Driver behavior analysis and route recognition by Hidden Markov Models

Sathyanarayana, Amardeep; Boyraz, Pınar; Hansen, John H. L.

doi:10.1109/icves.2008.4640874

Cited by 101 publications

(53 citation statements)

References 7 publications

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“…Baum-Welch algorithm (a maximum likelihood estimation method) which trains parameters of HMMs is applied to optimize three HMMs driving straight, normal steering, and emergency steering [5]. In [12] Sathyanarayana et al proposed a Driver Behavior Analysis and Route Recognition by Hidden Markov Models in two different approaches. The first (bottom-to-top) approach takes isolated maneuver recognition with model concatenation to construct a generic route, whereas the second (top-to-bottom) approach models the entire route as a phrase and refines the HMM to discover maneuvers.…”

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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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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“…In this study, the POSIT algorithm is used in estimating the head pose of the driver [16]. Head rotation is calculated by using the initialized standard 3D coordinate and current 2D shape coordinate.…”

Section: Head Pose Estimation By the Posit Algorithmmentioning

confidence: 99%

“…Indeed, NHTSA and ETSC manuals suggest that detection and tracking of driver's eye-blinking is the most reliable way in determining the consciousness level of the driver. using HMM [16]. In the present study, we have used a Markov chain framework whereby the driver's eye-blinking and head nodding are separately modelled based upon their visual features, and then the system makes a decision by combining those behavioral states of whether the driver is drowsy or not, according to a certain criteria.…”

Section: Introductionmentioning

confidence: 99%

Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model

2016

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This study presents a new method to track driver's facial states, such as head pose and eye-blinking in the real-time basis. Since a driver in the natural driving condition moves his head in diverse ways and his face is often occluded by his hand or the wheel, it should be a great challenge for the standard face models. Among many, Active Appearance Model (AAM), and Active Shape Model (ASM) are two favored face models. We have extended Discriminative Bayesian ASM by incorporating the extreme pose cases, called it Pose Extended-Active Shape model (PE-ASM). Two face databases (DB) are used for the comparison purpose: one is the Boston University face DB and the other is our custom-made driving DB. Our evaluation indicates that PE-ASM outperforms ASM and AAM in terms of the face fitting against extreme poses. Using this model, we can estimate the driver's head pose, as well as eye-blinking, by adding respective processes. Two HMMs are trained to model temporal behaviors of these two facial features, and consequently the system can make inference by enumerating these HMM states whether the driver is drowsy or not. Result suggests that it can be used as a driver drowsiness detector in the commercial car where the visual conditions are very diverse and often tough to deal with.

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“…So there arises the need to monitor the driving style and road anomalies to ensure driver safety and for the maintenance of roads. Previously a lot of work is done in this field but researchers mainly focused on monitoring either driver behavior or road conditions using specialized hardware deployed inside the car [1] [2] [3] [4] [5] [6] or roadside which is expensive and also requires maintenance. Our method, contrary from previous work, use accelerometer sensor of Smartphone to observe both the pattern of driver's driving style and road anomalies.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Driving and Road Events via Smartphone

Kalra¹,

Chugh²,

Bansal³

2014

IJCA

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Travelling in India by road is considered dangerous as traffic conditions are chaotic, the drivers drive recklessly, and the roads are poorly maintained. Therefore, there is need to monitor driver behavior and road condition regularly. In this work, we developed a method that collects data using accelerometer sensor present in Smartphone and analyze driving events and road anomalies. This paper presents the patterns of driving events, such as left turn, right turn, sudden braking and sudden forward acceleration, and road conditions, such as pothole, bump and rough patch, detected in the data set. This work will help in analyzing driving behavior and road anomalies to ensure driver safety and maintenance of roads.

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Driver behavior analysis and route recognition by Hidden Markov Models

Cited by 101 publications

References 7 publications

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

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

Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model

Analyzing Driving and Road Events via Smartphone

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