An Ontological Approach to Inform HMI Designs for Minimizing Driver Distractions with ADAS

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and efficiency as well as comfort for drivers in the driving process. Recent studies have noticed that when Human Machine Interface (HMI) is not designed properly, an ADAS can cause distraction which would affect its usage and even lead to safety issues. Current understanding of these issues is limited to the context-dependent nature of such systems. This paper reports the development of a holistic conceptualisation of how drivers interact with ADAS and how such interaction could lead to potential distraction. This is done taking an ontological approach to contextualise the potential distraction, driving tasks and user interactions centred on the use of ADAS. Example scenarios are also given to demonstrate how the developed ontology can be used to deduce rules for identifying distraction from ADAS and informing future designs

Virtual reality driving simulator for analysis of user response time

Master of ScienceDepartment of Computer ScienceWilliam H. HsuThe purpose of this master’s thesis is to investigate and analyze the ability of individual users of a virtual reality (VR) driving simulator to react to a set of complex scenarios involving various accident-prone scenarios in a mixed virtual environment. The virtual environment is realistic enough to examine the response times in complex scenarios, and to identify the response time taken and the factors affecting it. The final result identifies the response time and represented the correlation between demographics and the driver’s behavior

The potential mental health effects of remote control in an autonomous maritime world

Many maritime activities, such as loading, unloading and transporting cargoes, consist primarily of long periods of low-stress, with some moments of high stress during complex manoeuvres or unanticipated, dangerous, incidences. The increase in autonomy provided by machines and AI is beginning to take over certain tasks in the maritime sector, to reduce costs and mitigate human error. However, with the current levels of autonomous technology available, legislation, and public trust in the technology, such solutions are only able to remove majority of tasks associated with low-stress periods. In fact, many current remote control solutions still suggest relying on human operators to deal with the complex situations AI struggle with. Such a human–automation relationship could endanger the human element. The concern is that, if the human user is spending a disproportionate part of their time dealing with multiple, unconnected, high-stress tasks, without periods to de-stress, this could increasingly put workers at risk. This paper seeks to highlight potential technical, social, and mental, issues that may arise as the sector begins implementing semi-autonomous and fully autonomous maritime operations

Connected Vehicle Technology: User and System Performance Characteristics

The emerging connected vehicle (CV) technology plays a promising role in providing more operable and safer transportation environments. Yet, many questions remain unanswered as to how various user and system characteristics of CV-enabled networks can shape the successful implementation of the technology to maximize the return on investment. This research attempts to capture the effect of multiple factors such as traffic density, market penetration, and transmission range on the communication stability and overall network performance by developing a new CONnectivity ROBustness (CONROB) model. The model was tested with data collected from microscopic simulation of a 195 sq-mile traffic network and showed a potential to capture the effect of such factors on the communication stability in CV environments. The information exchanged among CVs can also be used to estimate traffic conditions in real time by invoking the probe vehicle feature of CV technology. Since factors affecting the connectivity robustness also have an impact on the performance of traffic condition estimation models, a direct relationship between connectivity robustness and traffic condition estimation performance was established. Simulation results show that the CONROB model can be used as a tool to predict the accuracy of the estimated traffic conditions (e.g. travel times), as well as the reliability of such estimates, given specific system characteristics. The optimal deployment of road-side units (RSUs) is another important factor that affects the communication stability and the traffic conditions estimates and reliability. Thus, an optimization approach was developed to identify the optimal RSUs locations with the objective function of maximizing the connectivity robustness. Simulation results for the developed approach show that CONROB model can help identify the optimal RSUs locations. This shows the importance of CONROB model as a planning tool for CV environments. For the individual user performance characteristics, a preliminary driving simulator test bed for CV technology was developed and tested on thirty licensed drivers. Forward collision warning messages were delivered to drivers when predefined time-to-collision values take place. The findings show improved reaction times of drivers when receiving the warning messages which lend credence to the safety benefits of the CV technology

Modeling and Analysis of Advanced Driver Assistance Systems in Police Vehicles

Motor vehicle crashes (MVCs) involving police vehicles have been identified as a significant problem nationwide. Police MVCs are attributed to driving at high speed, pursuit situations, extreme weather conditions, complex traffic situations, and interacting with in-vehicle non-driving related tasks (NDRTs). Advanced driver-assistance systems (ADAS) are promising technologies to enhance officers’ safety by relieving them from some driving related activities. This study aimed to examine whether ADAS technologies could enhance officers' driving performance, decrease their workload, and increase their trust in vehicle safety. The research methodology included a literature review, survey with law enforcement officers (LEOs), driving simulation study, and models of officers' reaction times for steering and braking. Initially, a systematic review of the existing and upcoming ADAS features in police vehicles was conducted. Based on the findings, a survey study with 73 police officers was conducted (Chapter 2) to understand their needs regarding ADAS in police vehicles. Results suggested that ADAS such as forward collision warning (FCW), blind spot monitoring (BSM), and automatic emergency braking (AEB) could be beneficial features for police vehicles. Additionally, results of the correlation analyses indicated that officer behavior and opinion on ADAS features were influenced by the trust officers had in the available ADAS systems among other key factors such as ADAS training and perceived usefulness. Technology acceptance modeling (TAM) results suggested that training on ADAS could enhance officers' perception of the features and increase their intention to use them. However, officers identified several obstacles to the adoption of ADAS, including lack of adaptability, usability issues, and distrust in the technology. To promote the use of ADAS, officers recommended having adaptive ADAS warnings tailored to specific driving situations, such as pursuit driving and engagement in an NDRT. Based on the results of the survey study, a driving simulator study was conducted to examine how FCW/AEB and BSM impact the driving performance, workload, and trust of officers (Chapter 3). The findings of the simulator study indicated that FCW and AEB improved driving performance, while the impact of BSM was limited due to its low salience. ADAS warnings increased drivers' workload up to a certain point, enhancing their passing performance. However, during pursuit situations, officers' driving performance degraded, and their cognitive load increased, emphasizing the need for ADAS that can help maintain their situational awareness. The study also developed predictive models to estimate police officers' brake reaction time and steering wheel angle during critical driving situations. The results can be used as inputs for an adaptive FCW system. The findings of this study can be used to improve the design of ADAS technologies, which can improve the safety of LEOs and reduce the risk of crashes during high-demand driving situations

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads