Analysis of Potential Co-Benefits for Bicyclist Crash Imminent Braking Systems

In the US, the number of traffic fatalities has had a long term downward trend as a result of advances in the crash worthiness of vehicles. However, these improvements in crash worthiness do little to protect other vulnerable road users such as pedestrians or bicyclists. Several manufacturers have developed a new generation of crash avoidance systems that attempt to recognize and mitigate imminent crashes with non-motorists. While the focus of these systems has been on pedestrians where they can make meaningful contributions to improved safety [1], recent designs of these systems have recognized mitigating bicyclist crashes as a potential co-benefit. This paper evaluates the performance of one system that is currently available for consumer purchase. Because the vehicle manufacturer does not claim effectiveness for their system under all crash geometries, we focus our attention on the crash scenario that has the highest social cost in the US: the cyclist and vehicle on parallel paths being struck from behind. Our analysis of co benefits examines the ability to reduce three measures: number of crashes, fatalities, and a comprehensive measure for social cost that incorporates morbidity and mortality. Test track simulations under realistic circumstances with a realistic surrogate bicyclist target are conducted. Empirical models are developed for system performance and potential benefits for injury and fatality reduction. These models identify three key variables in the analysis: vehicle speed, cyclist speed and cyclist age as key determinants of potential co-benefits. We find that the evaluated system offers only limited benefits for any but the oldest bicycle riders for our tested scenario

Computational interaction models for automated vehicles and cyclists

Cyclists’ safety is crucial for a sustainable transport system. Cyclists are considered vulnerableroad users because they are not protected by a physical compartment around them. In recentyears, passenger car occupants’ share of fatalities has been decreasing, but that of cyclists hasactually increased. Most of the conflicts between cyclists and motorized vehicles occur atcrossings where they cross each other’s path. Automated vehicles (AVs) are being developedto increase traffic safety and reduce human errors in driving tasks, including when theyencounter cyclists at intersections. AVs use behavioral models to predict other road user’sbehaviors and then plan their path accordingly. Thus, there is a need to investigate how cyclistsinteract and communicate with motorized vehicles at conflicting scenarios like unsignalizedintersections. This understanding will be used to develop accurate computational models ofcyclists’ behavior when they interact with motorized vehicles in conflict scenarios.The overall goal of this thesis is to investigate how cyclists communicate and interact withmotorized vehicles in the specific conflict scenario of an unsignalized intersection. In the firstof two studies, naturalistic data was used to model the cyclists’ decision whether to yield to apassenger car at an unsignalized intersection. Interaction events were extracted from thetrajectory dataset, and cyclists’ behavioral cues were added from the sensory data. Bothcyclists’ kinematics and visual cues were found to be significant in predicting who crossed theintersection first. The second study used a cycling simulator to acquire in-depth knowledgeabout cyclists’ behavioral patterns as they interacted with an approaching vehicle at theunsignalized intersection. Two independent variables were manipulated across the trials:difference in time to arrival at the intersection (DTA) and visibility condition (field of viewdistance). Results from the mixed effect logistic model showed that only DTA affected thecyclist’s decision to cross before the vehicle. However, increasing the visibility at theintersection reduced the severity of the cyclists’ braking profiles. Both studies contributed tothe development of computational models of cyclist behavior that may be used to support safeautomated driving.Future work aims to find differences in cyclists’ interactions with different vehicle types, suchas passenger cars, taxis, and trucks. In addition, the interaction process may also be evaluatedfrom the driver’s perspective by using a driving simulator instead of a riding simulator. Thissetup would allow us to investigate how drivers respond to cyclists at the same intersection.The resulting data will contribute to the development of accurate predictive models for AVs

15-07 App-based Crowd Sourcing of Bicycle and Pedestrian Conflict Data

Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-vehicle and other similar conflicts may represent a better performance measure for safety assessment. For transportation safety, a clear conflict occurs when two parties’ paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver occurs may also impact public perceptions of safety. Most existing literature on conflicts focuses on vehicle conflicts and intersections. While some research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. In this project, we propose field testing a crowd-sourced data app to better understand the continuum of conflicts (bicycle/pedestrian, bicycle/vehicle, and pedestrian/vehicle) experienced by pedestrians and cyclists; the study also tests the effectiveness of the app and its associated crowd-sourced data collection. This study assesses the data quality of the crowd sourced data and compares it to more traditional data sources while performing hot spot analysis. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, enforcement, infrastructure, programs and policies

Understanding interactions between autonomous vehicles and other road users: A literature review

This review draws on literature relating to the interactions of vehicles with other vehicles, interactions between vehicles and infrastructure, and interactions between autonomous vehicles and cyclists and autonomous vehicles and pedestrians. The available literature relating to autonomous vehicles interactions is currently limited and hence the review has considered issues which will be relevant to autonomous vehicles from reading and evaluating a broader but still relevant literature.The project is concerned primarily with autonomous vehicles within the urban environment and hence the greatest consideration has been given to interactions on typical urban roads, with specific consideration also being given to shared space. The central questions in relation to autonomous vehicles and other road users revolve around gap acceptance, overtaking behaviour, behaviour at road narrowings, the ability to detect and avoid cyclists taking paths through a junction which conflict with the autonomous vehicle’s path, and the ability of autonomous vehicles to sense and respond to human gestures. A long list of potential research questions has been developed, many of which are not realistically answerable by the Venturer project. However, the important research questions which might potentially be answered by the current project are offered as the basis for the more detailed consideration of the conduct of the interaction trial

Priorities and Potential of Pedestrian Protection - Accident data, Experimental tests and Numerical Simulations of Car-to-Pedestrian Impacts

Pedestrian disability and fatality as a consequence of car crashes is a large global health problem. To introduce maximally effective car-based countermeasures it is important to understand which injuries are most common and from which car parts they originate. It is also important to focus on the most severe injuries resulting in disability or death. The aim of this thesis was therefore to determine priorities for and evaluate the potential of car-mounted safety systems designed to mitigate severe upper-body injuries (including disability and fatality) of pedestrians in car crashes. Accident data was collected from two areas; severe (AIS3+) accidents in Dresden/Hannover in Germany and fatal accidents in Sweden. For the surviving pedestrians an estimate of long-term injury was performed using accident data- derived risk matrices of permanent injury. Results showed that 31% would sustain a permanent impairment of some kind and 5% would sustain a more severe impairment, where the head was most susceptible to severe impairment. The car front frequently caused leg injuries, which is addressed in current regulations. However, current legal tests do not address the most common upper-body injury source, the windshield, which was found to be the dominating cause of head injuries. Chest injuries, frequently caused by both the hood and windshield areas in the severe and fatal crashes in this thesis, are also unaddressed in legal tests. Children are most commonly head-injured from the hood area, which is addressed in current regulations. Further, regulations do not fully consider brain injury with the current head test methods. Therefore, in this thesis focus was on upper-body injury/source combinations not addressed in the regulations, that is, the head-to-windshield area and chest-to-hood/windshield areas, and the evaluation of brain injury in hood and windshield impacts. Experimental head-to-hood component tests with succeeding brain simulations were performed to evaluate the influence of the under-hood distance and head impact speed. A hood designed to minimize linear head loading to acceptable injury levels was also found effective in reducing combined linear/rotational brain loading. Further, in full-scale car-to-pedestrian finite element simulations both a braking and deployable system alone proved efficient in reducing head and chest loading, and an integrated countermeasure of combining the two systems proved to increase the protection potential. While current pedestrian countermeasures focus on the head-to-hood impact, this thesis recommends extending countermeasures to the lower part of the windshield and the A-pillars, and adding brain and chest injury assessment for both hood and windshield areas to effectively minimize disabling and fatal injuries. Since head impact location and head impact speed is dependent on the car design, the introduction of full-scale simulations in the test methods to determine impact conditions for experimental component tests is recommended. If the deployable countermeasures are combined with autonomous braking in an integrated system the most effective system is achieved. Auto-brake systems should, in high speed impacts, aim to reduce speeds to where the secondary countermeasures can effectively mitigate injury. Future pedestrian test methods should therefore evaluate how primary and secondary countermeasures interact

International Cycling Safety Conference - Book of abstracts

The present document compiles the abstracts submitted to the International Cycling Safety Conference that took place in Bologna from the 2nd to 4th of November, 2016. The topics of the submissions range from infrastructural and technological, to cognitive and behavioral issues related to cycling safety

International Cycling Safety Conference - Book of abstracts

The present document compiles the abstracts submitted to the International Cycling Safety Conference that took place in Bologna from the 2nd to 4th of November, 2016. The topics of the submissions range from infrastructural and technological, to cognitive and behavioral issues related to cycling safety

Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream

In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles

Carpooling Liability?: Applying Tort Law Principles to the Joint Emergence of Self-Driving Automobiles and Transportation Network Companies

Self-driving automobiles have emerged as the future of vehicular travel, but this innovation is not developing in isolation. Simultaneously, the popularity of transportation network companies functioning as ride-hailing and ride-sharing services have altered traditional conceptions of personal transportation. Technology companies, conventional automakers, and start-up businesses each play significant roles in fundamentally transforming transportation methods. These transformations raise numerous liability questions. Specifically, the emergence of self-driving vehicles and transportation network companies create uncertainty for the application of tort law’s negligence standard. This Note addresses technological innovations in vehicular transportation and their accompanying legislative and regulatory developments. Then, this Note discusses the implications for vicarious liability for vehicle owners, duties of care for vehicle operators, and corresponding insurance regimes. This Note also considers theoretical justifications for tort concepts including enterprise liability. Accounting for the inevitable uncertainty in applying tort law to new invention, this Note proposes a strict and vicarious liability regime with corresponding no-fault automobile insurance