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Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey

Research Article

Optimization Strategy for Car Following and Lane Changing Models of CAV in Mixed Traffic Environments

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  • @INPROCEEDINGS{10.4108/eai.21-11-2024.2354623,
        author={Wanyue  Li and Haowen  Cui and Liming  Chen and Qing  Zhan},
        title={Optimization Strategy for Car Following and Lane Changing Models of CAV in Mixed Traffic Environments},
        proceedings={Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, November 21, 2024, Adana, Turkey},
        publisher={EAI},
        proceedings_a={CONF-MLA},
        year={2025},
        month={3},
        keywords={autonomous vehicle lane-changing safety efficiency},
        doi={10.4108/eai.21-11-2024.2354623}
    }
    
  • Wanyue Li
    Haowen Cui
    Liming Chen
    Qing Zhan
    Year: 2025
    Optimization Strategy for Car Following and Lane Changing Models of CAV in Mixed Traffic Environments
    CONF-MLA
    EAI
    DOI: 10.4108/eai.21-11-2024.2354623
Wanyue Li1,*, Haowen Cui2, Liming Chen3, Qing Zhan4
  • 1: Tongji University, Shanghai, China
  • 2: University of California Santa Barbara, Goleta, United States
  • 3: Hubei University of Economics, Wuhan, China
  • 4: Wuhan University, Wuhan, China
*Contact email: L15662675317@outlook.com

Abstract

A mixed traffic environment is an environment where different types of agents, for instance, Connected Autonomous Vehicles, Human Driven Vehicles, and pedestrians in the same traffic space. In reality, such a mixed traffic environment is the most common for Connected Autonomous Vehicles, so it is practical to study the trade-off between the safety and efficiency of Connected Autonomous Vehicles. The paper proposes an optimization strategy for car-following and lane-changing models of Connected Autonomous Vehicles in mixed-traffic environments. In this study, real-time data (e.g., acceleration, position, signal status, etc.) from CARLA's inbuilt sensors are utilised to dynamically adapt the vehicle's decision-making logic. Compared to existing offline optimisation methods, it can better adapt to the uncertainty in real road environments. In order to check the validity, we use Carla to set up a simulation environment and evaluate the behavior of autonomous vehicles. Furthermore, we collect data through multiple sensors, such as acceleration sensors, to accurately measure vehicle status. Ultimately, we gather the data from the sensors and analyze it by mathematical methods. Through this experiment, we find out that the lane change strategy avoids unnecessary lane changes and shows strong adaptability.

Keywords
autonomous vehicle lane-changing safety efficiency
Published
2025-03-11
Publisher
EAI
http://dx.doi.org/10.4108/eai.21-11-2024.2354623
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