Open Access

ARTICLE

Enhancing Safety in Autonomous Vehicle Navigation: An Optimized Path Planning Approach Leveraging Model Predictive Control

Shih-Lin Lin^*, Bo-Chen Lin

Graduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua, 50007, Taiwan

* Corresponding Author: Shih-Lin Lin. Email:

Computers, Materials & Continua 2024, 80(3), 3555-3572. https://doi.org/10.32604/cmc.2024.055456

Received 27 June 2024; Accepted 07 August 2024; Issue published 12 September 2024

Abstract

This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories, ensuring safe and efficient navigation. Simulated onboard sensors, including vehicle cameras and millimeter-wave radar, are used to detect and respond to dynamic changes in the surrounding environment, enabling real-time decision-making for LCM and ACC. The simulation results highlight the superiority of the MPC-based approach in maintaining safe distances, executing controlled lane changes, and optimizing fuel efficiency. Specifically, the MPC controller effectively manages collision avoidance, reduces travel time, and contributes to smoother traffic flow compared to traditional path planning methods. These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios. Future research will focus on validating these results through real-world testing, addressing computational challenges for real-time implementation, and exploring the adaptability of MPC under various environmental conditions. This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation, paving the way for broader adoption of MPC in AV systems.

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Keywords

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