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Journal Articles

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Lichuan Ren, Zhimin Xi

Publisher: ASME

J. Auton. Veh. Sys. April 2025, 5(2): 021001.

Paper No: JAVS-24-1011

https://doi.org/10.1115/1.4067004

Published Online: November 19, 2024

View Article titled, Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Topics: Particle filtering (numerical methods), Trajectories (Physics), Uncertainty, Vehicles, Simulation

Proceedings Papers

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Lichuan Ren, Zhimin Xi

Proc. ASME. IDETC-CIE2024, Volume 3B: 50th Design Automation Conference (DAC), V03BT03A039, August 25–28, 2024

Publisher: American Society of Mechanical Engineers

Paper No: DETC2024-143361

https://doi.org/10.1115/DETC2024-143361

View Paper titled, Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Topics: Autonomous vehicles, Trajectories (Physics), Uncertainty, Vehicles

Journal Articles

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Lichuan Ren, Zhimin Xi

Publisher: ASME

J. Mech. Des. September 2022, 144(9): 091706.

Paper No: MD-21-1676

https://doi.org/10.1115/1.4054674

Published Online: June 13, 2022

View Article titled, Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Topics: Autonomous vehicles, Control equipment, Design, Dynamic models, Errors, Tires, Uncertainty, Vehicles, Simulation, Roads

Proceedings Papers

Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty

Lichuan Ren, Zhimin Xi

Proc. ASME. IDETC-CIE2021, Volume 3B: 47th Design Automation Conference (DAC), V03BT03A011, August 17–19, 2021

Publisher: American Society of Mechanical Engineers

Paper No: DETC2021-69284

https://doi.org/10.1115/DETC2021-69284

View Paper titled, Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty

Topics: Autonomous vehicles, Control equipment, Design, Dynamic models, Modeling, Uncertainty, Vehicle dynamics, Vehicles

Journal citation

NARROW

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Bias-Learning-Based Model Predictive Controller Design for Reliable Path Tracking of Autonomous Vehicles Under Model and Environmental Uncertainty

Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty

ASME Journals

ASME Conference Proceedings

ASME eBooks

Resources

Opportunities

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