Identification of winter road friction coefficient based on multi-task distillation attention network

Liu, Feilin; Wu, Yan; Yang, Xinneng; Mo, Yujian; Liao, Yun

doi:10.1007/s10044-022-01059-2

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…Furthermore, predicting road conditions, especially in winter, is an essential factor for car safety. Likewise, Liu et al showed that MTL could help improve the detection of the road [63]. They train together the road segments as auxiliary tasks to concentrate on the road objects and friction estimation coefficients as the main task.…”

Section: ) Road Conditions Predictionmentioning

confidence: 99%

Survey on Multi-Task Learning in Smart Transportation

Alzahrani,

Wang,

Liao

et al. 2024

IEEE Access

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Artificial Intelligence (AI) has been widely adopted in numerous fields and enabled various smart systems because of its strong ability to perform tasks, including prediction, event detection, and status estimation, among others. As one of the typical smart systems empowered by AI and Internet of Things (IoT) technologies, the smart transportation system has made dramatic progress for traditional transportation in numerous aspects, including autonomous driving, smart traffic lights, navigation, and traffic forecasting, among others. Deep learning is an essential component to enable such smart systems. Typically, specific deep learning models, e.g., Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), can be trained on collected transportation data for a particular task. However, traditional deep learning techniques rely on data sufficiency to build an effective model. Additionally, each trained model can only work on one single task. This has limited the efficacy of deep learning techniques in numerous application scenarios. To this end, multi-task learning (MTL) has been studied to train a single model that can work for multiple tasks. This technique effectively allows the learning model to expand to specific tasks with only a limited amount of data affiliated. In the meantime, MTL significantly reduces the training time of each task. The success of MTL requires that there are potential relationships among different tasks. Many tasks in the smart transportation system are related. For instance, traffic speed and vehicle volume estimations for each road are highly correlated. Based on this, research on applying MTL in smart transportation systems has been studied recently. This paper reviews the recent efforts to use MTL in smart transportation systems and conducts an extensive survey to provide insights. In particular, we categorize the MTL applications in smart transportation systems into traffic forecasting, traffic sign recognition, vehicle recognition, travel time estimation, road safety estimation, taxi demand prediction, and autonomous driving. Ultimately, we discuss challenges and future research directions in applying MTL in smart transportation systems.INDEX TERMS Multi-task learning (MTL), smart transportation, deep learning.

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Section: ) Road Conditions Predictionmentioning

confidence: 99%

Survey on Multi-Task Learning in Smart Transportation

Alzahrani,

Wang,

Liao

et al. 2024

IEEE Access

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“…Since the TRFC is difficult to be directly measured by sensors, many scholars have conducted in-depth research on its estimation. The existing research studies are mainly divided into experimental methods [6,7] and model methods [8,9]. The experimental methods use expensive sensors such as radar and camera to identify the pavement state and further estimate the pavement adhesion coefficient on this basis.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Mathematical Models for Precise Estimation of Tire–Road Friction Coefficient of Distributed Drive Electric Vehicles Based on Sensorless Control of the Permanent Magnet Synchronous Motor

Yu,

Hu,

Zeng

2024

Symmetry

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In order to reduce the use of wheel angular velocity sensors and improve the estimation accuracy and robustness of the tire–road friction coefficient (TRFC) in non-Gaussian noise environments, this paper proposes a sensorless control-based distributed drive electric vehicle TRFC estimation algorithm using a permanent magnet synchronous motor (PMSM). The algorithm replaces the wheel angular velocity signal with the rotor speed signal obtained from the sensorless control of the PMSM. Firstly, a seven-degree-of-freedom vehicle dynamics model and a mathematical model of the PMSM are established, and the maximum correntropy singular value decomposition generalized high-degree cubature Kalman filter algorithm (MCSVDGHCKF) is derived. Secondly, a sensorless control system of a PMSM based on the MCSVDGHCKF algorithm is established to estimate the rotor speed and position of the PMSM, and its effectiveness is verified. Finally, the feasibility of the algorithm for TRFC estimation in non-Gaussian noise is demonstrated through simulation experiments, the Root Mean Square Error (RMSE) of TRFC estimates for the right front wheel and the left rear wheel were reduced by at least 41.36% and 40.63%, respectively. The results show that the MCSVDGHCKF has a higher accuracy and stronger robustness compared to the maximum correntropy high-degree cubature Kalman filter (MCHCKF), singular value decomposition generalized high-degree cubature Kalman filter (SVDGHCKF), and high-degree cubature Kalman filter (HCKF).

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