Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
IEEE Transactions on Intelligent Transportation Systems
Estimating the driving state of oncoming vehicles from a moving platform using stereo vision
IEEE Transactions on Intelligent Transportation Systems
Analysis of multiresolution-based fusion strategies for a dual infrared system
IEEE Transactions on Intelligent Transportation Systems
Estimation on location, velocity, and acceleration with high precision for collision avoidance
IEEE Transactions on Intelligent Transportation Systems
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
A fuzzy aid rear-end collision warning/avoidance system
Expert Systems with Applications: An International Journal
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Road situation analysis in Interactive Intelligent Driver-Assistance and Safety Warning (I2DASW) systems involves estimation and prediction of the position and size of various on-road obstacles. Real-time processing, given incomplete and uncertain information, is a challenge for current object detection and tracking technologies. This paper proposed a development framework and novel algorithms for road situation analysis based on driving action behavior, where the safety situation is analyzed by simulating real driving action behaviors. First, we review recent development and trends in road situation analysis to provide perspective for the related research. Second, we introduce a road situation analysis framework, where onboard sensors provide information about drivers, traffic environment, and vehicles. Finally, on the basis of the previous frameworks, we proposed multiple-obstacle detection and tracking algorithms using multiple sensors including radar, lidar, and a camera, where a decentralized track-to-track fusion approach is introduced to fuse these sensors. In order to reduce the effect of obstacle shape and appearance, we cluster lidar data and then classify obstacles into two categories: static and moving objects. Future collisions are assessed by computation of local tracks of moving obstacles using extended Kalman filter, maximum likelihood estimation to fuse distributed local tracks into global tracks, and finally, computation of future collision distribution from the global tracks. Our experimental results show that our approach is efficient for road situation evaluation and prediction