Pedestrian Distraction at Railway Level Crossings: can Illuminated In-ground LEDs attract their Attention Back?

One major contributor to pedestrian risk of injury and death at railway level crossings in Australia and New Zealand are road users who are complacent, distracted or inattentive. Such users can either intentionally or unintentionally travel through a railway crossing, without looking first for oncoming trains. Increased use of mobile devices increases the prevalence of pedestrian distraction and tends to reduce the effectiveness of warning devices installed at level crossings. A potential innovative solution to combat this issue is to use in-ground LED visual warning devices. However, there is currently no evaluation of the safety improvements obtained from such an intervention. This research evaluated pedestrians’ eye gaze behaviour toward illuminated in-ground LEDs while conducting a distractive task with a mobile device or headphones. We conducted a laboratory study (N=20) where participants equipped with an eye tracker had to detect the activation of lights on the floor or on the wall under various distraction conditions. We found that such intervention could be very effective in attracting the attention of distracted pedestrians, even when participants looked at the screen of their phone, as they mainly used peripheral vision during this detection task. The Australasian Centre for Rail Innovation (ACRI) is now partnering with KiwiRail to trial such an intervention in the field at passively protected level crossings

Design and development of a contextualised interaction-aware trajectory prediction system

The rapid progression of technology, exemplified by collision avoidance systems, has presented novel opportunities for reducing the incidence and severity of road crashes. Effective delivery of driver collision warnings, in a timely manner, is a critical factor for improving driver responses and acceptance. This research contributes valuable knowledge regarding the parameters that significantly impact the design of Vehicle Trajectory Prediction (VTP) systems. Results highlight the effectiveness of integrating driving performance and environmental features in the design of VTP systems. Specifically, an interaction-aware VTP system is proposed, which yields a 10% increase in the accuracy of the VTP system

A retinal image authentication framework based on a graph-based representation algorithm in a two-stage matching structure

Retinal vascular pattern has many valuable characteristics such as uniqueness, stability and permanence as a basis for human authentication in security applications. This paper presents an automatic rotation-invariant retinal authentication framework based on a novel graph-based retinal representation scheme. In the proposed framework, to replace the retinal image with a relational mathematical graph (RMG), we propose a novel RMG definition algorithm from the corresponding blood vessel pattern of the retinal image. Then, the unique features of RMG are extracted to supplement the authentication process in our framework. The authentication process is carried out in a two-stage matching structure. In the first stage of this scenario, the defined RMG of enquiry image is authenticated with enrolled RMGs in the database based on isomorphism theory. If the defined RMG of enquiry image is not isomorphic with none enrolled RMG in the database, in the second stage of our matching structure, the authentication is performed based on the extracted features from the defined RMG by a similarity-based matching scheme. The proposed graph-based authentication framework is evaluated on VARIA database and accuracy rate of 97.14% with false accept ratio of zero and false reject ratio of 2.85% are obtained. The experimental results show that the proposed authentication framework provides the rotation invariant, multi resolution and optimized features with low computational complexity for the retina-based authentication application.</p

Unmanned aerial vehicle based speeding and tailgating detection system

Unmanned Aerial Vehicles (UAVs) offer a considerable potential for road safety stakeholders; they have been already used for tasks such as assisting first responders. This paper reports on a small-scale prototyping effort to use UAVs to measure speeding and tailgating on major urban roads. One hour of UAV footage was acquired and a custom vehicle detection system was programmed. From this, we developed methods to extract vehicles’ speed and headways. Validation information was extracted from TMR induction loop present near the testing site

A Dual Learning Model for Vehicle Trajectory Prediction

Automated vehicles and advanced driver-assistance systems require an accurate predictionof future traffic scene states. The tendency in recent years has been to use deep learning approaches foraccurate trajectory prediction but these approaches suffer from computational complexity, dependency on aspecific environment/dataset, and lack of insight into vehicle interactions. In this paper, we aim to addressthese limitations by proposing a Dual Learning Model (DLM) using lane occupancy and risk maps for vehicletrajectory prediction. To understand the spatial interactions of road users, make the model independent of theenvironment, and consider inter-vehicle distances, we embed an Occupancy Map (OM) into the trajectoryprediction model. We also utilise a traffic scene Risk Map (RM) to explicitly consider a comprehensivedefinition of risk based on Time-to-Collision in the traffic scene. These two features employed in theencoder-decoder architecture improve system accuracy with less complexity and provide insight into theinteraction between all road users. The experiment has been conducted on two different naturalistic highwaydriving datasets (i.e., NGSIM and HighD) demonstrating algorithm independence from a single environment.Comparison results indicate that the DLM achieves a more accurate trajectory prediction with a lesscomplex structure compared with existing approaches in terms of RMS prediction error, which indicatesthe effectiveness of DLM in such a context.<br/

Pedestrians distracted by their smartphone: Are in-ground flashing lights catching their attention? A laboratory study

Pedestrian distraction is a growing road safety concern worldwide. While there are currently no studies linking distraction and pedestrian crash risk, distraction has been shown to increase risky behaviours in pedestrians, for example, through reducing visual scanning before traversing an intersection. Illuminated in-ground Light Emitting Diodes (LEDs) embedded into pathways are an emerging solution to address the growing distraction problem associated with mobile use while walking. The current study sought to determine if such an intervention was effective in attracting the attention of distracted pedestrians. We conducted a controlled laboratory study (N = 24) to evaluate whether pedestrians detected the activation of flashing LEDs when distracted by a smartphone more accurately and efficiently when the lights were located on the floor compared to a control position on the wall. Eye gaze movements via an eye tracker and behavioural responses via response times assessed the detection of these flashing LEDs. Distracted participants were able to detect the activation of the floor and wall-mounted LEDs with accuracies above 90%. The visual and auditory distraction tasks increased reaction times by 143 and 124 ms, respectively. Even when distracted, performance improved with floor LEDs close to participants, with reaction time improvements by 43 and 159 ms for the LEDs 2 and 1 ms away from the participant respectively. The addition of floor LED lights resulted in a performance similar to the one observed for wall-mounted LEDs in the non-distracted condition. Moreover, participants did not necessarily need to fixate on the LEDs to detect their activation, thus were likely to have detected them using their peripheral vision. The findings suggest that LEDs embedded in pathways are likely to be effective at attracting the attention of distracted pedestrians. Further research needs to be conducted in the field to confirm these findings, and to evaluate the actual effects on behaviour under real-world conditions

Pedestrian distraction at railway level crossings: Can illuminated in-ground LEDs attract their attention back?

One major contributor to pedestrian risk of injury and death at railway level crossings in Australia and New Zealand are road users who are complacent, distracted or inattentive. Such users can either intentionally or unintentionally travel through a railway crossing, without looking first for oncoming trains. Increased use of mobile devices increases the prevalence of pedestrian distraction and tends to reduce the effectiveness of warning devices installed at level crossings. A potential innovative solution to combat this issue is to use in-ground LED visual warning devices. However, there is currently no evaluation of the safety improvements obtained from such an intervention. This research evaluated pedestrians’ eye gaze behaviour toward illuminated in-ground LEDs while conducting a distractive task with a mobile device or headphones. We conducted a laboratory study (N=20) where participants equipped with an eye tracker had to detect the activation of lights on the floor or on the wall under various distraction conditions. We found that such intervention could be very effective in attracting the attention of distracted pedestrians, even when participants looked at the screen of their phone, as they mainly used peripheral vision during this detection task. The Australasian Centre for Rail Innovation (ACRI) is now partnering with KiwiRail to trial such an intervention in the field at passively protected level crossings

Factors reducing the detectability of train horns by road users: A laboratory study

Level crossing safety is a well-researched safety issue worldwide, but little attention has been placed on the safety benefits of using train horns when a train approaches a level crossing. Given train horns' adverse effects on the health and well-being of residents living near rail tracks, the use of train horns must be beneficial to safety. The current study sought to determine in a laboratory environment whether road users (N = 31) can detect the range of train horns observed in Australia in terms of loudness and duration, using high-definition audio recordings from railway crossings. A repeated measures design was used to evaluate the effects of key factors likely to influence the detectability of train horns, including, visual and auditory distractive tasks, hearing loss and environmental noise (crossing bells). Train horn detectability was assessed based on participants' accuracy and reaction times. Results indicated the duration of the train horn had the most influential effect on the detectability of train horns, with short-duration train horns less likely to be detected. The presence of bells at a crossing was the second most important factor that limited train horn detection. Train horn loudness also affected detectability: faint blasts were less likely to be noticed, while loudest blasts were more likely to be noticed. However, loud horns reduced the ability to detect the side from which the train was approaching and may result in longer times to detect the train, in the field. The auditory distractive task reduced the train horn detection accuracy and increased reaction time. However, the visual distractive task and medium to severe hearing loss were not found to affect train horn detection. This laboratory study is the first to provide a broad understanding of the factors that affect the detectability of Australian train horns by road users. The findings from this study provide important insights into ways to reduce the use and modify the practice to mitigate the negative effects of train horns while maintaining the safety of road users

Visualising data of the Australian Naturalistic Driving Study

The ANDS study provided access to a large dataset. An interactive visualisation interface was developed to help researchers to investigate such a large dataset. The interface extends the one developed by SHRP2, and is aimed to provide researchers with a flexible tool allowing analyses outside predefined events of interest. This first version of the interface is presented in this abstract, and additional functions are suggested for future implementation

Driver influence on vehicle trajectory prediction

Drivers continually interact with other road users and use information from the road environment to make decisions to control their vehicle. A clear understanding of different parameters impacting this interaction can provide us with a new design approach for a more effective driver assistance system - a personalised trajectory prediction system. This paper highlights the influential factors on trajectory prediction system performance by (i) identifying driver behaviours impacting the trajectory prediction system; and (ii) analysing other contributing factors such as traffic density, secondary task, gender and age group. To explore the most influential contributing factors, we first train an interaction-aware trajectory prediction system using time-series data derived from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS). Prediction error is then analysed based on driver characteristics such as driver profile which is subjectively measured through self-reported questions, and driving performance which is based on evaluation of time-series information such as speed, acceleration, jerk, time, and space headway. The results show that prediction error significantly increased in the scenarios where the driver engaged in risky behaviour. Analysis shows that trajectory prediction system performance is also affected by factors such as traffic density, engagement in secondary tasks, driver gender and age group. We show that the driver profile, which is subjectively measured using self-reported questionnaires, is not as significant as the driving performance information, which is objectively measured and extracted during each specific driving scenario