4.7 Article

Make space to change lane: A cooperative adaptive cruise control lane change controller

Journal

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2022.103847

Keywords

CACC; Platoon Lane Change; Motion Planning; String Stability; Backward-Looking

Funding

  1. Shanghai Municipal Science and Technology Major Project [2021SHZDZX0100]
  2. Shanghai Oriental Scholar
  3. Tongji Zhongte Chair Professor Foundation [000000375-2018082]
  4. Fundamental Research Funds for the Central Universities

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The research proposes a Cooperative Adaptive Cruise Control Lane Change (CACCLC) controller, which shows enhanced lane-change capability, improved success rate, and string stability in dense traffic. Compared to conventional methods, the controller demonstrates significant improvements in both competence and efficiency on arterials and freeways.
This research proposes a Cooperative Adaptive Cruise Control Lane Change (CACCLC) controller. It is designed for maneuvering a CACC platoon to change lane in dense traffic. The proposed controller has the following features: i) with enhanced change-lane-in-dense-traffic capability; ii) with an improved success rate of lane-change; iii) with string stability. A Backward-Looking (BL) CACC information topology is adopted for better serving the objective of making space to change lane. The proposed controller is evaluated on a simulation platform with the context of traffic and a joint simulation platform consisting of PreScan and Matlab/Simulink. The proposed controller is evaluated against the conventional simultaneous CACCLC controller. Sensitivity analysis has been conducted in terms of congestion level and road type. Results demonstrate that, compared to the conventional simultaneous CACCLC method, the proposed controller does enhance CACCLC capability no matter the lane-change competence and efficiency. The magnitude of enhancement on competence is 29.13 % on arterials and 43.14 % on freeways on average. The magnitude of enhancement on efficiency is 88.65 % on arterials and 92.30 % on freeways on average. The computation time of the proposed CACCLC controller is approximately 15 ms when running on a laptop equipped with an Intel i7-8750H CPU. This indicates that the proposed controller has the potential for real-time implementation.

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