Trajectory Tracking-Based Lane Changing in Connected Vehicles With Unreliable Communication

We study lane changing in connected vehicles, in the presence of surrounding vehicles with non-uniform velocities, under unreliable communication. We adopt a dynamic trajectory tracking approach, wherein a subject vehicle (SV), modeled by the nonlinear dynamic bicycle model, must follow the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">desired dynamics</i> governed by the dynamic lane-change trajectory planning (DLTP) model, for changing lanes efficiently. We design a robust and adaptive Coupled Multiple Sliding Mode Control (CMSMC) law that ensures trajectory tracking despite time-varying disturbances acting on the SV. The controller accepts position and velocity inputs from the neighboring vehicles, through vehicle-to-vehicle (V2V) communication. The design is initially validated for an ideal communication channel that does not drop any data packets. Subsequently, we derive a sufficient condition that ensures the boundedness of the tracking error even when the communication channel exhibits packet drops. Using an appropriate model for packet drops, we derive an upper bound on the packet-transmission interval for each vehicle communicating with the SV, and also outline the associated condition for update of the controller. The DLTP model is validated using real-world field traffic data extracted from NGSIM; and the analytical results are substantiated through numerical computations, and simulations performed on SUMO. Our study contributes towards the co-design of controller and communication algorithms for connected vehicles under unreliable communication.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2025-02-03
DOI
https://doi.org/10.1109/tvt.2025.3531990
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
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article

Trajectory Tracking-Based Lane Changing in Connected Vehicles With Unreliable Communication

Uddipan Barooah, Sreelakshmi Manjunath
IEEE Transactions on Vehicular Technology
Traffic control and management
article

Trajectory Tracking-Based Lane Changing in Connected Vehicles With Unreliable Communication

Uddipan Barooah, Sreelakshmi Manjunath
article en

Abstract

We study lane changing in connected vehicles, in the presence of surrounding vehicles with non-uniform velocities, under unreliable communication. We adopt a dynamic trajectory tracking approach, wherein a subject vehicle (SV), modeled by the nonlinear dynamic bicycle model, must follow the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">desired dynamics</i> governed by the dynamic lane-change trajectory planning (DLTP) model, for changing lanes efficiently. We design a robust and adaptive Coupled Multiple Sliding Mode Control (CMSMC) law that ensures trajectory tracking despite time-varying disturbances acting on the SV. The controller accepts position and velocity inputs from the neighboring vehicles, through vehicle-to-vehicle (V2V) communication. The design is initially validated for an ideal communication channel that does not drop any data packets. Subsequently, we derive a sufficient condition that ensures the boundedness of the tracking error even when the communication channel exhibits packet drops. Using an appropriate model for packet drops, we derive an upper bound on the packet-transmission interval for each vehicle communicating with the SV, and also outline the associated condition for update of the controller. The DLTP model is validated using real-world field traffic data extracted from NGSIM; and the analytical results are substantiated through numerical computations, and simulations performed on SUMO. Our study contributes towards the co-design of controller and communication algorithms for connected vehicles under unreliable communication.

IEEE Transactions on Vehicular TechnologyVol. 74(6)
Indian Institute of Technology Mandi (IN)
Affordable and clean energy
Openalex Percentile: Top 98%
Traffic control and management
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