Use cases for rider assistant mobile application evaluation using travelling simulator

Today's personal mobility vehicles have been considered as a solution for solving the last/first mile problem of a rider in big cities. It is important to investigate the rider factors, rider behavior, and rider-machine interface while using personal mobility vehicles in order to propose useful and safe personal mobility systems (including vehicles and software for the rider assistance). These factors can be evaluated using a simulator that attains realistic environments. The paper presents use cases for the rider assistant using a personal mobile application and their evaluation using the developed travelling simulator. The mobile application for the rider assistant is generated recommendations for the rider based on detected dangerous situation during the riding to prevent an accident. Dangerous situations detection is based on images analysis of the rider face taken from the front camera of the rider's mobile device mounted in the personal mobility vehicle.

[1]  Benita M. Beamon,et al.  Last Mile Distribution in Humanitarian Relief , 2008, J. Intell. Transp. Syst..

[2]  Tomoya Ishikawa,et al.  Service-Field Simulator using MR Techniques : Behavior Comparison in Real and Virtual Environments , 2010 .

[3]  Seiichi Miyakoshi Omni-directional parallel two wheel type inverted pendulum mobile platform using mecanum wheels and omni-wheels , 2016 .

[4]  Osamu Matsumoto,et al.  Forward and backward motion control of Personal riding-type wheeled Mobile Platform , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[5]  Takeshi Kurata,et al.  Service Field Simulator: Virtual Environment Display System for Analyzing Human Behavior in Service Fields , 2014, ICServ.

[6]  Alexander V. Smirnov,et al.  Smartphone-based identification of dangerous driving situations: Algorithms and implementation , 2016, 2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT).

[7]  Osamu Matsumoto,et al.  An experimental study on vehicle behavior to wheel chairs and standing-type vehicles at intersection , 2013, 2013 13th International Conference on ITS Telecommunications (ITST).

[8]  Shin Kato,et al.  A Cooperative Assistance System Between Vehicles for Elderly Drivers , 2009 .

[9]  Jean Underwood,et al.  Driving Experience and Situation Awareness in Hazard Detection , 2013 .

[10]  Ali Boyali,et al.  Block-Sparse Representation Classification based gesture recognition approach for a robotic wheelchair , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[11]  Yorgos Goletsis,et al.  Towards Driver's State Recognition on Real Driving Conditions , 2011 .

[12]  Daniele Ruscio,et al.  Limitations and automation: the role of information about device-specific features in ADAS acceptability , 2016 .

[13]  Akiya Kamimura,et al.  Technology evaluations of personal mobility vehicles in Tsukuba-city mobility robot designated zone — An experimental approach for personal mobility for sharing , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).

[14]  Rainer Stark,et al.  How to Consider Emotional Reactions of the Driver within the Development of Advanced Driver Assistance Systems (ADAS) , 2014 .

[15]  Ali Boyali,et al.  Hand Posture Control of a Robotic Wheelchair Using a Leap Motion Sensor and Block Sparse Representation based Classification , 2014 .