In this paper, we propose a novel motion planning method for off-road unmanned ground vehicles, based on three-dimensional (3-D) terrain map information. Previous studies on the motion planning of a vehicle traveling on rough terrain dealt only with a relatively small environment. Furthermore, unique vehicle characteristics were not considered, and it was also impossible to incorporate regulations, such as maintaining driving speed and suppressing posture change. The proposed method enables vehicles to adaptively generate a path by considering vehicle characteristics and the regulations, in a large-scale environment, with rough terrain. A random sampling based scheme was applied to carrying out global path planning, based on a 3-D environmental model. Experimental results showed that the proposed off-road motion planner could generate an appropriate path, which satisfies vehicle characteristics and predefined regulations.

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 adriving color mapby 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.

In-vehicle speech-based interaction between a driver and a driving agent should be performed without affecting the driving behavior. A driving agent provides information to the driver and helps his/her driving behavior and non-driving-related tasks, e.g., selecting music and giving weather information. In this paper, we focus on a method for determining utterance timings when a driving agent provides non-driving-related information. If a driving agent provides a driver with non-driving-related information at an inappropriate moment, it will distract his/her driving behavior and deteriorate his/her safety driving. To solve or to mitigate the problem, we propose a novel method for determining the utterance timing of a driving agent on the basis of a double articulation analyzer, which is an unsupervised nonparametric Bayesian machine learning method for detecting contextual change points. To verify the effectiveness of the method, we conduct two experiments. One is an experiment on a short circuit around a park in an urban area, and the other is an experiment on a long course in a town. The results show that the proposed method enables a driving agent to avoid inappropriate timing better than baseline methods.

We present a novel rough terrain traversability analysis method for mobile robot navigation. We focused on the scenario of mobile robot operation in a disaster environment with limited sensor data. The robot builds a map in real time and analyzes the terrain traversability of its surrounding environment. The proposed method is based on fuzzy inference so that it can handle uncertainties in the sensor data. Two values associated with the terrain traversability, roughness and slope, are calculated from an elevation map built by a laser range finder mounted on the mobile robot. These two values are inputted to the fuzzy inference, and the traversability is analyzed. Based on the traversability output from the fuzzy inference, a vector field histogram (VFH) is generated. The mobile robot course is determined according to the VFH. We demonstrated our algorithm on an artificial environment. The experimental results showed that the mobile robot was able to reach the target position safely while avoiding untraversable areas.

Driving behavior can be represented by many different types of measured sensor information obtained through a control area network. We assume that the measured sensor information is generated from several hidden time-series data through multiple nonlinear transformations. These hidden time-series data are statistically independent of each other and capture essential driving behavior. Driving behavior information is usually generated by multiple nonlinear transformations that fuse essential features, e.g., "Yaw rate" is generated by fusing the velocity of the vehicle and the change of driving direction. However, driving behavior data is often redundant because such data includes multivariate information and involves duplicated essential features. In this paper, we propose a feature extraction method to extract essential features from redundant driving behavior data using a deep sparse autoencoder (DSAE), which is a deep learning method. Two-dimensional features are extracted from seven-dimensional artificial data using a DSAE and are determined experimentally to be highly correlated with the prepared essential features. DSAEs are also used to extract features from an actual driving behavior data set. To verify a DSAE's ability to extract essential driving behavior features and filter out redundant information, we prepare twelve data sets that include some or all of the driving behavior information. Twelve DSAEs are used to independently extract features from the twelve prepared data sets, and canonical correlation analysis is used to analyze the canonical correlation coefficients between extracted features. Furthermore, we verify DSAEs' ability to extract essential driving behavior features from the redundant driving behavior data sets.

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.