Conference Paper

Driver Behaviour Prediction for Motion Simulators Using Changepoint Segmentation

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... The design of MCAs in driving simulators is a complex task and depends on simulator architecture as well as the kind of maneuver that we want to regenerate [1,2]. The first type of MCA are known as classical washout filters. ...
... The rotational channel filter, which has a 1 st order high-pass filter [17], is given as (2) where is the high-pass cut-off frequency in the rotational channel. Through a low-pass filter in tilt-coordination the sustained component of the acceleration signal can be extracted. ...
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The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers’ experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in understanding the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm (GA) for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limitations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.
... In [27] and [28], two solutions to compute the maximum-a-posteriori (MAP) segmentation of the piece linear regression models in naturalistic driving data were proposed. Hossny et al. utilized pruned exact linear time algorithm (PELT) to detect segmentation points based on driver operation and vehicle movement signals [29]. The segmentation points were employed for sequential filtering to decrease the prediction error. ...
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Due to the growing interest in automated driving, a deep understanding on the characteristics of human driving behavior is critical for human-like autonomous vehicles. Among various driving behaviors, lane change is the most important one for vehicle lateral driving safety. This study proposes an unsupervised method to extract and discover the behavioral patterns of lane change maneuvers for the purpose of exploring the composed behavioral patterns during lane change. This method involves two phases: Firstly, the lane change sequences will be segmented into blocks using time-series segmentation algorithms. Three segmentation algorithms were utilized in this study. In the second phase, the segments will be clustered to find the corresponding behavioral pattern of each segment. Two extended latent Dirichlet allocation (LDA) models were adopted to cluster the segments. The combination of different segmentation and clustering algorithms were evaluated and compared by employing entropy and perplexity as the evaluation criteria. Collected lane change data from naturalistic driving were applied to examine its effectiveness. The results show that this method could effectively mine descriptive behavioral patterns from lane change data. This study provides a promising data mining solution to facilitating deep and comprehensive understanding on driver lane change behaviors, which will promote the development of human-like autonomous vehicles.
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Understanding drivers’ behavioral characteristics is critical for the design of decision-making modules in autonomous vehicles (AVs) and advanced driver assistance systems (ADASs). Current relevant studies are mainly based on supervised learning methods which involve extensive human efforts in model development. This paper proposed a framework for automatic descriptive driving pattern extraction from driving sequence data using unsupervised algorithms. Based on the Bayesian multivariate linear regression model, two unsupervised algorithms were utilized to segment driving sequences into fragments. Three extended latent Dirichlet allocation models were applied to cluster the fragments into multiple descriptive driving patterns. The collected driving data from a naturalistic driving experiment was applied to examine the effectiveness of our proposed framework. Results show that the unsupervised segmentation algorithms could help effectively detect the switch characteristics between two continuous driving maneuvers along time, and the clustered patterns could effectively describe the characteristics of each driving maneuver. The proposed unsupervised framework provides an effective and efficient data mining solution to facilitating deep and comprehensive understanding on drivers’ behavioral characteristics, which will benefit the development of AVs and ADASs. The personalized Share Link for 50 days' free access: https://authors.elsevier.com/a/1cXEs39%7Et0VMpw
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Many time series are characterised by abrupt changes in structure, such as sudden jumps in level or volatility. We consider changepoints to be those time points which divide a dataset into distinct homogeneous segments. In practice the number of changepoints will not be known. The ability to detect changepoints is important for both methodological and practical reasons including: the validation of an untested scientific hypothesis [27]; monitoring and assessment of safety critical processes [14]; and the validation of modelling assumptions [21]. The development of inference methods for changepoint problems is by no means a recent phenomenon, with early works including [39], [45] and [28]. Increasingly the ability to detect changepoints quickly and accurately is of interest to a wide range of disciplines. Recent examples of application areas include numerous bioinformatic applications [37, 15], the detection of malware within software [51], network traffic analysis [35], finance [46], climatology [32] and oceanography [34]. In this chapter we describe and compare a number of different approaches for estimating changepoints. For a more general overview of changepoint methods, we refer interested readers to [8] and [11]. The structure of this chapter is as follows. First we introduce the model we focus on. We then describe methods for detecting a single changepoint and methods for detecting multiple changepoint, which will cover both frequentist and Bayesian approaches.
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One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been de-veloped to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementa-tion of the recently proposed PELT algorithm. This article describes the search methods which are implemented in the package as well as some of the available test statistics whilst highlighting their application with simulated and practical examples. Particular empha-sis is placed on the PELT algorithm and how results differ from the binary segmentation approach.
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  • Rebecca Killick