Investigating the transition from normal driving to safety-critical scenarios

Investigation of the correlation between factors associated with crash development has enabled the implementation of methods aiming to avert and control crash causation at various points within the crash sequence (Evans, 2006). Partitioning the crash sequence is important because intricated crash causation sequences can be deconstructed and effective prevention strategies can be suggested (Wu & Thor, 2015). Towards this purpose, Tingvall et al. (2009) documented the so-called integrated safety chain which described the change of crash risk on the basis of a developing sequence of events that led to a collision. This thesis examines the crash sequence development and thus, the transition from normal driving to safety critical scenarios. [Continues.

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

Car crashes with two-wheelers in China: Proposal and assessment of C-NCAP automated emergency braking test scenarios

In China, around 15,000 users of two-wheelers (TWs) die on the road every year. Passenger cars are the dominating crash opponent of TWs in road traffic crashes. Understanding the characteristics of car crashes with TWs is essential to enhance cars’ safety performance and improve the safety of TW riders in China. This thesis has three objectives. First, to define test scenarios of Automated Emergency Braking systems for cars encountering TWs (TW-AEB) in China (Paper I). Second, to assess whether cars with good ratings in consumer safety rating programs (e.g., New Car Assessment Program: NCAP) are also likely to perform well in the real-world. Finally, to understand the characteristics of the car crashes with TWs after the TW-AEB application. To achieve the first objective, cluster analysis was applied to the China In-Depth Accident Study (CIDAS). The results were six test scenarios (Paper I), which are proposed for the Chinese NCAP (C-NCAP) TW-AEB testing. To achieve the second and third objectives, counterfactual virtual simulations were performed with and without TW-AEB to a) a C-NCAP TW-AEB test scenario set ; b) an alternative scenario set based on the results of Paper I; and c) real-world crashes in China. Results show much higher crash avoidance rate and lower impact speed were found for C-NCAP scenario set than for the other two sets. To better reflect car crashes with TW in China, longitudinal same-direction scenarios with the car or TW turning and perpendicular scenarios with high TW traveling speed are recommended to be included in C-NCAP future releases. Future work will focus on assessing the combined benefit of preventive and protective safety systems for car-to-TW crashes in China

Making overtaking cyclists safer: Driver intention models in threat assessment and decision-making of advanced driver assistance system

Introduction: The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS). Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS\u27s performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present. Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system. Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system\u27s effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment program

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

Methods and models for safety benefit assessment of advanced driver assistance systems in car-to-cyclist conflicts

To help drivers avoid or mitigate the severity of crashes, advanced driver assistance systems (ADAS) can be designed to provide warnings or interventions. Prospective safety assessment of ADAS is important to quantify and optimise their safety benefit. Such safety assessment methods include, for example, virtual simulations and test-track testing.Today, there are many components of virtual safety assessment simulations with models or methods that are missing or can be substantially improved. This is particularly true for simulations assessing ADASs that address crashes involving cyclists—a crash type that is not decreasing at the same rate as the overall number of road crashes in Europe. The specific methodological gaps that this work addresses are: a) computational driver models for car-to-cyclist overtaking, b) algorithms for model fitting and efficient calculation of ADAS intervention time, and c) a method for merging data from different data sources into the safety assessment.Specifically, for a), different driver models for everyday driver behaviour while overtaking cyclists in a naturalistic driving setting were derived and compared. For b), computationally efficient algorithms to fit driver models to data and compute ADAS intervention time were developed for different types of vehicle models. The algorithms can be included in ADAS both for offline use in virtual assessment simulations and online real-time use in in-vehicle ADAS. Lastly, for c), a method was developed that uses Bayesian statistics to combine results from different data sources, e.g., simulations and test-track data, for ADAS safety benefit assessment.In addition to presenting five peer-reviewed scientific publications, which address these issues, this compilation thesis discusses the use of different data sources; introduces the fundamentals of Bayesian inference, linear programming, and numerical root-finding algorithms; and provides the rationale for methodological choices made, where relevant. Finally, this thesis describes the relationships among the publications and places them into context with existing literature.This work developed driver models for the virtual simulations and methods for the reliable estimation of the prospective safety benefit, which together have the potential to improve the design and the evaluation of ADAS in general, and ADAS for the car-to-cyclist overtaking scenario in particular

Collaboration with other H2020 projects, Deliverable 1.1 of the H2020 project SafetyCube.

SafetyCube was one of five projects to be funded under the H2020 Topic MG‐3.4‐2014 Traffic Safety Analysis and Integrated Approach towards the Safety of Vulnerable Road Users. The five projects were invited by the European Commission to explore the possibilities to cooperate in technical and dissemination aspects. Early collaborations have been established between SafetyCube and other projects supported under the H2020 Topic MG‐3.4‐2014 Traffic Safety Analysis and Integrated Approach towards the Safety of Vulnerable Road Users. A series of joint activities have been conducted including: Project Coordinator collaboration meeting Joint Session at Transport Research Arena Conference, Warsaw April 2016 Informal joint project meeting at TRA Specific Joint Work Package meetings with InDeV in relation to the estimation of accident costs Future joint activities are planned to further explore collaboration opportunities between SafetyCube and other projects Invitation to other project representatives to attend SafetyCube Mid‐term workshop A joint session at the International Cycle Safety Conference in Bologna, November 2016 Further Work Package level discussions to explore potential cooperation in estimating the under‐reporting of crashes

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Toward a Safer Transportation System for Senior Road Users

Senior pedestrians and drivers (65 years and older) are among the most vulnerable road users. As the population of seniors rise, concerns regarding older adults\u27 traffic safety are growing. The advantages of using autonomous vehicles, innovative vehicle technologies, and active transportation are becoming more widely recognized to improve seniors\u27 mobility and safety. This behooves researchers to further investigate senior road users’ safety challenges and countermeasures. This study contributes to the literature by achieving two main goals. First, to explore contributing factors affecting the safety of older pedestrians and drivers in the current transportation system. Second, to examine seniors’ perceptions, preferences, and behaviors toward autonomous vehicles and advanced vehicle technologies, the main components of future transportation systems. To achieve the first objective, crash data involving senior pedestrians and drivers were collected and analyzed. Using structural equation modeling, it was found out that seniors’ susceptibility to pedestrian incidents is a function of level of walking difficulty, fear of falling, and crossing evaluation capability. Senior drivers’ risk factors were found to be driving maneuver & crash location, road features & traffic control devices, driver condition & behavior, road geometric characteristics, crash time and lighting, road class latent factors, as well as pandemic variable. To achieve the second objective, a national survey and a driving simulator experiment were conducted among seniors. The national survey investigates seniors’ perceptions and attitudes to a wide range of AVs features from the perspective of pedestrians and users. Using principal component analysis and cluster analysis, three distinctive clusters of seniors were identified with different perceptions and attitude toward different AV options. The driving simulator experiment examined drivers’ behavior and preferences towards vehicle to infrastructure warning messages. Using the analysis of covariance technique, the results revealed that audio warning message was more effective compared to other scenarios. This finding is consistent with the results of stated preferences of the participants. Female and senior drivers had higher speed limit compliance rate. The findings of this study shed light on key aspects of the current and future of transportation systems that are needed to improve the safety of senior road users