Powered Two-Wheelers Critical Events Detection and Recognition Using Data-Driven Approaches

Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of theroad environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: 1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and 2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether 'naturalistic' or 'critical' (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies.Driving errors are considered to be the greatest contributory cause in all road accidents and an important contributory cause of most fatal accidents. This is particularly the case for the users of powered two-wheeled vehicles (PTWs), perhaps because PTW riders play a greater role in the control of their vehicles' stability than four-wheeled vehicle drivers. Thus, observing and analyzing the evolution of riders' behavior in a real-life context is an important step in the identification of theroad environment characteristics that constitute a risk factor for PTW riders. A relevant research issue in naturalistic studies is related to the detection and identification of critical riding events from among the vast amount of data recorded during the experiment. In this paper, two approaches were used to automatically detect such critical riding events. First, we formalized this problem in terms of detecting changes in the mean and variance of the signals generated by the acceleration and angular velocity sensors. For this purpose, two steps were performed: 1) a data segmentation and feature extraction step in which the multidimensional time series of accelerometer and angular velocity data were segmented and modeled using a Gaussian mixture model with quadratic logistic proportions; and 2) a classification step in which each detected segment was assigned to the appropriate riding sequence, whether 'naturalistic' or 'critical' (i.e., fall or near fall), using the k-nearest neighbor algorithm. The second approach was based on online fall detection. This methodology used control charts (a multivariate cumulative sum), an approach that has been traditionally employed for sequential detection. These two algorithms were applied to a database composed of data from a real-life driving experiment. The obtained results show the effectiveness of both methodologies

Creating informed public acceptance by a user-centered human-machine interface for all automated transport modes : Paper presented at the Transport Research Arena (TRA), 27–30 April 2020, Helsinki, Finland

Increasing automation is ongoing in all areas of transport. This raises new challenges for the design and training of Human-Machine Interfaces (HMI) for different user groups. The EU-project Drive2theFuture investigates the needs and wants of transportation users, operators, passengers and passersby to gain their acceptance and to set the ground for a sustainable market introduction of automated transport. This paper describes how HMI concepts for the transport modes road, rail, maritime and aviation in Drive2theFuture are developed and comparatively assessed in order to be able to support an educated use of automated transport. By relying on a stepwise process, adaptable HMI strategies for different user clusters and levels of automation are defined. As a universal method, a comprehensive HMI development toolkit is developed, which can be adopted as training tool to create realistic expectations and enhance acceptance among users, operators and drivers in light of the deployment of automated vehicles