MARKOV SWITCHING MODEL FOR DRIVER BEHAVIOR PREDICTION: USE CASES ON SMARTPHONES A PREPRINT (original) (raw)
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Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones
ArXiv, 2021
Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable mod...
Estimating Driving Behavior by a Smartphone
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Modeling and prediction of driving behavior
Driving assistance systems that adapt to an individual driver are essential for avoiding traffic accidents because there are individual differences in the way of driving. To realize such systems, it is necessary to take account of not only observable physical quantities, but also information inferred from observation. For example, a collision avoidance system warns a driver according to an estimated probability of a future collision. Several probabilistic inference methods have been applied to modeling and recognition of driving behavior. Sakaguchi et al. inferred a probabilistic distribution of brake onset time by a static Bayesian network from various evidence, such as weather condition, methodical driving style scores, accelerator pedal release timing, and so on [1]. Dynamic Bayesian networks, including well-known hidden Markov models, have also attracted many researchers. Forbes et al. provided a decision-making model for an autonomous vehicle of a simple simulation environment ...
Smartphones, Suitable Tool for Driver Behavior Recognition. A Systematic Review
Communications in Computer and Information Science, 2020
A current reality is the increase in the number of road traffic accidents caused mainly by incorrect driving habits. For this reason, the development of different approaches that can help reduce accidents on the road is imperative. A strategy is the use of smartphones as a tool to identify driving behaviors, which is documented in the state of the art. This paper presents a systematic review focused on the strategies used to recognizing driving behaviors with sensors that are part of smartphones. The review was carried out on the Scopus database, included studies published in the last 4 years (2017-2020) that allowed identifying a total of 222222 relevant results. This paper presents a report of the most used sensors, algorithms, driving events and driving patterns. It includes result discussion and considerations of future work on this topic, additional to the bibliometric report.
2015 IEEE SENSORS, 2015
With the advent of smartphones and advancements in sensor capabilities, it is possible to actively monitor drivers and provide a viable solution necessary to reduce vehicle accidents. Driving maneuvers provide an insight to a driver's driving skills and behavior, which is an important aspect for applications such as driver profiling, driver safety, fuel consumption modeling, etc. Driver profiling requires detection of sharp and normal driving maneuvers having high and low Signal-to-Noise Ratio (SNR), respectively. Typical event detection techniques detect sharp driving maneuvers but fail to detect normal maneuvers. In this paper, we propose Hidden Markov Model (HMM) based technique to detect lateral maneuvers and Jerk Energy based technique to detect longitudinal maneuvers. Most driver profiling techniques consider only longitudinal events such as hard acceleration/braking, whereas the proposed approach profiles a driver by coupling lateral and longitudinal events. Based on collected datasets on diverse type of driving scenario, events are detected with 95% accuracy. For driver profiling, we achieve 90% accuracy in match between drivers subjective score and model-based estimated score.
Driving style recognition using machine learning and smartphones
F1000Research, 2022
Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone’s built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add “noise” to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able ...
Driving behavior analysis with smartphones
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia - MUM '12, 2012
We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.
Prediction of Driving Behavior through Probabilistic Inference
Driving assistance systems are essential technologies to avoid traffic accidents, reduce traffic jams, and solve environmental problems. Not only observable behavioral data, but also unobservable inferred values should be considered to realize advanced driving assistance systems that are adaptable to individual drivers and situations. For this purpose, Bayesian networks, which are the most consistent inference approach, have been applied for estimation of unobservable physical values and internal states introduced for convenience's sake. Nevertheless, only a few reports have addressed prediction of future states of driving behavior. This paper proposes predicting driving behavior in the near future through a simple dynamic Bayesian network, which is a hidden Markov model or a switching linear dynamic system. The proposed predictors were examined with real data. We focused on prediction of the future stop probability at an intersection because it is one of the most important mane...
DrivingStyles: A smartphone application to assess driver behavior
2013 IEEE Symposium on Computers and Communications (ISCC), 2013
The DrivingStyles architecture integrates both data mining techniques and neural networks to generate a classification of driving styles by analyzing the driver behavior along each route. In particular, based on parameters such as speed, acceleration, and revolutions per minute of the engine (rpm), we have implemented a neural network based algorithm that is able to characterize the type of road on which the vehicle is moving, as well as the degree of aggressiveness of each driver. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. In this work we take advantage of two key-points: the evolution of mobile terminals and the availability of a standard interface to access car data. Our DrivingStyles platform to achieve a symbiosis between smartphones and vehicles able to make the former operate as an onboard unit. Results show that neural networks were able to achieve a high degree of exactitude at classifying both road and driver types based on user traces. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 1550 downloads from different countries in just a few months. Index Terms-Driving styles; Android smartphone; OBD-II; neural networks; eco-driving.
Driving event recognition using machine learning and smartphones
F1000Research, 2022
Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone's built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add "noise" to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able to distinguish different driving events with an accuracy of about 95%. Conclusions: Based on the results of preliminary studies, we have determined that the proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver's driving behavior.