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

Driver's visual attention to different categories of roadside advertising signs

Roadside advertising signs are a salient potential source of driver's distraction. Previous research has mainly investigated driver's visual attention to billboards, which represents only one category of advertising signs. In this study, driver's visual attention was assessed in a naturalistic driving setting for six categories of roadside advertising signs: vendor signs, billboards, movable display boards, single and multiple commercial directional signs, and gas price LED displays. Fixation rate, fixation duration, fixation distance and driving speed were assessed in a sample of 15 drivers along a 30-km route including a total of 154 advertising signs belonging to the six categories described above. The role of clearance from the road, elevation, height, width, surface, number and size of characters, total number of characters, side of the road (driving side, opposite side), context (rural, urban), were also considered. Overall 24% of the roadside advertising signs were fixated. Fixation rate was significantly influenced by sign category, clearance from the road and number of characters. Median value for fixation duration was 297 ms. Fixation duration was significantly influenced by speed, elevation from road level, number of medium size characters, and was higher in the rural context. Median value for fixation distance was 58.10 m, and was significantly influenced by advertising sign category, character count and speed

EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research

Analysis of Road-User Interaction by Extraction of Driver Behavior Features Using Deep Learning

In this study, an improved deep learning model is proposed to explore the complex interactions between the road environment and driver's behaviour throughout the generation of a graphical representation. The proposed model consists of an unsupervised Denoising Stacked Autoencoder (SDAE) able to provide output layers in RGB colors. The dataset comes from an experimental driving test where kinematic measures were tracked with an in-vehicle GPS device. The graphical outcomes reveal the method ability to efficiently detect patterns of simple driving behaviors, as well as the road environment complexity and some events encountered along the path

EEG-Based Mental Workload Neurometric to Evaluate the Impact of Different Traffic and Road Conditions in Real Driving Settings

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research

Road sign vision and driver behaviour in work zones

The effectiveness of roadwork signs on drivers\u2019 safety is a poorly investigated topic. The present study examined visual fixations of 29 participants to work zone signs, while driving 27 km along rural roads. The drivers\u2019 visual fixations on the work zones signs were recorded with an eye tracking device, synchronized to a GPS recorder that collected kinematic data. The routes crossed 23 roadwork zones, including a total of 69 vertical work zone signs. Visual behaviour to roadwork signs were compared to visual behaviour to permanent vertical signs. The results revealed that drivers glanced at both temporary and permanent signs along the roadwork areas with a similar 40% frequency. In addition, they glanced at single roadwork signs more often and for longer than at multiple-roadwork signs. The main findings of this paper lead to conclude that driver behaviour, investigated by comparing instant speed and visual fixations, is frequently unsafe

T-junction priority scheme and road user\u2019s yielding behavior

Four studies investigated yielding behavior in yield-controlled T-junctions that differed for two priority schemes. In one case road users in the intersecting arm had to give way to road users in the straight arm (priority to straight arm). In the other case road users in the straight arm had to give way to road users approaching from the intersecting arm (priority to intersecting arm). In two studies, yielding behavior was assessed with approaching speed and gaze behavior to the critical areas of the intersection. Two additional studies monitored road users\u2019 speed and eye movements approaching the intersection. The results of the two behavioral studies showed a significant speed reduction and an increase of driver\u2019s visual inspection to the intersection area in the priority-to-straight-arm condition in comparison to the priority-to-intersecting-arm condition. The eye movement analysis showed that total fixation time towards the intersection critical area and horizontal eye movements were significantly higher in the priority-to-straight-arm condition. The results emphasize the importance of considering perceptual affordances and expectations for priority in intersection design to increase drivers\u2019 compliance to yielding rules

Effects of median refuge island and flashing vertical sign on conspicuity and safety of unsignalized crosswalks

Pedestrian crossings are critical points in terms of road safety because they tend to be characterized by high accident rates. Pedestrian safety at zebra crossings depends mainly on vehicles speed. In this study the effects of median refuge island and ''Yield here to pedestrians" flashing vertical sign on pedestrian crossing conspicuity were assessed with a before-after analysis of both speed and visual behaviour of drivers approaching to crosswalks. The elements of the pedestrian crossing that were more salient and how drivers' visual behaviour was related to speed were assessed analysing drivers' eye movements. The intervention significantly increased the fixation time to the zebra markings and the addition of the flashing light increased conspicuity and fixation time to the vertical sign. The median refuge island was glanced by 60.7% of the drivers. Distance of first-fixation of the crosswalk increased by 44.7%. Notwithstanding mean and V85 speed parameters were lower after the intervention , the effects on crosswalk visual attention were higher than on speed