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A novel self adaptive-electric fish optimization-based multi-lane changing and merging control strategy on connected and autonomous vehicle

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Abstract

Merging areas on freeways are key locations due to vehicles’ fixed lateral differences. Though, such challenges are considerably unnecessary with connected and autonomous vehicle (CAV) technology. In recent times, the existing studies focusing on CAV methodology intend to merge the maneuvers among an incoming ramp and a single-lane mainline. This paper develops the lane optimization model for CAV system for solving the complexities of multilane merging areas. The two major stages of the proposed model are lane changing and lane merging control optimization. For performing the lane changing control optimization, a self adaptive-electric fish optimization (SA-EFO)-based “cooperative lane changing control (CLCC)” is developed. The significant aim of the SA-EFO-based CLCC is to exploit the average velocity concerning the entire vehicles. Once the lane changing control strategies is done, lane merging control is performed through “cooperative merging control” optimization using the same proposed SA-EFO with the intention of maximizing the vehicle's average velocity. Finally, the simulation of the designed model reveals that the developed model is superior to existing merging algorithms over the existing models under different demand scenarios.

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Authors and Affiliations

  1. Department of Electronics and Engineering, Saveetha Engineering College, Chennai, India

    T. Vaishnavi

  2. Department of Electronics and Communication, Saveetha Teaching Learning Centre, Saveetha Engineering College, Chennai, India

    C. Sheeba Joice

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  1. T. Vaishnavi
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  2. C. Sheeba Joice
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Correspondence to T. Vaishnavi.

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Vaishnavi, T., Sheeba Joice, C. A novel self adaptive-electric fish optimization-based multi-lane changing and merging control strategy on connected and autonomous vehicle. Wireless Netw 28, 3077–3099 (2022). https://doi.org/10.1007/s11276-022-03022-9

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