Vehicle Trajectory Prediction with Gaussian Process Regression in Connected Vehicle Environment$\star$

Goli, Sepideh Afkhami; Far, Behrouz H.; Fapojuwo, Abraham O.

doi:10.1109/ivs.2018.8500614

Cited by 54 publications

(17 citation statements)

References 16 publications

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“…ML methods can predict vehicle trajectory with a large amount of traffic data through the developed data acquisition technologies such as GPS and roadside cameras. Some ML approaches have already been applied for trajectory prediction, including hidden Markov models [9][10][11], Gaussian process regression models [12,13], Bayesian networks [14], Support Vector Machine [15][16][17], and Long Short-Term Memory [18][19][20][21][22].…”

Section: Related Researchmentioning

confidence: 99%

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Liu

Fan

2021

PROMET

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Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.

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Section: Related Researchmentioning

confidence: 99%

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Liu

Fan

2021

PROMET

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“…For example, if a vehicle knows the projected trajectories of its surrounding vehicles, then it can easily make timely decisions to warn or avoid a potential accident. A research work [40] based on Gaussian process regression model has focused on the long term (particularly more than one second) trajectory prediction of the nearby vehicles, in a dynamically changing and uncertain V2X scenario. The most important and critical systems of a vehicle such as, Anti-lock Braking System (ABS), Electronic Stability Program (ESP), and Traction Control System (TCS), rely on the low-level data from various vehicular sensors.…”

Section: Ai In V2x Applicationsmentioning

confidence: 99%

Artificial Intelligence for Vehicle-to-Everything: A Survey

et al. 2019

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Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.

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“…Efficient sensor development relying on ultrasonic technology [27], Radio Detection and Ranging (RADARs) [28], Light Detection and Ranging (LIDARs) [29]- [30] and complex image processing algorithms [4] as well as control systems and algorithms [31]- [32], have been developed. This enables the vehicles within the near field IAV, to announce and plan their trajectory in coordination with other cars [33]- [34] and coordinate their collective motion [28], [35]- [36] with other cars while traveling the form of platoons [37]- [38] with efficient self-driving algorithms; such as Robust Cruise Control [39]- [40].…”

Section: B Control Strategies and Sensor Development For The Iavsmentioning

confidence: 99%

“…This technique makes use of clustering for extracting better models from the available data. Hence the more accurate location of a vehicle can be identified to avoid accidents [33]. Another method to optimize the IAV control can be to merge IAV at the roundabouts using predictive control to reduce traffic congestions.…”

Section: B Autonomous Vehicles -Selfish Packets On the Iav Networkmentioning

confidence: 99%

Why is Internet of Autonomous Vehicles not as Plug and Play as We Think ? Lessons to Be Learnt From Present Internet and Future Directions

et al. 2020

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The recent race for autonomous or 'driverless' vehicles, has spawned a lot of research in the area of Internet of Autonomous Vehicles (IAVs). With the advent of the latest technology fueled by Artificial Intelligence and Machine Learning, Autonomous Vehicles (AVs) can now determine the best possible route to a destination based on the current traffic situation and take dynamic driving decisions accordingly, while preventing accidents. Field trials for single autonomous vehicles have been largely successful. However, as more autonomous vehicles will be added to the intelligent transport networks, current research is now centered around their synergistic coexistence in the offering of network-centric and user-centric services. This development is governed by borrowing several concepts from the legacy Internet to address the problems of IAVs. In this paper, we present an extensive overview of the research challenges in the IAVs. Moreover, our contributions in this paper are that (i) We show how the network-oriented cooperative client-server model will give way to a more unorthodox and 'selfish' decentralized and peer-to-peer (P2P) model, for example in the offering of navigation services on the IAV. (ii) We discuss how centralized architecture will give way to more distributed architectures for real-time information propagation over the IAV. (iii) We discuss how network-centric policies will begin to shift to user-centric under more beneficial revenue models by offering network-assisted quality of service (QoS) provisioning. (iv) We discuss in detail how vehicle traffic grooming in the IAV would present as much of a challenge as in the legacy Internet. (v) We discuss the disruptive role of value-added services on the IAV, and (iv) Finally, we discuss the problem of cyber threats in the IAV just as in the legacy Internet.

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Vehicle Trajectory Prediction with Gaussian Process Regression in Connected Vehicle Environment$\star$

Cited by 54 publications

References 16 publications

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Artificial Intelligence for Vehicle-to-Everything: A Survey

Why is Internet of Autonomous Vehicles not as Plug and Play as We Think ? Lessons to Be Learnt From Present Internet and Future Directions

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