Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 18, Issue 2, Pages 460-476Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2016.2605163
Keywords
Behavior reasoning; curvilinear coordinate; geometry constraints; interacting multiple model (IMM) filter; roadway map; vehicle tracking
Categories
Funding
- National Research Foundation of Korea - Korean Government (Ministry of Education, Science and Technology) [2011-0017495]
- Practical Development of Wireless Low-Floor Tram - Korean Ministry of Land, Infrastructure and Transport [15RTRP-B067379-03]
- BK21 plus program under the Ministry of Education, Republic of Korea [22A20130000045]
- National Research Foundation of Korea [2011-0017495] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Tracking and behavior reasoning of surrounding vehicles on a roadway are keys for the development of automated vehicles and an advanced driver assistance system (ADAS). Based on dynamic information of the surrounding vehicles from the tracking algorithm and driver intentions from the behavior reasoning algorithm, the automated vehicles and ADAS can predict possible collisions and generate safe motion to avoid accidents. This paper presents a unified vehicle tracking and behavior reasoning algorithm that can simultaneously estimate the vehicle dynamic state and driver intentions. The multiple model filter based on various behavior models was used to classify the vehicle behavior and estimate the dynamic state of surrounding vehicles. In addition, roadway geometry constraints were applied to the unified vehicle tracking and behavior reasoning algorithm in order to improve the dynamic state estimation and the behavior classification performance. The curvilinear coordinate system was constructed based on the precise map information in order to apply the roadway geometry to the tracking and behavior reasoning algorithm. The proposed algorithm was verified and evaluated through experiments under various test scenarios. From the experimental results, we concluded that the presented tracking and behavior reasoning algorithm based on the roadway geometry constraints provides sufficient accuracy and reliability for automated vehicles and ADAS applications.
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