Traffic Safety in Mixed Environments by Predicting Lane Merging and Adaptive Control

Abstract

Autonomous driving technology is primarily developed to enhance traffic safety through advancements in motion prediction and adaptive control mechanisms. Highway lane merging remains a high-risk scenario, accounting for approximately 7% of highway collisions globally due to misjudged vehicle interactions, according to international statistics. This paper proposes a two-stage deep learning framework for autonomous lane merging in mixed traffic. Using the Argoverse dataset, which contains over 300,000 vehicle trajectories mapped to high-definition road networks, we first predict vehicle trajectories using a Seq2Seq model with LSTM layers, achieving a 21% improvement in prediction accuracy over a baseline Multi-layer Perceptron model. In the second stage, reinforcement learning is employed for maneuver generation, where a Dueling Deep Q-Network outperforms a standard DQN by 8% in collision avoidance. Experimental results indicate that the combined trajectory prediction and RL-based framework significantly reduces merging delays, enhances data-driven decision-making in mixed traffic environments, and provides a scalable solution for safer autonomous highway merging.