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Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging

The rapid advancements in autonomous vehicle (AV) technology promise enhanced safety and operational efficiency. However, frequent lane changes and merging maneuvers continue to pose significant safety risks and disrupt traffic flow. This paper introduces the Minimizing Lane Change Algorithm (MLCA), a state-machine-based approach designed to reduce unnecessary lane changes, thereby enhancing both traffic safety and efficiency. The MLCA algorithm prioritizes maintaining lane stability unless safety-critical conditions necessitate a lane change. The algorithm's effectiveness was evaluated through simulations conducted on the SUMO platform, comparing its performance against established models, including LC2017 and MOBIL. Results demonstrate substantial reductions in lane changes and collisions, leading to smoother traffic flow and improved safety metrics. Additionally, the study highlights the MLCA's adaptability to various traffic densities and roadway configurations, showcasing its potential for wide-scale deployment in real-world AV systems. Future work aims to validate these findings in more complex scenarios using the CARLA simulator, which will enable the testing of the algorithm under more dynamic and high-fidelity conditions, such as urban traffic environments with diverse road users. Moreover, the integration of cybersecurity measures for vehicle-to-vehicle (V2V) communication will be explored to ensure robust and secure data exchange, further enhancing the reliability and safety of AV operations. This research contributes to the broader goal of developing intelligent traffic systems that optimize both individual vehicle performance and overall traffic network efficiency.

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