Adapting Crash Modification Factors for the Connected and Autonomous Vehicle Environment

The Crash Modification Factor (CMF) clearinghouse can be used to estimate benefits for specific highway safety countermeasures. It assists safety professionals in the allocation of investments. The clearinghouse contains over 7000 entries of which only 446 are categorized as intelligent transportation systems or advanced technology, but none directly address connected or autonomous vehicles (CAVs). Further, the effectiveness of highway safety countermeasures is assumed to remain constant over time, an assumption that is particularly problematic as new technologies are introduced. For example, for the existing fleet of human-driven vehicles, installation of rumble strip can potentially reduce “run-off-road” crashes by 40%. If specific CAV technologies, e.g., lane-tracking, can work without rumble strips, and say, half of all cars are so equipped, only half of the fleet will benefit, reducing the benefits of rumble strips by a commensurate amount. Benefits of the two improvements, e.g., rumble strips and automated vehicles, should not be double-counted. As there will still be human-driven and/or non-connected vehicles in the fleet, conventional countermeasures are still necessary, although returns on conventional safety investments may be significantly overestimated. This is important as safety investments should be optimized and geared to future, not past fleets. Moreover, as CMFs are based on historical events, the types of crashes experienced by human-driven, un-connected cars are likely to be much different in the future. This research presents methods to estimate the safety benefits that autonomous vehicles have to offer and the changes needed in CMFs as a result of their adoption. This will primarily be achieved by modifying and enhancing a tool co-developed by the Fellow that estimates the safety benefits of different levels of autonomy. This tool, ddSAFCAT, estimates CAV safety benefits using real-world data for crashes, market penetration, and effectiveness

Evaluating Risks Associated With Automated Driving Systems

Many countries are already testing automated driving systems (ADS) on public roads. Since original equipment manufacturers (OEMs) and technology firms invest billions of dollars in research and development, technology evolution is expeditious. However, the uptake is not as rapid as was forecasted five years ago. One reason is that the fundamental concern is still unresolved: Are Autonomous Vehicles (AVs) safe enough? This unresolved question adds urgency and the necessity to understand when AVs can be considered acceptably safe. Contrary to manual-driven vehicles (MVs), the safety of AVs cannot be solely determined by design specifications. Instead, safety incorporates the AV’s brain and behaviour. Thus, it expands into a realm formerly concerning not to the vehicle but to the proficiency of human drivers to drive safely in mixed traffic. Moreover, the road transport ecosystem will include other vehicles-primarily human-driven and pedestrians, bicyclists, and other road users in the foreseeable future. Hence, AVs must be proficient at interacting with elements of traffic safely. To put it simply, AVs will be considered safe when they can drive safely (without mishap) in mixed traffic on public roads. This thesis aims to explore different frameworks and approaches to evaluate risks associated with automated driving systems. The aims of the research are fulfilled through three objectives: understanding AV data and identifying the safety-related features from the data, exploring and developing methodologies for investigating the safety, and recognising the limitations of AV data for examining safety. Chapters 1 and 2 are introduction and literature review, respectively. Chapter 3 of this thesis analyses disengagement and crash data from the California Department of Motor Vehicles (CA DMV) and develops a crash severity model. Chapter 4 and 5 conducts a comprehensive safety assessment of the connected automated vehicle (CAV) deployed mixed Freeway and Urban traffic network, respectively. Results indicate that CAVs can improve safety and network performance. However, Pareto-optimality between network performance and traffic safety is identified using various CAV behaviours. Finally, chapter 6 explores AV field data (Waymo Open Dataset) to investigate AV behaviour. The results suggest that the mere presence of AVs could help reduce sudden braking and improve overall stability in the traffic flow due to higher variance in reaction times. Finally, the last chapter summarises significant findings, prospects of future research and final remarks

A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports

Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domain

In Search of Severity Dimensions of Traffic Conflicts for Different Simulated Mixed Fleets Involving Connected and Autonomous Vehicles

-is study aims to estimate the severity of con3icts that may arise from the introduction of connected and automated vehicles (CAVs) by examining the vehicle paths generated by microsimulations of mixed 3eets of human-driven vehicles and CAVs with di9erent levels of automation (L1-L4 vehicles). -e study assesses the severity of con3icts using a holistic approach that considers three dimensions: (1) proximity to collision, via the time-to-collision (TTC) indicator; (2) potential consequences of a con3ict, via single surrogate safety measures such as maximum speed (MaxS) and vehicle speed di9erence (DeltaS); and (3) a combination of both dimensions to assign severity scores, via TTC and velocity vectors. -e study’s >ndings suggest that moderate penetration rates of L3 and L4 vehicles (35–55%) show signi>cant di9erences in the number of traAc con3icts with varying TTC values. Additionally, high penetration rates of L3 and L4 vehicles (above 55%) result in lower values of con3ict consequences measures such as MaxS and DeltaS. Furthermore, the study shows that con3ict consequences decrease if the follower is a L3 or L4 vehicle. -e study’s >ndings also reveal that there is a considerable reduction in high severity con3icts when the penetration rate of CAV levels reaches 50%, and the full operation of L4 vehicles results in a 75.5% reduction in high severity con3icts. -erefore, this study provides valuable insight into the potential severe con3icts during the transition period from manual vehicle operation to full CAV operation. Overall, the study’s >ndings highlight the importance of assessing the severity of potential con3icts arising from the introduction of CAVs. By considering the proximity to collision and the potential consequences of con3icts, the study provides a comprehensive assessment of the severity of con3icts. -is information can inform the development of policies and strategies to ensure the safe and responsible introduction of CAVs into our transportation systems.Spanish Government PID2019-110741RA-I00CRUE-CBUA Gol

Highway Safety and Traffic Flow Analysis of Mixed traffic with Connected and Non-Connected Vehicles

Safety is the number one issue in the deployment of any vehicle technology. This leads to two interconnected challenges. First, how to ensure safety without having a significant negative impact in traffic flow. Second, how will varying penetrations of autonomous vehicles (AVs) impact safety and efficiency in mixed traffic. To address these issues, we start by proposing a risk metric that takes into account the severity of a collision that would happen under a worst-case scenario and the time the vehicle is exposed to such a collision. With this definition, we propose an autonomous lane changing procedure in which the vehicle behaves as if it was simultaneously on both lanes. This ensure that the vehicle never puts itself in a collision prone situation. Given the conservative nature of this approach, which can negatively impact traffic flow, we include the possibility of the AV accepting risks in its gap acceptance decision process. We extend this approach to a scenario with connected and autonomous vehicles (CAV), which can cooperate to generate lane change gaps through communications. In this case, a CAV in the destination lane also behaves as if it was simultaneously on two lanes, thus generating the gap for the incoming vehicle. We perform extensive micro simulations using the commercial software VISSIM with varying percentages of AVs and CAVs, different vehicle inputs, and several accepted risk values. Results indicate that, while AVs need to accept small risks in order to achieve the same traffic flow efficiency as humans, CAVs can improve both safety and efficiency without having to accept any risks. Our results also indicate that AVs and CAVs still behave safely in mixed fleets, but they do not bring significant improvements in traffic flow

Carbon Free Boston: Transportation Technical Report

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical ReportOVERVIEW: Transportation connects Boston’s workers, residents and tourists to their livelihoods, health care, education, recreation, culture, and other aspects of life quality. In cities, transit access is a critical factor determining upward mobility. Yet many urban transportation systems, including Boston’s, underserve some populations along one or more of those dimensions. Boston has the opportunity and means to expand mobility access to all residents, and at the same time reduce GHG emissions from transportation. This requires the transformation of the automobile-centric system that is fueled predominantly by gasoline and diesel fuel. The near elimination of fossil fuels—combined with more transit, walking, and biking—will curtail air pollution and crashes, and dramatically reduce the public health impact of transportation. The City embarks on this transition from a position of strength. Boston is consistently ranked as one of the most walkable and bikeable cities in the nation, and one in three commuters already take public transportation. There are three general strategies to reaching a carbon-neutral transportation system: • Shift trips out of automobiles to transit, biking, and walking;1 • Reduce automobile trips via land use planning that encourages denser development and affordable housing in transit-rich neighborhoods; • Shift most automobiles, trucks, buses, and trains to zero-GHG electricity. Even with Boston’s strong transit foundation, a carbon-neutral transportation system requires a wholesale change in Boston’s transportation culture. Success depends on the intelligent adoption of new technologies, influencing behavior with strong, equitable, and clearly articulated planning and investment, and effective collaboration with state and regional partners.Published versio

Ensuring Cooperative Driving Automation (CDA) and Vulnerable Road Users (VRUs) Safety Through Infrastructure

693JJ319D000012Vulnerable road users (VRUs), including pedestrians, bicyclists, motorcyclists, and a variety of micromobility users, are at an increased risk for collisions, severe injuries, and fatalities relative to other road users, particularly in crowded urban environments. New transportation technologies could have both positive and negative effects on VRU safety. These new technologies include automated driving systems (ADS), which are capable of controlling vehicles with no or limited input from human drivers and cooperative driving automation (CDA), which send and receive cooperative and safety messages. The current literature review assesses the potential impact of ADS-equipped vehicles and CDA technology on VRU safety and the potential role of infrastructure in facilitating safe interactions. The review also includes a prioritized list of issues related to human factors and generated research needs, based on feedback from a panel of subject matter experts

A Study of Readiness for Transportation Electrification and Automation Focusing on Safety and Future Adoption

Transportation electrification and automation are growing societal trends and considered promising pathways to enhance the safety, mobility, efficiency, and sustainability of the surface transportation system. At this early stage of transportation electrification and automation, one of the most critical issues is whether and to what extent people are willing to adopt electric vehicle (EV) and automated vehicle (AV) technologies in the future. Another critical issue, especially concerning transportation automation, is how to thoroughly ensure the safety of automated driving performance to resolve safety concerns about AVs, which is one of the key challenges to AV adoption. In this regard, the dissertation aims to provide new knowledge and deep insights regarding the readiness for transportation electrification and automation in terms of safety and future adoption by investigating how different types of travelers are willing to embrace EV and AV technologies and what safety-related challenges the automated driving systems are facing. First, the dissertation systematically analyzes how individuals become inclined to use AV-based travel options and adopt alternative fuel vehicles (AFVs). For this, an “AV inclination index” is developed to quantify individual travelers’ inclination toward AV-based travel options encompassing owning an AV, using AV ride-hailing services, and using Shared AV (SAV) ride-hailing services. Importantly, the dissertation reveals a meaningful relationship between the “AV inclination index” and AFV adoption. Considering that the commercial sector has the potential to adopt a considerable amount of EVs in the future, the dissertation explores commercial light-duty fleet owners’ intention to adopt different types of EVs. Paying attention to early adopters’ experiences and perspectives, the dissertation investigates BEV owners’ satisfaction and willingness to repurchase a BEV in the future. Given that the safety of AVs is one of the critical factors associated with individual travelers’ willingness to use AVs in the future, the dissertation performs an exhaustive analysis of crashes involving AVs tested on public roads to provide a better understanding of AV safety performance. Based on the findings from each chapter, the dissertation provides the vehicle and transportation industries, engineers, planners, and policymakers with practical implications for a smooth transition to transportation electrification and automation

Contributions to the 10th International Cycling Safety Conference 2022 (ICSC2022)

This publication contains all contributions (extended abstracts) to the 10th International Cycling Safety Conference, which was held in Dresden, Germany, Nov. 08-10, 2022