C-ITS Safety Evaluation Methodology based on Cooperative Awareness Messages

Elhenawy, Mohammed; Bond, Andy; Rakotonirainy, Andry

doi:10.1109/itsc.2018.8569417

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…• Recent research uses lane changes as a safety indicator to evaluate the safety benefits of C-ITS [10]. The challenge in such studies is the limited number of sensing modalities so that the trained machine learning models yield relatively low sensitivity and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A framework for testing independence between lane change and cooperative intelligent transportation system

Elhenawy¹,

Glaser²,

Bond³

et al. 2020

PLoS ONE

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Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.

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Section: Introductionmentioning

confidence: 99%

A framework for testing independence between lane change and cooperative intelligent transportation system

Elhenawy¹,

Glaser²,

Bond³

et al. 2020

PLoS ONE

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“…In Australia, they are also working on evaluation based on cooperative awareness messages to assess safety impact [14].…”

Section: State Of the Artmentioning

confidence: 99%

Methodology of Functional and Technical Evaluation of Cooperative Intelligent Transport Systems and Its Practical Application

et al. 2021

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In the area of smart cities, great emphasis is placed on many different fields such as energetics, information systems, and transportation. All of these should lead to a simplification of life thanks to smart technologies. If we talk about the transportation field, the main issues related to this area are safety, traffic efficiency, or the environment. Another condition is the successful acceptance of any new technology by its users. Cooperative systems prove to be a suitable solution for these issues, especially in urban areas. Today, pilot implementations of cooperative systems in European countries are being carried out. However, before they are put into full operation, they need to be tested, evaluated, and assessed. This article focuses on the latter two points, i.e., evaluation and assessment of the cooperative systems. For this purpose, a methodology was created, which describes the procedure chosen in the evaluation and assessment of cooperative systems in the Czech Republic and a demonstration of its use by example. The methodology is focused on three main areas, which in this case are functional evaluation, user acceptance, and impact assessment. For the area of user acceptance, the main source was questionnaires, impact assessment relied on measured data while functional evaluation was based on discussions with the drivers, evaluating the cooperative systems, the measured data, and the expert observations. All collected and measured data were then processed and some of the results of the evaluation of the selected service are presented at the end of this article.

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“…At present, there exist many pilot studies of connected vehicles in developed countries like the USA, Australia, Japan, China, France and so on. Some common and renowned connected vehicle pilots are New York City DOT Pilot [12], Tampa-Hillsborough Expressway Authority Pilot [13], Wyoming DOT Pilot [7], Ipswich Connected vehicle Pilot [14,15], Safe and Intelligent Mobility Project [16] etc. They are working on implementing connected vehicle technology and applications practically on-road as field operation test (FOT) over large urban areas.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Based Intersection Trajectory Movement Classification for Big Connected Vehicle Data

Komol¹,

Elhenawy

Masoud

et al. 2021

IEEE Access

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Trajectory movement labelling is an important pre-stage for predicting connected vehicle (CV) movement at intersections. Drivers' movement prediction and warning at intersections ensure advanced transportation safety and researchers use machine learning-based data-driven approaches to implement these technologies. However, prediction of drivers' movements at intersections requires labelling the train and test dataset accurately with different vehicle movements at intersections to evaluate the performance of the prediction model by comparing the actual and predicted intersection movements. Moreover, due to GPS detection error or missing co-operative awareness messages (CAM), the data resides with many abnormal trajectories which are unable to be matched with regular straight or any turning movements. Especially for big data with million trajectories, it is tedious to label the movements manually.To solve this problem, we have created an automated trajectory movement classification technique using a dual approach of map matching technique and deep transfer learning modelling. Data of connected vehicle trajectory information is taken from the Ipswich Connected Vehicle Pilot (ICVP) Project, which is one of the largest connected vehicle pilots within a naturalistic driving environment in Australia. Map matching approach is performed as initial labelling by analysing the origin and destination of the vehicle CAM messages at intersections and then was converted as image datasets of 19202 samples. The map matching error and abnormal trajectories are identified by visual inspection. With properly labelled 9496 training images, 10 transfer learning models are built and tested through the remaining 9706 testing images. The maximum testing accuracy (99.73%) is achieved from the Densenet169 model, and the result shows satisfactory accuracy for individual classes: straight (99.85%), turn left (99.59), turn right (99.25), u-turn (100%), abnormal (98.63%). This model becomes a routine tool that is used daily to automatically classify thousands of trajectory movements of the C-ITS data in the ICVP project.

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C-ITS Safety Evaluation Methodology based on Cooperative Awareness Messages

Cited by 14 publications

References 4 publications

A framework for testing independence between lane change and cooperative intelligent transportation system

A framework for testing independence between lane change and cooperative intelligent transportation system

Methodology of Functional and Technical Evaluation of Cooperative Intelligent Transport Systems and Its Practical Application

Deep Transfer Learning Based Intersection Trajectory Movement Classification for Big Connected Vehicle Data

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