Unsupervised Hierarchical Modeling of Driving Behavior and Prediction of Contextual Changing Points

Delport²,

2019

Sensors

Drivers’ behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using the Gaussian mixture models (GMM); and predicting vehicles’ short-term lateral motions (i.e., left/right turn and left/right lane change) based on real-world vehicle mobility data, provided by the U.S. Department of Transportation, with different ensemble decision trees. We considered several important kinetic features and higher order kinematic variables. The research results of our proposed approach demonstrate the effectiveness of pattern classification and on-road lateral motion prediction. This methodology framework has the potential to be incorporated into current data-driven collision warning systems, to enable more practical on-road preprocessing in intelligent vehicles, and to be applied in autopilot-driving scenarios.

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach

Delport²,

2019

Sensors

International Journal of Advanced Robotic Systems

“…Note that several automatic segmentation methods of time series data for operation data have been proposed. [38][39][40] In this article, the driving operation data are segmented based on GP-HSMM 40 Competitive learning based on HMMs. Next, a set of Positive data is classified and modeled by HMM.…”

Section: Procedures Of Contributing Modelmentioning

confidence: 99%

Detection of contributing object to driving operations based on hidden Markov model

Hashimoto¹,

Yamada²,

Tsuchiya³

et al. 2019

With increase in the number of elderly people in the Japanese society, traffic accidents caused by elderly driver is considered problematic. The primary factor of the traffic accidents is a reduction in their driving cognitive performance. Therefore, a system that supports the cognitive performance of drivers can greatly contribute in preventing accidents. Recently, the development of devices for visually providing information, such as smart glasses or head up display, is in progress. These devices can provide more effective supporting information for cognitive performance. In this article, we focus on the selection problem of information to be presented for drivers to realize the cognitive support system. It has been reported that the presentation of excessive information to a driver reduces the judgment ability of the driver and makes the information less trustworthy. Thus, indiscriminate presentation of information in the vision of the driver is not an effective cognitive support. Therefore, a mechanism for determining the information to be presented to the driver based on the current driving situation is required. In this study, the object that contributes to execution of avoidance driving operation is regarded as the object that drivers must recognize and present for drivers. This object is called as contributing object. In this article, we propose a method that selects contributing objects among the appeared objects on the current driving scene. The proposed method expresses the relation between the time series change of an appeared object and avoidance operation of the driver by a mathematical model. This model can predict execution timing of avoidance driving operation and estimate contributing object based on the prediction result of driving operation. This model named as contributing model consisted of multi-hidden Markov models. Hidden Markov model is time series probabilistic model with high readability. This is because that model parameters express the probabilistic distribution and its statistics. Therefore, the characteristics of contributing model are that it enables the designer to understand the basis for the output decision. In this article, we evaluated detection accuracy of contributing object based on the proposed method, and readability of contributing model through several experiments. According to the results of these experiments, high detection accuracy of contributing object was confirmed. Moreover, it was confirmed that the basis of detected contributing object judgment can be understood from contributing model.

Journal of Advanced Transportation

“…In this method, the driving speed is assumed to be a function of several factors such as overall traveling schedule, speed, and road surface conditions. Taniguchi et al [23] proposed an unsupervised learning method, which is established on the basis of the original double articulation analyzer model. is method predicts possible scenarios of driving behavior by segmenting and modeling incoming driving behavior time series data.…”

Section: Introductionmentioning

confidence: 99%

Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network

Liu

Jia²,

Wang³

et al. 2020

Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system. To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method. This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data. Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained through the whale optimization algorithm. Finally, the well-trained model can be used to represent the human drivers’ operation effectively. The MATLAB simulation results showed that the driver model can achieve an accuracy of 90%.