Characterizing Driving Context from Driver Behavior

Moosavi, Sobhan; Omidvar-Tehrani, Behrooz; Craig, Romany; Nandi, Arnab; Ramnath, Rajiv

doi:10.1145/3139958.3139992

Cited by 16 publications

(8 citation statements)

References 14 publications

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“…Apart from harnessing videos and crowd-sourced information, several works [14], [15] are done on abnormal driving behavior detection by exploiting IMU and GPS data. To prevent fatal accidents, authors [16]- [18] try to alert the drivers whenever risky driving signature is observed, such as lane departure or sudden slow-down indicating congestion. However, they have not looked into the effect of neighboring vehicles or other surrounding factors on various driving maneuvers.…”

Section: Related Workmentioning

confidence: 99%

DriCon: On-device Just-in-Time Context Characterization for Unexpected Driving Events

Das¹,

Chakraborty²,

Mitra³

2023

Preprint

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Driving is a complex task carried out under the influence of diverse spatial objects and their temporal interactions. Therefore, a sudden fluctuation in driving behavior can be due to either a lack of driving skill or the effect of various on-road spatial factors such as pedestrian movements, peer vehicles' actions, etc. Therefore, understanding the context behind a degraded driving behavior just-in-time is necessary to ensure on-road safety. In this paper, we develop a system called DriCon that exploits the information acquired from a dashboard-mounted edge-device to understand the context in terms of micro-events from a diverse set of on-road spatial factors and in-vehicle driving maneuvers taken. DriCon uses the live in-house testbed and the largest publicly available driving dataset to generate human interpretable explanations against the unexpected driving events. Also, it provides a better insight with an improved similarity of 80% over 50 hours of driving data than the existing driving behavior characterization techniques.

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Section: Related Workmentioning

confidence: 99%

DriCon: On-device Just-in-Time Context Characterization for Unexpected Driving Events

Das¹,

Chakraborty²,

Mitra³

2023

Preprint

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“…Additionally, we set the minimum duration of a trajectory to be 10 minutes and the maximum to 30 minutes. This step will not limit the generalizability of our method, but ensures to have enough data for each trajectory and helps to achieve reasonable running time by avoiding long trajectories 3 . Lastly, we remove those trajectories with any missing attribute to preserve consistency in data.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The problem of learning driving style is specifically important for driver risk prediction used by insurance companies * All rights reserved to the authors, and The Ohio State University (2021). [1][2][3]. The shift towards more personalized insurance products has created a need for understanding driving style at an individual level.…”

Section: Introductionmentioning

confidence: 99%

Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification

Moosavi¹,

Mahajan²,

Parthasarathy³

et al. 2021

Preprint

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Identifying driving styles is the task of analyzing the behavior of drivers in order to capture variations that will serve to discriminate different drivers from each other. This task has become a prerequisite for a variety of applications, including usage-based insurance, driver coaching, driver action prediction, and even in designing autonomous vehicles; because driving style encodes essential information needed by these applications. In this paper, we present a deep-neural-network architecture, we term D-CRNN, for building high-fidelity representations for driving style, that combine the power of convolutional neural networks (CNN) and recurrent neural networks (RNN). Using CNN, we capture semantic patterns of driver behavior from trajectories (such as a turn or a braking event). We then find temporal dependencies between these semantic patterns using RNN to encode driving style. We demonstrate the effectiveness of these techniques for driver identification by learning driving style through extensive experiments conducted on several large, real-world datasets, and comparing the results with the state-of-the-art deep-learning and non-deep-learning solutions. These experiments also demonstrate a useful example of bias removal, by presenting how we preprocess the input data by sampling dissimilar trajectories for each driver to prevent spatial memorization. Finally, this paper presents an analysis of the contribution of different attributes for driver identification; we find that engine RPM, Speed, and Acceleration are the best combination of features.

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“…• ψ 3 : End the current segment after a drastic change in the signal, where the drift is captured using signal segmentation approaches. We employ the Wedding Cake technique for the dynamic segmentation of our signals [30,31].…”

Section: Principlesmentioning

confidence: 99%

Interactive Region-of-Interest Discovery using Exploratory Feedback

Omidvar-Tehrani¹

2021

Preprint

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In this paper, we propose a geospatial data management framework called IRIDEF which captures and analyzes user's exploratory feedback for an enriched guidance mechanism in the context of interactive analysis. We discuss that exploratory feedback can be a proxy for decision-making feedback when the latter is scarce or unavailable. IRIDEF identifies regions of interest (ROIs) via exploratory feedback, and highlights a few interesting and out-of-sight POIs in each ROI. These highlights enable the user to shape up his/her future interactions with the system. We detail the components of our proposed framework in the form of a data analysis pipeline, and present the aspects of efficiency and effectiveness for each component. We also discuss evaluation plans and future directions for IRIDEF.

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Characterizing Driving Context from Driver Behavior

Cited by 16 publications

References 14 publications

DriCon: On-device Just-in-Time Context Characterization for Unexpected Driving Events

DriCon: On-device Just-in-Time Context Characterization for Unexpected Driving Events

Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification

Interactive Region-of-Interest Discovery using Exploratory Feedback

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