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Intelligent Transportation Research Center

• Leadership: Dr. Ichiro Masaki, Director

Transportation is an important infrastructure for our society. It is time to propose a new transportation scheme for resolving the increasing transportation problems. In responding to social needs, MIT’s Microsystems Technology Laboratories established the Intelligent Transportation Research Center (ITRC) in September 1998 as a contact point of industry, government, and academia for ITS research and development.

ITRC focuses on the key Intelligent Transportation Systems (ITS) technologies, including an integrated network of transportation information, automatic crash and incident detection, notification and response, advanced crash avoidance technology, advanced transportation monitoring and management, etc., in order to improve safety, security, efficiency, mobile access, and environment. There are two emphases for research conduced in the center:

• The integration of component technology research and system design research.
• The integration of technical possibilities and social needs.

ITRC proposes the incremental conversion and development process from current to near- and far-future systems and develops enabling key components in collaboration with the government, industries, and other institutions. Other necessary steps are the integration of technical, social, economical, and political aspects. The integration of the Intelligent Transportation Systems in different countries is also essential. The integration of vehicles, roads, and other modes of transportation, such as railways and public buses, is all imperative.

These integrations are fulfilled with the cooperation of researchers in various fields, including the Microsystems Technology Laboratory (MTL), the Research Laboratory of Electronics (RLE), the Artificial Intelligence Laboratory (AI), the Center for Transportation Studies (CTS), the Age Laboratory, the Department of Electrical Engineering and Computer Science, the Department of Civil and Environmental Engineering, the Department of Aeronautics and Astronautics, and the Sloan School of Management. The research center has 8 MIT faculty and several visiting professors and scientists. The director of the center is Dr. Ichiro Masaki.

Ichiro Masaki

• Title: Director
• Department: Intelligent Transportation Research Center

Collaborators

• J. F. Coughlin
• B. K. P. Horn
• H.-S. Lee
• C. G. Sodini
• J. M. Sussman

Graduate Students

• Y. Fang, Research Assistant, EECS
• B. Bilgic, Research Assistant, EECS

Visting Scientists

• T.H. Fang
• S. Yokomitsu

Support Staff

• V. DiNardo, Administrative Assistant II

Publications

Yajun Fang, Sumio Yokomitsu, Berthold Horn, Ichiro Masaki, “A Layered-based Fusion-based Approach to Detect and Track the Movements of Pedestrians through Partially Occluded Situations.” IEEE Intelligent Vehicles Symposium 2009 (IV2009). Best Conference Poster Award” for The IEEE 2009 Intelligent Vehicles Symposium

Berthold Horn, Yajun Fang, Ichiro Masaki, “Hierarchical framework for direct gradient-based time-to-contact estimation.” IEEE Intelligent Vehicles Symposium 2009 (IV2009).

Fang. Y., Horn. B.K.P., Masaki I., “Systematic information fusion methodology for static and dynamic obstacle detection in ITS.” 15th World Congress On ITS, 2008. First Prize for “Student Essay Competition” 15th  World Congress on ITS

Horn. B.K.P., Fang. Y., Masaki I., “Time to Contact Relative to a Planar Surface.” Berthold Horn, Yajun Fang, Masaki. I., IEEE Intelligent Vehicles Symposium 2007 (IV2007).

Herrington, W.F., Jr., Horn. B. K. P. and Masaki. I., “Application of the Discrete Haar Wavelet Transform to Image Fusion for Nighttime Driving”, IEEE Intelligent Vehicles Symposium 2005 (June 6-8, 2005),

Fang, Y., K. Yamada, Y. Ninomiya,   B.  Horn and Masaki. I., “A Shape-Independent-Method for Pedestrian Detection with Far Infrared-images.”  Special issue on “In-Vehicle Computer Vision Systems” of IEEE Transactions on Vehicular Technology, Vol.53, No.6, Nov. 2004, pp.1679-1697.

Fang, Y., K. Yamada, Y. Ninomiya, Horn. B. K. P. and Masaki. I., “Comparison between Infrared-image-based and Visible-image-based Approaches for Pedestrian Detection,” IEEE Intelligent Vehicles Symposium 2003 (IV2003, pp.505-510). First Prize, Best Paper Award by IEEE ITS Council

Kato. T., Ninomiya. Y., Masaki. I., “An Obstacle Detection Method by Fusion of Radar and Motion Stereo.” SICE Annual Conference in Fukui, August 4-6, 2003

Fang, Y., Masaki. I. and  Horn. B. K. P., “Depth-Based Target Segmentation for Intelligent Vehicles: Fusion of Radar and Binocular Stereo,” IEEE Transactions on Intelligent Transportation Systems, Vol.3, No.3, Sept. 2002, pp.196-202.

Kato. T., Ninomiya. Y., Masaki. I., “An Obstacle Detection Method by Fusion of Radar and Motion Stereo.”   IEEE Transactions on Intelligent Transportation Systems, Vol.3, No.3, Sep. 2002, pp.182-188.

Kato. T., Ninomiya. Y., Masaki. I., “Preceding vehicle recognition based on learning from sample images.” IEEE Transactions on Intelligent Transportation Systems, Vol.3, No.4, Dec. 2002, pp.252-260.

Fang, Y., Masaki. I. and Horn. B. K. P., “Distance/Motion Based Segmentation under Heavy Background Noise,” IEEE Intelligent Vehicles Symposium 2002 (IV2002, pp.483-488).

Fang, Y., Ninomiya. Y., Masaki. I., “Intelligent Transportation Systems, Challenges and Opportunities,” The 2^nd International Symposium on Multimedia Mediation Systems, 2002 (pp.72-77). Invited Paper

Fang, Y., Masaki. I. and Horn. B. K. P., “Distance Range Based Segmentation in Intelligent Transportation Systems: Fusion of Radar and Binocular Stereo,” IEEE Intelligent Vehicles Symposium 2001 (IV2001, pp.171-176).

Yajun Fang, Marcelo Mizuki, Berthold Horn, Ichiro Masaki, “TV Camera-based Vehicle Motion Detection and its Chip Implementation.” IEEE Intelligent Vehicles Symposium 2000  (IV2000, pp.134-139).

Computer Vision for Vehicles

• Authors: Y. Fang, B. Bilgic, B. K. P. Horn, I. Masaki
• Sponsorship: Intelligent Transportation Research Center, MTL



Figure 1: Segmentation result from single camera for urban day-time driving

Under the pressure of increasing population, crowded traffic, the energy crisis, and environmental concerns, current transportation systems have run into serious challenges in the following respects: safety, security, efficiency, mobile access, and the environment ^[1] . There have been over 200,000 pedestrian fatalities in the last 30 years in US. Eighty percent of police reports ^[1] cited driver errors as the primary cause of vehicle crashes. With the availability of faster computers, better sensor technology, and wider coverage of the wireless communication network, Intelligent Vehicles and Intelligent Transportation Systems (ITS) are gradually being seen as a crucial innovation to improve safety and to reduce damages. It is estimated that implementing collision-avoidance systems in vehicles could prevent 1.1 million accidents in the US each year — 17 percent of all traffic accidents, which could save 17,500 lives and $26 billion in accident-related costs ^[2] . The demand for in-car electronic products is increasing. Around 35 percent of the cost of car assembly comes from electronics ^[3] .



Figure 2: Segmentation results for urban day-time driving, for night driving, and for pedestrian tracking.

Environment-understanding technology is very vital to provide Intelligent Vehicles with the ability to respond automatically to fast-changing environments and dangerous situations. To obtain perceptual abilities, it is expected to automatically detect static and dynamic obstacles and obtain their related information, such as locations, speed, collision/occlusion possibility, and other dynamic current/historic information. Conventional methods independently detect individual pieces of information, which are normally noisy and not very reliable. Instead we propose a fusion-based and layered-based information-retrieval methodology to systematically detect obstacles and obtain their location/timing information for visible and infrared sequences. The proposed obstacle-detection methodologies take advantage of connections between different kinds of information and increase the computational accuracy of obstacle information estimation, thus improving environment-understanding abilities and driving safety. Three examples are shown in Figures 1 and 2. ^{[4] [5] [6] [7]}

1. I.~T.~S. of~America, “National intelligent transportation system program plan: A ten-year vision,” the United States Department of Transportation, Tech. Rep., January 2002. [↩] [↩]
2. The Intelligent Vehicle Initiative: Advancing “Human-Centered” Smart Vehicles. Available: http://www.tfhrc.gov/pubrds/pr97-10/p18.htm [↩]
3. “Asia – New Hotbed for Consumer Automotive Electronics.” Available: http://www.technewsworld.com/story/52539.html [↩]
4. Yajun Fang, Sumio Yokomitsu, Berthold Horn, Ichiro Masaki, “A Layered-based Fusion-based Approach to Detect and Track the Movements of Pedestrians through Partially Occluded Situations.” IEEE Intelligent Vehicles Symposium 2009 (IV2009). [↩]
5. Fang. Y., Horn. B.K.P., Masaki I., “Systematic information fusion methodology for static and dynamic obstacle detection in ITS.” 15th World Congress On ITS, 2008. [↩]
6. B.K.P.Horn, Y. Fang, I. Masaki, “Time to Contact Relative to a Planar Surface.” IEEE Intelligent Vehicles Symposium 2007. [↩]
7. Y. Fang, K. Yamada, Y. Ninomiya, B.K.P. Horn, and I. Masaki, “A Shape-Independent-Method for Pedestrian Detection with Far Infrared-images.” Special issue on “In-Vehicle Computer Vision Systems” of IEEE Transactions on Vehicular Technology, Vol.53, No.6, Nov. 2004, pp.1679-1697. [↩]