Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
IEEE Transactions on Intelligent Transportation Systems
Model-based probabilistic collision detection in autonomous driving
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
IEEE Transactions on Intelligent Transportation Systems
Multi-sensor IMM estimator for uncertain measurement
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A modified model for the Lobula Giant Movement Detector and its FPGA implementation
Computer Vision and Image Understanding
Map estimation using GPS-equipped mobile wireless nodes
Pervasive and Mobile Computing
IEEE Transactions on Intelligent Transportation Systems
An early collision warning algorithm for vehicles based on V2V communication
International Journal of Communication Systems
Engineering Applications of Artificial Intelligence
Multiple hypothesis tracking for data association in vehicular networks
Information Fusion
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Path prediction is the only way that an active safety system can predict a driver's intention. In this paper, a model-based description of the traffic environment is presented - both vehicles and infrastructure - in order to provide, in real time, sufficient information for an accurate prediction of the ego-vehicle's path. The proposed approach is a hierarchical-structured algorithm that fuses traffic environment data with car dynamics in order to accurately predict the trajectory of the ego-vehicle, allowing the active safety system to inform, warn the driver, or intervene when critical situations occur. The algorithms are tested with real data, under normal conditions, for collision warning (CW) and vision-enhancement applications. The results clearly show that this approach allows a dynamic situation and threat assessment and can enhance the capabilities of adaptive cruise control and CW functions by reducing the false alarm rate.