Gaussian Process Auto Regression for vehicle center coordinates Trajectory Prediction

Lim, Qun; Johari, Kritika; Tan, U-Xuan

doi:10.1109/tencon.2019.8929719

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…We used the polyfit to estimate the pedestrian’s actual depth value in order to reduce the noise of the pedestrian’s depth value. There are many other methods available for predicting trajectory, speed estimation, and prediction, especially in the automobile industry, such as in [ 49 ]. In our paper, the gradient of the polyfit was used to estimate the velocity of the pedestrian.…”

Section: Resultsmentioning

confidence: 99%

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Ramalingam

et al. 2021

Sensors

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Regular washing of public pavements is necessary to ensure that the public environment is sanitary for social activities. This is a challenge for autonomous cleaning robots, as they must adapt to the environment with varying pavement widths while avoiding pedestrians. A self-reconfigurable pavement sweeping robot, named Panthera, has the mechanisms to perform reconfiguration in width to enable smooth cleaning operations, and it changes its behavior based on environment dynamics of moving pedestrians and changing pavement widths. Reconfiguration in the robot’s width is possible, due to the scissor mechanism at the core of the robot’s body, which is driven by a lead screw motor. Panthera will perform locomotion and reconfiguration based on perception sensors feedback control proposed while using an Red Green Blue-D (RGB-D) camera. The proposed control scheme involves publishing robot kinematic parameters for reconfiguration during locomotion. Experiments were conducted in outdoor pavements to demonstrate the autonomous reconfiguration during locomotion to avoid pedestrians while complying with varying pavements widths in a real-world scenario.

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Section: Resultsmentioning

confidence: 99%

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Ramalingam

et al. 2021

Sensors

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“…In the real world, almost all data can satisfy the Gaussian distribution [32]. The Gaussian mixture model can calculate the joint probability distribution of the moving object trajectory, which is mainly used to predict coordinates [33,34]. The Gaussian mixture model has high accuracy in short-term prediction, but it is prone to be affected by the complexity of the data, which results in reduced accuracy and low practicability.…”

Section: Probabilistic Statistical Modelmentioning

confidence: 99%

Prediction techniques of spatiotemporal trajectory data: progress and framework

Qin,

Ran,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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With the wide application of intelligent devices, a large amount of spatiotemporal trajectory data can be preserved and recorded. The application of spatiotemporal trajectories has penetrated every aspect of people's daily lives. It is of great applications value to predict the future position of a moving object by mining its motion law. Data are the basis of the method, and thus, the form, source and characteristics of spatiotemporal trajectory data are discussed in this paper. Second, trajectory prediction technology is introduced, and its advantages, disadvantages and applications scope are summarized. Trajectory prediction technology can be divided into trajectory prediction methods based on constructing motion models, pattern mining and deep learning. Finally, the problems and potential research directions of trajectory prediction technology are discussed.

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“…There are many papers that use machine learning and deep neural network algorithms which are used as a reference for this paper's trajectory prediction. They are articles by Lim et al (2019), Goli et al (2018), Heravi and Khanmohammadi (2011), Park et al (2018).…”

Section: Limitationmentioning

confidence: 99%

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

Lim

Muhammad

et al. 2021

JICV

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Purpose The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle). Design/methodology/approach This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor. Findings This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification. Originality/value The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

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Gaussian Process Auto Regression for vehicle center coordinates Trajectory Prediction

Cited by 8 publications

References 13 publications

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Locomotion with Pedestrian Aware from Perception Sensor by Pavement Sweeping Reconfigurable Robot

Prediction techniques of spatiotemporal trajectory data: progress and framework

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

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