Masumi Egawa

Human Drivers Based Active-Passive Model for Automated Lane Change

Lane change maneuver is a complicated maneuver, and incorrect maneuvering is an important reason for expressway accidents and fatalities. In this scenario, automated lane change has great potential to reduce the number of accidents. Previous research in this area, typically, focuses on the generation of an optimal lane change trajectory, while ignoring the human behavior model. To understand the human lane change behavior model, we carried out experiments on Japanese expressways. By analyzing the human-driver lane change data, we propose a two-segment lane change model that mimics the human-driver. We categorize the driving environment based on the observation grid and propose different lane change behaviors to handle the different scenarios. We develop an intuitive method to select the suitable lane change behavior, for a given scenario, using active (accelerate/decelerate) and passive (wait) information derived from the distance and related velocity (dx/dv) graph. Additionally, we also identify the most desirable and safe conditions for doing lane change based on the human driver preference data. We evaluated the proposed model by performing lane change simulations in the PreScan environment, while considering the vehicle motion/control model. The simulation results show the proposed model is able to handle complicated lane change scenarios with human driver-like performance.

Modeling and Prediction of Driving Behaviors Using a Nonparametric Bayesian Method With AR Models

To develop a new generation advanced driver assistance system that avoids a dangerous condition in advance, we need to predict driving behaviors. Since a nonparametric Bayesian method with a two-level structure successfully predicted the symbolized behaviors only, we applied a nonparametric Bayesian method with linear dynamical systems to predicting the driving behavior. The method called the beta process autoregressive hidden Markov model (BP-AR-HMM) segments driving behaviors into states each of which corresponds to an AR model and it predicts future behaviors using the estimated future state sequence and the dynamical systems therein. Here, the segmentation as well as the parameters of the dynamical systems are determined using given training data in an unsupervised way. We carried out experiments with real driving data and found that the BP-AR-HMM predicted driving behaviors better than other methods.

Towards prediction of driving behavior via basic pattern discovery with BP-AR-HMM

Prediction of driving behaviors is important problem in developing the next-generation driving support system. In order to take account of diverse driving situations, it is necessary to deal with multiple time series data considering commonalities and differences among them. In this paper we utilize the beta process autoregressive hidden Markov model (BP-AR-HMM) that can model multiple time series considering common and different features among them using the beta process as a prior distribution. We apply the BP-AR-HMM to actual driving behavior data to estimate VAR process parameters that represent the driving behaviors, and with the estimated parameters we predict the driving behaviors of unknown test data. The results suggest that it is possible to identify the dynamical behaviors of driving operations using BP-AR-HMM, and to predict driving behaviors in actual environment.

A comparative study of time series modeling for driving behavior towards prediction

Prediction of driving behaviors is an important problem in developing a next-generation driving support system. In order to take diverse driving situations into account, it is necessary to model multiple driving operation time series data. In this study we modeled multiple driving operation time series with four modeling methods including beta process autoregressive hidden Markov model (BP-AR-HMM), which we used in our previous study. We quantitatively compared the modeling methods with respect to prediction accuracies, and concluded that BP-AR-HMM excelled the other modeling methods in modeling multiple driving operation time series and predicting unknown driving operations. The result suggests that BP-AR-HMM estimated behaviors of a driver and transition probabilities between the behaviors more successfully than the other methods, because BP-AR-HMM can deal with commonalities and differences among multiple time series, but the others cannot. Therefore BP-AR-HMM may help us to predict driver behaviors in real environment and to develop the next-generation driving support system.

Analyzing driver gaze behavior and consistency of decision making during automated driving

We investigate a possible method for detecting a drivers negative adaptation to an automated driving system by analyzing consistency of driver decision making and driver gaze behavior during automated driving. We focus on an automated driving system equivalent to Level 2 automation per the NHTSAs definition. At this level of automation, drivers must be ready to take control of the vehicle in critical situations by monitoring the driving environment and vehicle behavior. Since drivers are not required to operate the pedals or steering wheel during automated driving, a drivers negative adaptation to an automated system needs to be detected from behavior other than vehicle operation. In this study, we focus on driver gaze behavior. We conduct a simulator study to compare the gaze behavior of fifteen drivers during conventional and automated driving. We also analyze the consistency of driver decision making when changing lanes during conventional and automated driving. Experimental results show that drivers who pay less attention to the road ahead during automated driving tend to be less sensitive to risk factors in the surrounding environment and also tend to make inconsistent lane change decisions during automated driving.

General behavior and motion model for automated lane change

Lane change maneuver is a cause for many severe highway accidents and automatic lane change has great potentials to reduce the impact of human error and number of accidents. Previous researches mostly tried to find an optimal trajectory and ignore the behavior model. Presented methods can be applied for simple lane change scenario and generally fail for complicated cases or in the presence of time/distance constraints. Through analysis and inspiring of human driver lane change data, we propose a multi segments lane change model to mimic the human driver for challenging scenarios. We also propose a method to convert behavior/motion selection to a time-based pattern recognition problem. We developed a simulation platform in PreScan and evaluated proposed automatic lane change method for challenging scenarios.

Integrating driving behavior and traffic context through signal symbolization

This paper presents a novel method for integrating driving behavior and traffic context through signal symbolization in order to summarize driving semantics from sensor outputs. The method has been applied to risky lane change detection. Language models (nested Pitman-Yor language model) and speech recognition algorithms (hidden Markov Model) have been utilized for converting continuous sensor signals into a sequence of non-uniform segments (chunks). After symbolization, Latent Dirichlet Allocation (LDA) is used to integrate the symbolized driving behavior and the surrounding vehicle information for establishing the semantics of the driving scene. 988 lane changes of real-world highway driving are used for the evaluation. Risk level of each lane change rated by 10 subjects are used as ground truth. Best results have been obtained when driving behavior and surrounding vehicle information are integrated through co-occurrence chunking after independent symbolization of behavior and context signals.

Prediction of driving behaviors in intersections based on a supervised dimension reduction considering locality

Prediction of driving behavior has been regarded as one of the important issue to realize the next generation of advanced driver assistance systems. However, prediction of driving behaviors is also difficult issue, because the distribution of each driving behavior seems to be not unimodal but multimodal due to its intrinsic complexity and lack of a well-established segmentation method. When we consider to predict driving behaviors with a supervised dimension reduction method and hidden Markov models (HMMs), the multimodal structure of observed distributions should be preserved since they can contain information regarding these behaviors. We therefore propose to combine HMMs with local Fisher discriminant analysis (LFDA) that can maximize the between-class separatability and preserve within-class multimodality. We evaluated the performance of the HMMs with LFDA in predicting actual driving behaviors, and compared its performance with those of with conventional Fisher discriminant analysis and with no dimension reduction. As a result, the LFDA based method showed the best prediction accuracy among the all methods.