Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (12): 3852-3861.doi: 10.13229/j.cnki.jdxbgxb.20240361

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Vehicle travel speed estimation based on self⁃supervised long short⁃term memory network under single global navigation satellite system signal

Jing TIAN(),She-qiang MA(),Dan ZHAO,Fa-cheng CHEN   

  1. School of Transportation Management，People's Public Security University of China，Beijing 100038，China
  • Received:2024-04-07 Online:2025-12-01 Published:2026-02-03
  • Contact: She-qiang MA E-mail:jingt202310@163.com;masheqiang@163.com

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Abstract:

The vehicle speed detection in the single Global Navigation Satellite System （GNSS） environment is subject to significant noise interference， and the sparsity of data sampling will further exacerbate the instability of speed calculation. To address this issue， the vehicle speed estimation under the single GNSS scenario is formulated as a speed expectation optimization problem based on spatiotemporal correlation， and a self-supervised bidirectional Long Short-Term Memory （LSTM） algorithm is proposed for its solution. Firstly， a sparse data spatiotemporal feature extraction LSTM is constructed by this algorithm， where time gate and spatial gate functions are introduced to analyze the changes in speed spatiotemporal correlations in sparse and unequal-interval vehicle detection data， and embedded vectors for speed spatiotemporal features are extracted. Secondly， the trend of vehicle speed changes is analyzed from both forward and backward directions by the noise bidirectional suppression LSTM network for vehicle speed estimation， enabling the accurate achievement of noise elimination and speed estimation. Finally， experimental verification of the vehicle speed estimation performance of the proposed algorithm was conducted using GPS signals as an example. The results show that an average noise reduction of 26.73 dB PSNR was achieved by the proposed algorithm in sparse speed data with sampling intervals ranging from 1 minute to 10 minutes， which is 28.93% higher than that achieved by the LWR， EnKF， and Noise2Void algorithms on average. Additionally， the speed estimation accuracy of the proposed algorithm is 2.02% higher on average.

Key words: traffic management engineering, vehicle speed, self-supervised learning, bidirectional long short-term memory network, single global navigation satellite system signal

CLC Number: 

  • U149.41

Fig.1

Overall structure of self-supervised bidirectional LSTM network for vehicle travel speed estimation"

Fig.2

Sparse data spatial-temporal feature extraction LSTM network"

Fig.3

Noise bidirectional suppression LSTM network for vehicle travel speed estimation"

Fig.4

Estimation error of SSB-LSTM for vehicle speed with different sampling time intervals"

Table 1

Error indicators for vehicle speed estimation of SSB-LSTM under different noise interferences"

噪声水平/（km2·h-2）高斯噪声泊松噪声尖峰噪声
PSNR/dBSSIMPSNR/dBSSIMPSNR/dBSSIM
520.980.5822.090.3118.130.41
1023.200.2422.650.2820.300.47
1520.440.4122.670.2818.370.37
2021.660.3623.280.2517.280.37

Fig.5

Box plot of absolute error between the noise removed by the algorithm in speed estimation and the actual noise"

Fig.6

Comparison of estimation errors of different algorithms on real vehicle speed dataset"

Fig.7

Comparison of speed estimation errors of different algorithms on synthetic Gaussian noise data"

Fig.8

Comparison of speed estimation errors of different algorithms on synthetic Poisson noise data"

Fig.9

Comparison of speed estimation errors of different algorithms on synthetic Peak noise data"

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