Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction
Although traffic conflict techniques have proven to be effective means for road safety analysis, they still suffer from incomplete conceptualization, observer subjectivity, and high data collection cost. To address these problems, video analysis has been increasingly applied to gain a better underst...
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doaj-920008bd99d54ad9b151545b7cc5f1e62021-03-30T14:59:51ZengIEEEIEEE Access2169-35362021-01-019345583456910.1109/ACCESS.2021.30612669360592Vision-Based Traffic Conflict Detection Using Trajectory Learning and PredictionZongyuan Sun0https://orcid.org/0000-0001-6773-9254Yuren Chen1https://orcid.org/0000-0001-6176-8302Pin Wang2Shouen Fang3Boming Tang4Department of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, ChinaSchool of Transportation Engineering, Tongji University, Shanghai, ChinaCalifornia PATH, University of California at Berkeley, Berkeley, CA, USASchool of Transportation Engineering, Tongji University, Shanghai, ChinaDepartment of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, ChinaAlthough traffic conflict techniques have proven to be effective means for road safety analysis, they still suffer from incomplete conceptualization, observer subjectivity, and high data collection cost. To address these problems, video analysis has been increasingly applied to gain a better understanding of the behaviors of road users based on detailed motion data. However, the motion patterns underlying these data are rarely extracted to study the safety of their interactions. This article presents a vision-based method of traffic conflict detection through learning motion patterns from trajectories, for which an original algorithm was established through clustering and subsequent modeling. Using the extracted path and velocity information, we clustered trajectories hierarchically by applying an improved fuzzy K-means algorithm with a modified Hausdorff distance. Each obtained cluster was taken as a labeled set to determine the structure and train the parameters of a hidden Markov model (HMM) that encoded the spatiotemporal characteristics of the trajectories as motion patterns. Based on the targeted trajectory predictions by the learned HMMs following the conflict development, a probabilistic model was developed to estimate the collision likelihood between vehicles to identify traffic conflicts. The experimental results obtained using actual traffic videos demonstrated the applicability of the algorithms for learning motion patterns and the feasibility of the approach for traffic conflict detection. The predicted trajectories were sufficiently accurate to calculate the collision probability, which was qualified as an indicator for measuring the conflict severity. These findings will have important implications for effective improvements in active road safety.https://ieeexplore.ieee.org/document/9360592/Collision estimationhidden Markov model (HMM)motion patterntraffic conflict detectiontrajectory learningvideo analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zongyuan Sun Yuren Chen Pin Wang Shouen Fang Boming Tang |
spellingShingle |
Zongyuan Sun Yuren Chen Pin Wang Shouen Fang Boming Tang Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction IEEE Access Collision estimation hidden Markov model (HMM) motion pattern traffic conflict detection trajectory learning video analysis |
author_facet |
Zongyuan Sun Yuren Chen Pin Wang Shouen Fang Boming Tang |
author_sort |
Zongyuan Sun |
title |
Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction |
title_short |
Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction |
title_full |
Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction |
title_fullStr |
Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction |
title_full_unstemmed |
Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction |
title_sort |
vision-based traffic conflict detection using trajectory learning and prediction |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Although traffic conflict techniques have proven to be effective means for road safety analysis, they still suffer from incomplete conceptualization, observer subjectivity, and high data collection cost. To address these problems, video analysis has been increasingly applied to gain a better understanding of the behaviors of road users based on detailed motion data. However, the motion patterns underlying these data are rarely extracted to study the safety of their interactions. This article presents a vision-based method of traffic conflict detection through learning motion patterns from trajectories, for which an original algorithm was established through clustering and subsequent modeling. Using the extracted path and velocity information, we clustered trajectories hierarchically by applying an improved fuzzy K-means algorithm with a modified Hausdorff distance. Each obtained cluster was taken as a labeled set to determine the structure and train the parameters of a hidden Markov model (HMM) that encoded the spatiotemporal characteristics of the trajectories as motion patterns. Based on the targeted trajectory predictions by the learned HMMs following the conflict development, a probabilistic model was developed to estimate the collision likelihood between vehicles to identify traffic conflicts. The experimental results obtained using actual traffic videos demonstrated the applicability of the algorithms for learning motion patterns and the feasibility of the approach for traffic conflict detection. The predicted trajectories were sufficiently accurate to calculate the collision probability, which was qualified as an indicator for measuring the conflict severity. These findings will have important implications for effective improvements in active road safety. |
topic |
Collision estimation hidden Markov model (HMM) motion pattern traffic conflict detection trajectory learning video analysis |
url |
https://ieeexplore.ieee.org/document/9360592/ |
work_keys_str_mv |
AT zongyuansun visionbasedtrafficconflictdetectionusingtrajectorylearningandprediction AT yurenchen visionbasedtrafficconflictdetectionusingtrajectorylearningandprediction AT pinwang visionbasedtrafficconflictdetectionusingtrajectorylearningandprediction AT shouenfang visionbasedtrafficconflictdetectionusingtrajectorylearningandprediction AT bomingtang visionbasedtrafficconflictdetectionusingtrajectorylearningandprediction |
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