In this paper, we propose a visualization method for driving behavior that helps people to recognize distinctive driving behavior patterns in continuous driving behavior data. Driving behavior can be measured using various types of sensors connected to a control area network. The measured multi-dimensional time series data are called driving behavior data. In many cases, each dimension of the time series data is not independent of each other in a statistical sense. For example, accelerator opening rate and longitudinal acceleration are mutually dependent. We hypothesize that only a small number of hidden features that are essential for driving behavior are generating the multivariate driving behavior data. Thus, extracting essential hidden features from measured redundant driving behavior data is a problem to be solved to develop an effective visualization method for driving behavior. In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the DSAE, we propose a visualization method called a driving color map by mapping the extracted 3-D hidden feature to the red green blue (RGB) color space. A driving color map is produced by placing the colors in the corresponding positions on the map. The subjective experiment shows that feature extraction method based on the DSAE is effective for visualization. In addition, its performance is also evaluated numerically by using pattern recognition method. We also provide examples of applications that use driving color maps in practical problems. In summary, it is shown the driving color map based on DSAE facilitates better visualization of driving behavior.

This paper introduces a method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors. Deep learning techniques were used to extract latent features from the collected data. Extracted features were clustered into behaviors and used to create velocity profiles, allowing an autonomous driving agent could drive in a human-like manner. By using proactive driving behaviors, the agent could limit potential sources of discomfort such as jerk and uncomfortable velocities. Additionally, we proposed a method to compare trajectories where not only the geometric similarity is considered, but also velocity, acceleration and jerk. Experimental results in a simulator implemented in ROS show that the autonomous agent built with the driving behaviors was capable of driving similarly to expert human drivers.

Direct word discovery from audio speech signals is a very difficult and challenging problem for a developmental robot. Human infants are able to discover words directly from speech signals, and, to understand human infants’ developmental capability using a constructive approach, it is very important to build a machine learning system that can acquire knowledge about words and phonemes, i.e. a language model and an acoustic model, autonomously in an unsupervised manner. To achieve this, the nonparametric Bayesian double articulation analyzer (NPB-DAA) with the deep sparse autoencoder (DSAE) is proposed in this paper. The NPB-DAA has been proposed to achieve totally unsupervised direct word discovery from speech signals. However, the performance was still unsatisfactory, although it outperformed pre-existing unsupervised learning methods. In this paper, we integrate the NPB-DAA with the DSAE, which is a neural network model that can be trained in an unsupervised manner, and demonstrate its performance through an experiment about direct word discovery from auditory speech signals. The experiment shows that the combined method, the NPB-DAA with the DSAE, outperforms pre-existing unsupervised learning methods, and shows state-of-the-art performance. It is also shown that the proposed method outperforms several standard speech recognizer-based methods with true word dictionaries. Graphical Abstract

Interactions between pedestrians and autonomous personal mobility vehicle (APMV) will increase with the popularity of autonomous driving systems. However, when the APMVs are applied in a mixed traffic environment after manual driving PMV (MPMV) have been popular, pedestrians may feel unsafe in the interactions when they are uncertain about the driving intention of the APMV. This study seeks to find a surrogate measure for pedestrians’ understanding of driving intention and perceived safety during the interaction with an APMV. We conducted an experiment to measure the gaze duration and subjective evaluations of the participants when they interacted with a PMV in manual and autonomous driving modes. Pedestrians fixed their gaze at the APMV longer when they did not accurately understand the driving intention than when they understood it. Furthermore, the pedestrians perceived danger when they did not clearly understand the driving intention of the APMV. Besides, these factors were different when pedestrians interact with an MPMV and an APMV.

Driving behavioral data is too high-dimensional for people to review their driving behavior. It includes accelerator opening rate, steering angle, brake Master-Cylinder pressure and other various information. The high-dimensional data is not very intuitive for drivers to understand their driving behavior when they take a look back on their recorded driving behavior. We used a deep sparse autoencoder to extract the low-dimensional high-level representation from high-dimensional raw driving behavioral data obtained from a control area network. Based on this low-dimensional representation, we propose two visualization methods called Driving Cube and Driving Color Map. Driving Cube is a cubic representation displaying extracted three-dimensional features. Driving Color Map is a colored trajectory shown on a road map representing the extracted features. The trajectory is colored using the RGB color space, which corresponds to the extracted three-dimensional features. To evaluate the proposed method for extracting low-dimensional feature, we conducted an experiment and found several differences with recorded driving behavior by viewing the visualized Driving Color Map and that our visualization methods can help people to recognize different driving behavior. To evaluate the effectiveness of low-dimensional representation, we compared deep sparse autoencoder with other conventional methods from the viewpoint of linear separability of elemental driving behavior. As a result, our methods outperformed other conventional methods.

Passengers of level 3-5 autonomous personal mobility vehicles (APMV) can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness, especially when frequently avoiding pedestrians or obstacles in shared spaces. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Among them, models based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs v.s. those expected by the central nervous system, have been actively developed. To model motion sickness due to conflict between visual vertical information and vestibular sensation, we proposed a 6 DoF SVC-VV model which added a visually perceived vertical block into a conventional 6 DoF SVC model to predict visual vertical directions from image data simulating the visual input of a human. In a driving experiment, 27 participants rode on the APMV and experienced slalom driving with two visual conditions: looking ahead (LAD) and working with a tablet device (WAD). We verified that passengers got motion sickness while riding the APMV, and the symptoms were severer when especially working on it, by simulating the frequent pedestrian avoidance scenarios of the APMV in the experiment. In addition, the results of the experiment demonstrated that the proposed 6 DoF SVC-VV model could describe the increased motion sickness experienced when the visual vertical and gravitational acceleration directions were different.