Surrogate Safety Measures Prediction at Multiple Timescales in V2P Conflicts Based on Gated Recurrent Unit

Improving pedestrian safety at urban intersections requires intelligent systems that should not only understand the actual vehicle–pedestrian (V2P) interaction state but also proactively anticipate the event’s future severity pattern. This paper presents a Gated Recurrent Unit-based system that aims to predict, up to 3 s ahead in time, the severity level of V2P encounters, depending on the current scene representation drawn from on-board radars’ data. A car-driving simulator experiment has been designed to collect sequential mobility features on a cohort of 65 licensed university students who faced different V2P conflicts on a planned urban route. To accurately describe the pedestrian safety condition during the encounter process, a combination of surrogate safety indicators, namely TAdv (Time Advantage) and T2 (Nearness of the Encroachment), are considered for modeling. Due to the nature of these indicators, multiple recurrent neural networks are trained to separately predict T2 continuous values and TAdv categories. Afterwards, their predictions are exploited to label serious conflict interactions. As a comparison, an additional Gated Recurrent Unit (GRU) neural network is developed to directly predict the severity level of inner-city encounters. The latter neural model reaches the best performance on the test set, scoring a recall value of 0.899. Based on selected threshold values, the presented models can be used to label pedestrians near accident events and to enhance existing intelligent driving systems.

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PEDESTRIAN SAFETY EVALUATION OF SIGNALIZED INTERSECTIONS USING SURROGATE SAFETY MEASURES

Transport ◽

10.3846/transport.2020.12157 ◽

2020 ◽

Vol 35 (1) ◽

pp. 48-56

Author(s):

Sankaran Marisamynathan ◽

Perumal Vedagiri

Keyword(s):

Safety Evaluation ◽

Pedestrian Safety ◽

Signalized Intersections ◽

Signalized Intersection ◽

Mixed Traffic ◽

Model Framework ◽

Safety Measures ◽

Severity Level ◽

Traffic Conditions ◽

Surrogate Safety Measures

The large proportions of pedestrian fatalities led researchers to make the improvements of pedestrian safety at intersections. Thus, this paper proposes a methodology to evaluate crosswalk safety at signalized intersections using Surrogate Safety Measures (SSM) under mixed traffic conditions. The required pedestrian, traffic, and geometric data were extracted based on the videographic survey conducted at signalized intersections in Mumbai (India). Post Encroachment Time (PET) for each pedestrian were segregated into three categories for estimating pedestrian–vehicle interactions and Cumulative Frequency Distribution (CDF) was plotted to calculate the threshold values for each interaction severity level. The Cumulative Logistic Regression (CLR) model was developed to predict the pedestrian mean PET values in the cross-walk at signalized intersections. The proposed model was validated with a new signalized intersection and the results were shown that the proposed PET ranges and model appropriate for Indian mixed traffic conditions. To assess the suitability of model framework, model transferability was carried out with data collected at signalized intersection in Kolkata (India). Finally, this study can be helpful to rank the severity level of pedestrian safety in the crosswalk and improve the existing facilities at signalized intersections.

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Assessment of the Speed Management Impact on Road Traffic Safety on the Sections of Motorways and Expressways Using Simulation Methods

Sensors ◽

10.3390/s20185057 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5057

Author(s):

Jacek Oskarbski ◽

Tomasz Kamiński ◽

Kyandoghere Kyamakya ◽

Jean Chamberlain Chedjou ◽

Karol Żarski ◽

...

Keyword(s):

Road Safety ◽

Driving Simulator ◽

Intelligent Transport System ◽

Variable Speed ◽

Speed Limits ◽

Safety Measures ◽

Traffic Models ◽

Traffic Efficiency ◽

Variable Speed Limits ◽

Surrogate Safety Measures

Methods used to evaluate the impact of Intelligent Transport System (ITS) services on road safety are usually based on expert assessments or statistical studies. However, commonly used methods are challenging to apply in the planning process of ITS services. This paper presents the methodology of research using surrogate safety measures calculated and calibrated with the use of simulation techniques and a driving simulator. This approach supports the choice of the type of ITS services that are beneficial for traffic efficiency and road safety. This paper presents results of research on the influence of selected scenarios of variable speed limits on the efficiency and safety of traffic on the sections of motorways and expressways in various traffic conditions. The driving simulator was used to estimate the efficiency of lane-keeping by the driver. The simulation traffic models were calibrated using driving simulator data and roadside sensor data. The traffic models made it possible to determine surrogate safety measures (number of conflicts and their severity) in selected scenarios of using ITS services. The presented studies confirmed the positive impact of Variable Speed Limits (VSLs) on the level of road safety and traffic efficiency. This paper also presents recommendations and plans for further research in this area.

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Implementing Surrogate Safety Measures in Driving Simulator and Evaluating the Safety Effects of Simulator-Based Training on Risky Driving Behaviors

Journal of Advanced Transportation ◽

10.1155/2020/7525721 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Eunhan Ka ◽

Do-Gyeong Kim ◽

Jooneui Hong ◽

Chungwon Lee

Keyword(s):

Driving Simulator ◽

Driver Education ◽

Risky Driving ◽

Safety Measures ◽

Novice Drivers ◽

Elderly Drivers ◽

Driving Behaviors ◽

Commercial Drivers ◽

Surrogate Safety Measures ◽

Risky Driving Behaviors

Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about 52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky driving behaviors associated with surrounding vehicles. This study introduces surrogate safety measures (SSMs) into simulator-based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky driving behaviors. The results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. This study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a simulator-based training program using the SSMs is effective in reducing risky driving behaviors. The risky driving behaviors by novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall. The results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management strategies as well as to reduce risky driving behaviors in simulator-based training.

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Deep Learning Architectures for Skateboarder–Pedestrian Surrogate Safety Measures

Future Transportation ◽

10.3390/futuretransp1020022 ◽

2021 ◽

Vol 1 (2) ◽

pp. 387-413

Author(s):

Chowdhury Erfan Shourov ◽

Mahasweta Sarkar ◽

Arash Jahangiri ◽

Christopher Paolini

Keyword(s):

Deep Learning ◽

Real Time ◽

State Of The Art ◽

Pedestrian Safety ◽

Time Signal ◽

Single Shot ◽

Safety Measures ◽

Surrogate Safety Measures ◽

Learning Architectures

Skateboarding as a method of transportation has become prevalent, which has increased the occurrence and likelihood of pedestrian–skateboarder collisions and near-collision scenarios in shared-use roadway areas. Collisions between pedestrians and skateboarders can result in significant injury. New approaches are needed to evaluate shared-use areas prone to hazardous pedestrian–skateboarder interactions, and perform real-time, in situ (e.g., on-device) predictions of pedestrian–skateboarder collisions as road conditions vary due to changes in land usage and construction. A mechanism called the Surrogate Safety Measures for skateboarder–pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. In this paper, we present the first ever skateboarder–pedestrian safety study leveraging deep learning architectures. We view and analyze state of the art deep learning architectures, namely the Faster R-CNN and two variants of the Single Shot Multi-box Detector (SSD) model to select the correct model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. We also contribute a new annotated data set that contains skateboarder–pedestrian interactions that has been collected for this study. Both our selected models can detect and classify pedestrians and skateboarders correctly and efficiently. However, due to differences in their architectures and based on the advantages and disadvantages of each model, both models were individually used to perform two different set of tasks. Due to improved accuracy, the Faster R-CNN model was used to automate the calculation of post encroachment time, whereas to determine hazardous regions in real-time, due to its extremely fast inference rate, the Single Shot Multibox MobileNet V1 model was used. An outcome of this work is a model that can be deployed on low-cost, small-footprint mobile and IoT devices at traffic intersections with existing cameras to perform on-device inferencing for in situ Surrogate Safety Measurement (SSM), such as Time-To-Collision (TTC) and Post Encroachment Time (PET). SSM values that exceed a hazard threshold can be published to an Message Queuing Telemetry Transport (MQTT) broker, where messages are received by an intersection traffic signal controller for real-time signal adjustment, thus contributing to state-of-the-art vehicle and pedestrian safety at hazard-prone intersections.

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Validating a driving simulator using surrogate safety measures

Accident Analysis & Prevention ◽

10.1016/j.aap.2007.06.007 ◽

2008 ◽

Vol 40 (1) ◽

pp. 274-288 ◽

Cited By ~ 125

Author(s):

Xuedong Yan ◽

Mohamed Abdel-Aty ◽

Essam Radwan ◽

Xuesong Wang ◽

Praveen Chilakapati

Keyword(s):

Driving Simulator ◽

Safety Measures ◽

Surrogate Safety Measures

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Maternal car driving capacity after birth: a pilot prospective study randomizing postnatal women to early verses late driving in a driving simulator

The Journal of Maternal-Fetal & Neonatal Medicine ◽

10.1080/14767058.2018.1519537 ◽

2018 ◽

pp. 1-8

Author(s):

Margaret E. Harpham ◽

Natasha Nassar ◽

Stefanie Leung ◽

Anne Lainchbury ◽

Antonia W. Shand

Keyword(s):

Prospective Study ◽

Driving Simulator ◽

Postnatal Women ◽

Car Driving

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Research and training car driving simulator with weight up to 3.5 tons dedicated to physical disabled drivers

Mechanik ◽

10.17814/mechanik.2019.8-9.69 ◽

2019 ◽

Vol 92 (8-9) ◽

pp. 571-573

Author(s):

Jarosław Jankowski

Keyword(s):

Degrees Of Freedom ◽

Driving Simulator ◽

Physical Disabilities ◽

Motion Platform ◽

Six Degrees Of Freedom ◽

Work Related ◽

The Creation ◽

People With Physical Disabilities ◽

Car Driving ◽

And Training

The article presents the continuation of work related to the creation of a car driving simulator with a weight of up to 3.5 tons adapted to selected disabilities. The article contains a description of the developed motion platform with six degrees of freedom and the cockpit. In order to ensure the possibility of being managed by the largest group of people with physical disabilities, selected support solutions were implemented. These devices can be easily dismantled to test others. The platform together with the cockpit is controlled from the simulator application and the image is presented to the simulation participant in 3D projection glasses and optionally on a three-segment screen.

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AN EVALUATION OF AIRPORT WAYFINDING AND SIGNAGE ON SENIOR DRIVER BEHAVIOUR AND SAFETY OF AIRPORT ROAD ACCESS DESIGN

Journal of Air Transport Studies ◽

10.38008/jats.v8i1.43 ◽

2017 ◽

Vol 8 (1) ◽

pp. 108-129

Author(s):

Nur Khairiel Anuar ◽

Romano Pagliari ◽

Richard Moxon

Keyword(s):

Road Safety ◽

Driving Simulator ◽

Age Groups ◽

Driver Behaviour ◽

Driving Behaviour ◽

Road Design ◽

Driver Performance ◽

Study Participants ◽

The Impact ◽

Car Driving

The purpose of this study was to investigate the impact of different wayfinding provision on senior driving behaviour and road safety. A car driving simulator was used to model scenarios of differing wayfinding complexity and road design. Three scenario types were designed consisting of 3.8 miles of airport road. Wayfinding complexity varied due to differing levels of road-side furniture. Experienced car drivers were asked to drive simulated routes. Forty drivers in the age ranges: 50 to 54, 55 to 59 and those aged over 60 were selected to perform the study. Participants drove for approximately 20 minutes to complete the simulated driving. The driver performance was compared between age groups. Results were analysed by Mean, Standard Deviation and ANOVA Test, and discussed with reference to the use of the driving simulator. The ANOVA confirmed that age group has a correlation between road design complexity, driving behaviour and driving errors.

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