A multidisciplinary research approach for experimental applications in road-driver interaction analysis

This doctoral dissertation represents a cluster of the research activities conducted at the DICAM Department of the University of Bologna during a three years Ph.D. course. In relation to the broader research topic of “road safety”, the presented research focuses on the investigation of the interaction between the road and the drivers according to human factor principles and supported by the following strategies: 1) The multidisciplinary structure of the research team covering the following academic disciplines: Civil Engineering, Psychology, Neuroscience and Computer Science Engineering. 2) The development of several experimental real driving tests aimed to provide investigators with knowledge and insights on the relation between the driver and the surrounding road environment by focusing on the behaviour of drivers. 3) The use of innovative technologies for the experimental studies, capable to collect data of the vehicle and on the user: a GPS data recorder, for recording the kinematic parameters of the vehicle; an eye tracking device, for monitoring the drivers’ visual behaviour; a neural helmet, for the detection of drivers’ cerebral activity (electroencephalography, EEG). 4) The use of mathematical-computational methodologies (deep learning) for data analyses from experimental studies. The outcomes of this work consist of new knowledge on the casualties between drivers’ behaviour and road environment to be considered for infrastructure design. In particular, the ground-breaking results are represented by: - the reliability and effectiveness of the methodology based on human EEG signals to objectively measure driver’s mental workload with respect to different road factors; - the successful approach for extracting latent features from multidimensional driving behaviour data using a deep learning technique, obtaining driving colour maps which represent an immediate visualization with potential impacts on road safety

Effectiveness of an Emergency Vehicle Operations Course Component, Visual and Perceptual Skills: Analyzing Student Response to Searching, Identifying, Predicting, Deciding, and Executing Skills

As funding for driver education declines according to the National Highway Safety Administration (NHSA) 2013 Traffic Safety Facts, there were 2,345,719 people injured or killed as a result of vehicle crashes. NHTSA reported that there were a total of 118 people killed in 2013 from accidents involving emergency vehicles. The effectiveness of an Emergency Vehicle Operations Course component of visual and perceptual skills can be measured by administering the Driver Performance Test prior to and after student participation. This study examined the population of the students who participated in the TRS 235: Emergency Vehicle Operations Course at Eastern Kentucky University (EKU) in a traditional classroom and online delivery formats. This study determined the potential for a participant to be involved in a crash prior to and after completing TRS 235, as well as the effect the course had on the participants’ visual and perceptual skills. The results of this study indicated that the online and traditional course delivery formats pre and post-test total scores increased significantly. This study also determined that there was a significant difference between the efficacy of online and traditional delivery with online participants scoring higher than the traditional participants. It is important to note that this study does not examine the actual physical performance of the participants’ driving skills or behavior

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeEC/FP7/625991/EU/Hyperscanning 2.0 Analyses of Multimodal Neuroimaging Data: Concept, Methods and Applications/HYPERSCANNING 2.0DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systeme

WiseBench: A Motion Planning Benchmarking Framework for Autonomous Vehicles

Rapid advances in every sphere of autonomous driving technology have intensified the need to be able to benchmark and compare different approaches. While many benchmarking tools tailored to different sub-systems of an autonomous vehicle, such as perception, already exist, certain aspects of autonomous driving still lack the necessary depth and diversity of coverage in suitable benchmarking approaches - autonomous vehicle motion planning is one such aspect. While motion planning benchmarking tools are abundant in the robotics community in general, they largely tend to lack the specificity and scope required to rigorously compare algorithms that are tailored to the autonomous vehicle domain. Furthermore, approaches that are targeted at autonomous vehicle motion planning are generally either not sensitive enough to distinguish subtle differences between different approaches, or not able to scale across problems and operational design domains of varying complexity. This work aims to address these issues by proposing WiseBench, an autonomous vehicle motion planning benchmark framework aimed at comprehensively uncovering fine and coarse-grained differences in motion planners across a wide range of operational design domains. WiseBench outlines a robust set of requirements for a suitable autonomous vehicle motion planner. These include simulation requirements that determine the environmental representation and physics models used by the simulator, scenario-suite requirements that govern the type and complexity of interactions with the environment and other traffic agents, and comparison metrics requirements that are geared towards distinguishing the behavioral capabilities and decision making processes of different motion planners. WiseBench is implemented using a carefully crafted set of scenarios and robust comparison metrics that operate within an in-house simulation environment, all of which satisfy these requirements. The benchmark proved to be successful in comparing and contrasting two different autonomous vehicle motion planners, and was shown to be an effective measure of passenger comfort and safety in a real-life experiment. The main contributions of our work on WiseBench thus include: a scenario creation methodology for the representative scenario suite, a comparison methodology to evaluate different motion planning algorithms, and a proof-of-concept implementation of the WiseBench framework as a whole

The Effects of Work Zone Configurations on Physiological and Subjective Workload

There is a dearth of research on the effect of driving through work zones on physiological and subjective workload of drivers. The objectives of this research were to (a) study the effect of work zones and traffic density on physiological measures of workload, subjective workload and performance variables (b) study the relationship between physiological measures of workload, subjective workload and performance variables. Conventional lane merge (CLM), joint lane merge (JLM) and a road without a work zone (control) were modeled with high and low traffic density by using a full-size driving simulator. 13 female and 17 male students volunteered to participate in this study. Data regarding physiological measures of workload through heart rate variability measures (RMSSD, LF, HF and LF/HF ratio) were collected by using a heart monitoring watch. NASA-TLX was used to measure subjective workload. Variability in steering, braking and speed were used as performance variables. Results showed that the driving scenarios and traffic density did not affect physiological measures of workload. In terms of subjective workload, CLM and JLM did not differ significantly from each other. However, with respect to mental demand, temporal demand, effort and total workload, CLM was significantly more demanding than the control group. Total workload for driving in high traffic density was 27.2% more than that of in low traffic density. No significant differences were observed in brake variability between different scenarios. However, CLM and JLM had significantly higher speed variability than the control group but they were not significantly different from each other. Steer variability and brake variability were higher in high traffic density. In conclusion, results showed that when it comes to using driving simulators, physiological measures of workload show no sensitivity to changes in the work zone but subjective and performance variables are influenced and can be used to compare different work zone configurations

Predicting Inattentional Blindness with Pupillary Response in a Simulated Flight Task

Inattentional blindness (IB) is the failure of observers to notice the presence of a clearly viewable but unexpected visual event when attentional resources are diverted elsewhere. Knowing when an operator is unable to respond or detect an unexpected event may help improve safety during task performance. Unfortunately, it is difficult to predict when such failures might occur. The current study was a secondary data analysis of data collected in the Human and Autonomous Vehicle Systems Laboratory at NASA Langley Research Center. Specifically, 60 subjects (29 male, with normal or corrected-to-normal vision, mean age of 34.5 years (SD = 13.3) were randomly assigned to one of three automation conditions (full automation, partial automation, and full manual) and took part in a simulated flight landing task. The dependent variable was the detection/non-detection of an IB occurrence (a truck on the landing runway). Scores on the NASA-TLX workload rating scale varied significantly by automation condition. The full automation condition reported the lowest subjective task load followed by partial automation and then manual condition. IB detection varied significantly across automation condition. The moderate workload condition of partial automation exhibited the lowest likelihood of IB occurrence. The low workload full automation condition did not differ significantly from the manual condition. Subjects who reported higher task demand had increased pupil dilation and subjects with larger pupil dilation were more likely to detect the runway incursion. These results show eye tracking may be used to identify periods of reduced unexpected visual stimulus detection for possible real-time IB mitigation