Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance for drivers to enhance safety and reduce workload. As the core of all ACC control algorithms, the spacing policy plays a crucial role in various aspects. This paper presents a comprehensive survey on spacing policies for existing ACC solutions in the literature. The objectives of this paper are to clarify the operating mechanisms and characteristics of the common spacing policies, and to reveal their advantages and shortcomings by means of a comparative study. In this survey, the general evaluation criteria for spacing policies are first introduced. Then, the existing spacing policies are categorized into different types according to their operating mechanisms, and their characteristics are carefully reviewed and explained. A comparative study is followed to analyze the performances of five typical spacing policies in the literature, including the constant spacing policy, constant time headway, traffic flow stability, constant safety factor and human driving behavior spacing policies. The contents provided in this paper serve as a tool for understanding current ACC spacing policies, and pave the way for future ACC enhancement.INDEX TERMS Adaptive cruise control, spacing policy, traffic flow stability, string stability, time headway.
Digital wavelet transform (DWT) is a well-known tool for characterizing piecewise-smooth signals and is based on predicting smooth signals. Seismic signals are not smooth. However, they are predictable. Seislet transform is a digital wavelet-like transform, which is tailored specifically for representing seismic data. Its construction is based on the notion of signal prediction. Seislet transform uses predictions of sinusoids (applicable to seismic data in the F-X domain), plane waves (applicable to 2-D or 3-D seismic data in the T-X domain), or reflection events (applicable to prestack seismic data). We combine statistical or physical predictability of seismic signals with the lifting scheme of DWT to define the seislet transform. One can view the seislet transform as decomposition into multiscale orthogonal basis functions aligned with seismic events. When multiple interfering events are present in the data, it is also possible to follow all of them simultaneously by turning the seislet basis into an overcomplete representation (a tight frame). Even though the seislet frame is overcomplete, it can be constrained to have only a small number of significant coefficients and, therefore, to provide an optimally sparse representation. The sparsity is easily demonstrated by comparing the seislet transform and frame with the classic transforms, such as Fourier and DWT. The classic DWT is equivalent to the seislet transform with a zero frequency (in 1-D) or zero slope (in 2-D). The sparsity of the transform domain provides not only an effective seismic data compression tool but also a way for designing efficient data analysis algorithms. Traditional geophysical data analysis tasks, such as signal-noise separation and data regularization, are conveniently formulated in the transform domain, where the signal is sparse. When applied in the offset direction on prestack data, the seislet transform finds an additional application in optimal stacking of seismic records.
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