Driving Behavior and Traffic Safety: An Acceleration-Based Safety Evaluation Procedure for Smartphones

Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

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“…Another category of driving events that are of interest in [238] x x Thresholding. [239] x x x Thresholding. [83], [240] x x Thresholding.…”

Section: Driver Behavior Classificationmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

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“…FCD data have also been used to establish unsafe driving behaviors in Axer and Friedrich [22]. Other works on traffic safety based on FCD are Biral et al [23], Vaiana et al [24], and Guido et al [25]. Kerner et al [26] showed that 1.5% of FCD penetration rate can be enough to detect an incident in a traffic center with a probability of 65%.…”

Section: State Of the Art And Contributionmentioning

confidence: 99%

The Use of Adaptive Traffic Signal Systems Based on Floating Car Data

Astarita

Giofrè

Guido

et al. 2017

Wireless Communications and Mobile Computing

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This paper presents a simple concept which has not been, up to now, thoroughly explored in scientific research: the use of information coming from the network of Internet connected mobile devices (on vehicles) to regulate traffic light systems. Three large-scale changes are going to shape the future of transportation and could lead to the regulation of traffic signal system based on floating car data (FCD): (i) the implementation of Internet connected cars with global navigation satellite (GNSS) system receivers and the autonomous car revolution; (ii) the spreading of mobile cooperative Web 2.0 and the extension to connected vehicles; (iii) an increasing need for sustainability of transportation in terms of energy efficiency, traffic safety, and environmental issues. Up to now, the concept of floating car data (FCD) has only been extensively used to obtain traffic information and estimate traffic parameters. Traffic lights regulation based on FCD technology has not been fully researched since the implementation requires new ideas and algorithms. This paper intends to provide a seminal insight into the important issue of adaptive traffic light based on FCD by presenting ideas that can be useful to researchers and engineers in the long-term task of developing new algorithms and systems that may revolutionize the way traffic lights are regulated.

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“…In [18] has been proposed a theoretical framework in which acceleration behavior can be analyzed in order to detect aggressive behavior. The limits of these accelerations are related to the edges of the friction circle (also called ellipse of adherence), which depend on tire characteristics and road surface condition.…”

Section: Related Workmentioning

confidence: 99%

Comparison of different driving style analysis approaches based on trip segmentation over GPS information

Brambilla

Mascetti

Mauri

2017

2017 IEEE International Conference on Big Data (Big Data)

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Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage "better" driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.

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Driving Behavior and Traffic Safety: An Acceleration-Based Safety Evaluation Procedure for Smartphones

Cited by 93 publications

References 11 publications

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

The Use of Adaptive Traffic Signal Systems Based on Floating Car Data

Comparison of different driving style analysis approaches based on trip segmentation over GPS information

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