single-rb.php

JRM Vol.27 No.6 pp. 636-644
doi: 10.20965/jrm.2015.p0636
(2015)

Paper:

Hazard Anticipatory Autonomous Braking Control System Based on 2-D Pedestrian Motion Prediction

Kazuhiro Ezawa*, Pongsathorn Raksincharoensak**, and Masao Nagai***

*Department of Mechanical Systems Engineering, Faculty of Engineering, Tokyo University of Agriculture and Technology
2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

**Department of Industrial Technology and Innovation, Faculty of Engineering, Tokyo University of Agriculture and Technology
2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

***Japan Automobile Research Institute
1-1-30 Shibadaimon, Minato, Tokyo 105-0012, Japan

Received:
June 29, 2015
Accepted:
October 1, 2015
Published:
December 20, 2015
Keywords:
active safety, driver assistance systems, collision avoidance, autonomous braking
Abstract
The focused scenario

This paper discusses 2-dimensional (2-D) pedestrian motion prediction and autonomous braking control for enhancing the collision avoidance performance of an active safety system. The paper targets a typical scenario involving a pedestrian walking toward a parked vehicle on a crowded urban road. The pedestrian is not expected to continue walking in a straight line. Conventional first-order motion prediction accuracy alone is not enough to predict the pedestrian motion because prediction is based on the pedestrian’s current position and velocity within a finite time. We formulated a 2-D pedestrian motion model of the parked vehicle based on learning the measured trajectory of pedestrians in the same scenario. We then designed an autonomous braking control system based on whether the vehicle will overtake a pedestrian. We evaluated the validity of the proposed autonomous braking control system in simulation experiments.

Cite this article as:
K. Ezawa, P. Raksincharoensak, and M. Nagai, “Hazard Anticipatory Autonomous Braking Control System Based on 2-D Pedestrian Motion Prediction,” J. Robot. Mechatron., Vol.27, No.6, pp. 636-644, 2015.
Data files:
References
  1. [1] Institute for Traffic Accident Research and Data Analysis (ITARDA), “Car-to-Pedestrian Accidents,” ITARDA Information, No.83, pp. 1-12, Japan, 2010.
  2. [2] E. Coelingh, A. Eidehall, and M. Bengtsson, “Collision Warning with Full Auto Brake and Pedestrian Detection – a practical example of Automatic Emergency Braking,” Proc. of IEEE ITSC’10, pp. 155-160, 2010.
  3. [3] S. Makabe, “Active Safety System New EyeSight Version 2,” Society of Automotive Engineers of Japan, Vol.66, No.3, pp. 88-93, 2012.
  4. [4] S. Schramm and F. Roth, “Method to Assess the Effectiveness of Active Pedestrian Protection Safety System” Proc. of ESV Conf. 2009, Paper No.09-0398, 2009.
  5. [5] T. Shimizu, Y. Ohama, S. Nagata, and J. Sakugawa, “A Computational Framework for Estimating Collision Risk against Pedestrians,” R&D Review of Toyota CRDL, Vol.43, No.1, pp. 33-42, 2012.
  6. [6] H. Tsuyuki, R. Hayashi, and M. Nagai, “Development of Hazard-Anticipative Driving Assistance System at Overtaking a Pedestrian on Narrow Roads,” Proc. of 2013 JSAE Annual Congress (Autumn), No.125-13, pp. 5-8, 2013.
  7. [7] R. Matsumi, R. Pongsathorn, and M. Nagai, “Study on Autonomous Intelligent Drive System Based on Potential Field with Hazard Anticipation,” J. of Robotics and Mechatronics, Vol.27, No.1, pp. 5-11, 2015.
  8. [8] N. Tiemann et al., “Predictive Pedestrian Protection – Situation Analysis with a Pedestrian Motion Model,” Proc. of AVEC’10, UK, 2010.
  9. [9] X. Cao, H. Qiao, and J. Keane, “A Low-Cost Pedestrian-Detection System with a Single Optical Camera,” IEEE Trans. on Intelligent Transportation Systems, Vol.9, No.1, pp. 58-67, 2008.
  10. [10] M. Nagai, P. Raksinsharoensak, R. Hayashi, and Y. Ishizaki, “Study on Evaluation Method for Pedestrian/Bicycle Accident Avoidance System Based on Analysis of Near-miss Incident Database,” Proc. of 54th JACC, CD-ROM, pp. 222-226, 2011.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 12, 2018