Comparison of Curve Representations for Memory-Efficient and High-Precision Map Generation

Stannartz, Niklas; Theers, Mario; Llarena, Adalberto; Sons, M. Witmark; Kühn, Markus; Bertram, Torsten

doi:10.1109/itsc45102.2020.9294250

Cited by 2 publications

(4 citation statements)

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“…If it is not possible to always meet the error constraint requirement, uniform equidistant interpolation should be performed on the resulting data set to increase the node density in the interval, as shown by 1 in Figure 5. This process is continued until a resulting datapoint set that meets the error requirement is obtained, as shown by 4 in Figure 5.…”

Section: Random Shape-dividing Algorithm Of Lane Linesmentioning

confidence: 99%

“…We evaluated the storage efficiency of the proposed method by using the proposed method and the B-spline modeling method [4] to separately model a lane line in the obtained dataset, as shown in Figure 7. The data storage demand of the NURBS curve mainly came from the control points (two floating-point numbers) and weight factor (an integer).…”

Section: • Memory Efficiencymentioning

confidence: 99%

“…In order to meet the requirements of real-world applications, researchers have carried out extensive research in this field. A few researchers have proposed a host of curve models to accurately represent the lane line elements in the map, such as multiple segment polygons [4,5], circular arc splines [6,7], cubic B-spline [8,9], clothoid splines [10], B-spline, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…In order to ensure smoothness, some researchers use clothoids to connect a straight line and an arc in order to represent a lane line [11]. However, one drawback of defining clothoids [12,13] using the Fresnel integral is the huge workload of mapping calculation and operation; therefore, this method was not pursued further in this study [4]. The curve constructed using segment polynomials can be used to approximate real lane lines.…”

Section: Introductionmentioning

confidence: 99%

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An Autonomous Storage Optimization LRM Generation Strategy Based on Cascaded NURBS

et al. 2022

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Automatic control of intelligent vehicles depends heavily on accurate lane-level location information. The traditional digital map cannot meet the automatic driving requirements, and it is very difficult for lane-level road maps (LRMs) to achieve a good balance between the representation accuracy and data storage volume. To tackle this problem, we obtain the recursive definition of a lane line by analyzing road lanes using the fractal geometry theory. Subsequently, we construct a recursive feedback system and model it. On this basis, an autonomous storage optimization LRM generation strategy based on cascaded NURBS is devised. According to the input data density, this framework generates an LRM with the minimum data storage volume within the error constraint. It is mainly composed of two modules: (1) a random fractal and (2) an adaptive iteration memory optimizer, based on cascaded NURBS error feedback. First, the whole lane is divided into several meta lane line modules. Second, data sampling/interpolation is carried out iteratively according to the NURBS fitting error and symmetry of road geometry. In this way, the storage space used to store lane lines and other information is automatically optimized. Last, a simulation experiment is carried out by using a dataset obtained from a park. The simulation results show that the proposed method can achieve high accuracy and high storage efficiency. Under the constraint of a 0.1 m curve fitting error, the average memory demand of the lane line elements is less than 2.4 bytes/m.

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Section: Random Shape-dividing Algorithm Of Lane Linesmentioning

confidence: 99%

Section: • Memory Efficiencymentioning

confidence: 99%