Finite-Horizon URTSS-Based Position Estimation for Urban Vehicle Localization

Liu, Xinghua; Guan, Jianwei; Jiang, Rui; Ge, Shuzhi Sam; Chen, Badong

doi:10.1109/jsen.2023.3235519

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…Te IDM provides a unifed framework to describe various states of vehicles, ranging from free fow to complete congestion, with a concise set of interpretable parameters. Furthermore, the IDM has been widely utilized to capture the efects of driving behavior between CAVs and HVs [7,20,24]. Terefore, for the sake of modeling convenience, this study assumes that the interactions between HVs and CAVs, as well as between HVs, conform to the IDM.…”

Section: Acceleration Estimationmentioning

confidence: 99%

“…􏽮 􏽯; (6) %Step 2: Calculate the speed correction factor Θ(t), acceleration index correction factorκ(t), and headway distance correction factor β(t)at the current time. (7)…”

Section: Performance Under Diferent Densitiesmentioning

confidence: 99%

“…Tey can only detect limited data of fxed sections at fxed times, including trafc fow and vehicle types. However, the detection range of these sensors is small and the missed detection rate is high, making it impossible to record detailed vehicle spatiotemporal information [7]. Moreover, due to their high installation and maintenance costs and the high coverage of urban road networks, these fxed detectors are difcult to cover the entire urban road network [8].…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

Xia,

Liu,

et al. 2023

Journal of Advanced Transportation

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This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

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Section: Acceleration Estimationmentioning

confidence: 99%

“…􏽮 􏽯; (6) %Step 2: Calculate the speed correction factor Θ(t), acceleration index correction factorκ(t), and headway distance correction factor β(t)at the current time. (7)…”

Section: Performance Under Diferent Densitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle Speed and Position Estimation considering Microscopic Heterogeneous Car-Following Characteristics in Connected Vehicle Environments

Xia,

Liu,

et al. 2023

Journal of Advanced Transportation

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“…In addition to improvements in positioning and communication technology, data processing and fusion can also improve the quality of trajectory data. Using spatial clustering and filtering algorithms can make trajectory data smoother, avoiding deviations or anomalies in trajectory data [12][13][14][15]. By integrating information beyond positioning data, such as electronic maps, Doppler velocimetry, and vehicle inertial measurement units, the quality of trajectory data can also be improved [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Optimization Method for Trajectory Data Based on Satellite Doppler Velocimetry

Li,

Lian

2023

IJACSA

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Due to cost and energy consumption limitations, there are significant differences in the positioning capabilities of mobile terminals, resulting in unsatisfactory quality of trajectory data. In this paper, satellite Doppler data is used to optimize trajectory data. First, the system state equation is established by the kinematic relationship between the measured velocity and position, and the static linear Kalman filter estimates the optimal system state. Then a dynamic Kalman filter system is established by correlating the measurement error matrix parameters of the Kalman filter with the vertical dilution of precision of satellite positioning. Finally, the whole-day trajectory of a taxi in Shenzhen was visualized, and the deviation between the trajectory points and the urban road was calculated to compare the optimized and non-optimized taxi trajectories. The results show that the proposed optimization method can effectively reduce the deviation between trajectory points and urban roads, and this method can be used to process vehicle trajectory data in urban traffic research.

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