Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

Editorial: A better tomorrow: towards human-oriented, sustainable transportation systems

In a rapidly changing world, transportation is a big determinant of quality of life, financial growth and progress. New challenges (such as the emergence of the COVID-19 pandemic) and opportunities (such as the three revolutions of shared, electric and automated mobility) are expected to drastically change the future mobility landscape. Researchers, policy makers and practitioners are working hard to prepare for and shape the future of mobility that will maximize benefits. Adopting a human perspective as a guiding principle in this endeavor is expected to help prioritize the “right” needs as requirements. In this special issue, eight research papers outline ways in which transportation research can contribute to a better tomorrow. In this editorial, we position the research within the state-of-the-art, identify the needs for future research, and then outline how the included contributions fit in this puzzle. Naturally, the problem of sustainable future transportation systems is way too complicated to be covered with a single special issue. We thus conclude this editorial with a discussion about open questions and future research topics

Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions

Open access articleCurrently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research

A simulation study of predicting conflict-prone traffic conditions in real-time

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing the traffic conditions just prior to collisions with the traffic conditions during normal operations. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the aggregated traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. This may subsequently lead to an incorrect calibration of the model used to predict the probability of a collision. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data (i.e. vehicle trajectories) from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts (i.e. dangerous concurrences between vehicles) data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety, and data on traffic collisions are therefore not needed. Two classifiers are then employed to examine the proposed idea: (i) Support Vector Machines (SVMs) – a sophisticated classifier and (ii) k-Nearest Neighbors (kNN) – a relatively simple classifier. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of 3-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective conflicts prediction

A new integrated collision risk assessment methodology for autonomous vehicles

Real-time risk assessment of autonomous driving at tactical and operational levels is extremely challenging since both contextual and circumferential factors should concurrently be considered. Recent methods have started to simultaneously treat the context of the traffic environment along with vehicle dynamics. In particular, interaction-aware motion models that take inter-vehicle dependencies into account by utilizing the Bayesian interference are employed to mutually control multiple factors. However, communications between vehicles are often assumed and the developed models are required many parameters to be tuned. Consequently, they are computationally very demanding. Even in the cases where these desiderata are fulfilled, current approaches cannot cope with a large volume of sequential data from organically changing traffic scenarios, especially in highly complex operational environments such as dense urban areas with heterogeneous road users. To overcome these limitations, this paper develops a new risk assessment methodology that integrates a network-level collision estimate with a vehicle-based risk estimate in real-time under the joint framework of interaction-aware motion models and Dynamic Bayesian Networks (DBN). Following the formulation and explanation of the required functions, machine learning classifiers were utilized for the real-time network-level collision prediction and the results were then incorporated into the integrated DBN model for predicting collision probabilities in real-time. Results indicated an enhancement of the interaction-aware model by up to 10%, when traffic conditions are deemed as collision-prone. Hence, it was concluded that a well-calibrated collision prediction classifier provides a crucial hint for better risk perception by autonomous vehicles

Real-time classification of aggregated traffic conditions using relevance vector machines

This paper examines the theory and application of a recently developed machine learning technique namely Relevance Vector Machines (RVMs) in the task of traffic conditions classification. Traffic conditions are labelled as dangerous (i.e. probably leading to a collision) and safe (i.e. a normal driving) based on 15-minute measurements of average speed and volume. Two different RVM algorithms are trained with two real-world datasets and validated with one real-world dataset describing traffic conditions of a motorway and two A-class roads in the UK. The performance of these classifiers is compared to the popular and successfully applied technique of Support vector machines (SVMs). The main findings indicate that RVMs could successfully be employed in real-time classification of traffic conditions. They rely on a fewer number of decision vectors, their training time could be reduced to the level of seconds and their classification rates are similar to those of SVMs. However, RVM algorithms with a larger training dataset consisting of highly disaggregated traffic data, as well as the incorporation of other traffic or network variables so as to better describe traffic dynamics, may lead to higher classification accuracy than the one presented in this paper

A simulation study of predicting real-time conflict-prone traffic conditions

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative to pre-collision traffic dynamics. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e. Support Vector Machines, k-Nearest Neighbours and Random Forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of Random Forests with 5-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective prediction of conflicts

Applications of 5G Communications in Civil Protection

Τα δίκτυα πέμπτης γενιάς θεωρούνται ευρέως ως μία από τις πιο θεμελιώδεις τεχνολογικές εξελίξεις του τρέχοντος αιώνα, προσφέροντας υψηλή ταχύτητα, χαμηλή καθυστέρηση και κλιμάκωση. Τα επόμενα χρόνια, τα δίκτυα πέμπτης γενιάς αναμένεται να δημιουργήσουν τη χωρητικότητα, την απόδοση και την ευελιξία του ασύρματου δικτύου για να υποστηρίξουν μια εκρηκτική αύξηση στις συνδεδεμένες συσκευές, μαζί με πρωτοποριακές εφαρμογές. Αυτή η καινοτόμος νέα τεχνολογία μπορεί να βελτιώσει όλο το φάσμα της καθημερινής ζωής από την υγεία στην ψυχαγωγία και από τη γεωργία στην πολιτική προστασία. Οι κρίσιμες επικοινωνίες, ο ακρογωνιαίος λίθος της Πολιτικής Προστασίας, θα επωφεληθούν σε μεγάλο βαθμό από το 5G. Η παρούσα εργασία μελετά πώς νέα στοιχεία και τεχνολογίες του 5G όπως η επαυξημένη πραγματικότητα, η ηλεκτρονική υγεία και η βελτιστοποιημένη δρομολόγηση ασθενοφόρων μπορούν να υποστηρίξουν την Πολιτική Προστασία ενισχύοντας παράλληλα το περιβάλλον και την οικονομία.5G networks are widely considered as one of the most fundamental technology developments of our century, providing ultra-high-speed, low-latency and scalability. Over the coming years, 5G is expected to create the wireless network capacity, performance and flexibility to support an explosive increase in connected devices, along with exciting new use cases. This innovative technology can improve the whole spectrum of everyday life from health to entertainment and from agriculture to civil protection. Mission critical Communications, the cornerstone of civil protection, are to be greatly impacted by 5G. This thesis studies how new 5G components and technologies such as augmented reality, ehealth and optimized routing of ambulances are able to support the role of civil protection while enhancing the protection of the environment and the economy

Public health policy for management of hearing impairments based on big data analytics: EVOTION at Genesis

The holistic management of hearing loss (HL) requires appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation; detection and prevention of cognitive decline; protection from noise; and socioeconomic inclusion of HL patients. However, currently the evidential basis for forming such policies is limited. Holistic HL management policies require the analysis of heterogeneous data, including Hearing Aid (HA) usage, noise episodes, audiological, physiological, cognitive, clinical and medication, personal, behavioural, life style, occupational and environmental data. To utilise these data in forming holistic HL management policies, EVOTION, a new European research and innovation project, aims to develop an integrated platform supporting: (a) the analysis of related datasets to enable the identification of causal and other effects amongst them using various forms of big data analytics, (b) policy decision making focusing on the selection of effective interventions related to the holistic management of HL, based on the outcomes of (a) and the formulation of related public health policies, and (c) the specification and monitoring of such policies in a sustainable manner. In this paper, we describe the EVOTION approach