2021. Simultaneously, sign data were harvested using Google Street View and mapped using ArcGIS. Data collected during the experimental trips were then reduced and carefully prepared for analysis. Researchers conducted a thorough data analysis, particularly looking at sign location, viewing distance, sign color, font size, sun position, and illumination, to assess the impact of many environmental and infrastructure factors on the legibility of sign characters. Results showed that blue and brown signage with white legend text provided the best chance of sign character legibility during the daytime; sign characters were easy to read during the day at all three experimental distances (200, 400, and 500 ft), with small characters becoming less legible as view distance increased; daytime legibility decreased as light levels decreased; sign images captured at nighttime illumination levels had poor legibility results; sign characters on overhead signage were found to be more legible and are expected to be identified at a higher rate by vehicle vision systems; and vehicle vision systems should use a high-quality camera capable of taking pictures at night without motion blur.
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    Understanding Crashes Involving Roadway Objects with SHRP 2 Naturalistic Driving Study Data
    Li, Eric; Hao, Haiyang; Gibbons, Ronald B.; Medina, Alejandra (National Surface Transportation Safety Center for Excellence, 2023-03-08)
    This project used the second Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS) data as an alternate data source to police-reported crash data to better understand roadway object crashes. The objectives included determining crash causation, recommending strategies for crash prevention, and understanding the implications for highly automated vehicles (HAVs). Researchers addressed these objectives with a three-pronged approach: (1) a detailed engineering study of roadway object events to identify and quantify effects of a large number of relevant variables; (2) a machine-vision-oriented study to document the implications of roadway object events on machine vision performance; and (3) a detailed case study analysis of representative roadway object events to provide further qualitative results on how and why roadway object crashes occur and what potential actions can prevent such events effectively.
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    Use of Disruptive Technologies to Support Safety Analysis and Meet New Federal Requirements
    Tsapakis, Ioannis; Das, Subasish; Khodadadi, Ali; Lord, Dominique; Morris, Jessica; Li, Eric (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-03)
    States are required to have access to annual average daily traffic (AADT) for all public paved roads, including non-federal aid system (NFAS) roadways. The expectation is to use AADT estimates in data-driven safety analysis. Because collecting data on NFAS roads is financially difficult, agencies are interested in exploring affordable ways to estimate AADT. The goal of this project was to determine the accuracy of AADT estimates developed from alternative data sources and quantify the impact of AADT on safety analysis. The researchers compared 2017 AADT data provided by the Texas and Virginia Departments of Transportation against probebased AADT estimates supplied by StreetLight Data Inc. Further, the research team developed safety performance functions (SPFs) for Texas and Virginia and performed a sensitivity analysis to determine the effects of AADT on the results obtained from the empirical Bayes method that uses SPFs. The results showed that the errors stemming from the probe AADT estimates were lower than those reported in a similar study that used 2015 AADT estimates. The sensitivity analysis revealed that the impact of AADT on safety analysis mainly depends on the size of the network, the AADT coefficients, and the overdispersion parameter of the SPFs.