Abstract:Inter-vehicle distance measuring technique is widely used for Driving Assistance System (DAS) such as Adaptive Cruise Control (ACC) and Forward Collision Warning (FCW) which are useful for safety enhancement as well as driving convenience. In this paper, we propose a new method for providing accurate longitudinal inter-vehicle distance measurement by using a monocular camera and a line laser. Experimental results show that the proposed method outperforms the disparity-based method and the error in distance mea… Show more
“…2 illustrates the results of the pre-processing stage. After we apply the illumination-invariant characteristic of the chromaticity, the region of interest is reduced to nearly half of the original image size [32].…”
“…2 illustrates the results of the pre-processing stage. After we apply the illumination-invariant characteristic of the chromaticity, the region of interest is reduced to nearly half of the original image size [32].…”
“…, dv LV FV (2) where D denotes the inter-vehicle distance, S denotes the expected inter-vehicle space, LV and FV are the speeds of the leading and trailing vehicles, respectively. Note that D here denotes the current longitudinal inter-vehicle distance measured using such as line laser [11] or wireless signal [12] while S indicates the inter-vehicle space by which the driving safety could be guaranteed considering the vehicular dynamics and braking process. The speed of the trailing vehicle FV should be known since the collision warning system has been installed on it, while the speed of the leading vehicle LV could be obtained through GPS sharing [13] or wireless communication systems.…”
In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme for the autonomous or cruising vehicles in Intelligent Transportation Systems (ITSs). Through detailed analysis of the carfollowing cases, our controller is established on some reasonable control rules. In addition, to refine the initialized fuzzy rules considering characteristics of the rear-end collisions, the genetic algorithm is introduced to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our Genetic algorithmoptimized Fuzzy Logic Controller (GFLC) outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency.
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