Autonomous Vehicles Accident Prevention for Highway On-Ramp Merging in Mixed Traffic
Keywords:
Autonomous vehicles, Accident prevention, Highway merging, Mixed traffic, On-Ramp merging, Reinforcement learning, Sensor fusionAbstract
Highway on-ramp merging is a significant challenge for autonomous vehicles (AVs) in mixed traffic, requiring seamless coordination with human-driven vehicles (HDVs) to prevent accidents and ensure efficient traffic flow. This paper presents an integrated approach to accident prevention using a combination of sensor fusion, reinforcement learning, and cooperative control strategies. Our methodology employs LiDAR, cameras, and radar to enhance situational awareness and predict vehicle behaviors, reducing uncertainty in dynamic traffic environments. A reinforcement learning-based decision-making framework, supported by game-theoretic models, optimizes AV merging strategies by prioritizing safety, efficiency, and comfort. Additionally, Model Predictive Control (MPC) ensures smooth trajectory planning and adaptive maneuvering, enabling AVs to respond effectively to varying traffic conditions. Simulation results using CARLA and SUMO demonstrate that our approach significantly reduces collision rates by 40% and improves merging success rates by 25% compared to traditional methods. The adaptability of our framework to diverse traffic scenarios highlights its potential for real-world implementation, enhancing overall road safety and traffic efficiency. Future research will focus on refining human behavior models and validating performance through large-scale real-world testing, further advancing AV capabilities in complex highway merging scenarios.
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