| Abstract |
As the era of 6G networks approaches, the importance of Intelligent Transportation Systems is increasingly prominent, representing the frontier of technological advancement and serving as a core component of smart city development. The integration of autonomous driving technologies with advanced techniques such as deep learning is rapidly progressing, encompassing key areas such as environmental perception, localization and map construction, path planning and decision-making, as well as motion control. However, challenges remain in ensuring both the efficiency and safety of the model learning process. To address these issues, this paper proposes the Generative AI-Enhanced Autonomous Driving (GAIHAD) framework, which leverages generative AI techniques to enhance decision-making and execution capabilities in multi-interactive environments. The GAIHAD framework consists of two primary modules: the Interactive Enhanced Intelligent Decision-Making and Execution Module, and the Noise-Enhanced Risk Assessment Module. The former employs a Proximal Policy Optimization based interactive reinforcement learning approach to mimic human driving behaviors and incorporates a bidirectional Model Predictive Control system to handle multi-constraint motion planning. The latter introduces a noise-augmented dual-stream Generative Adversarial Network to capture the randomness inherent in driving patterns and predict potential collision trajectories. Our experiments, conducted across various scenarios, demonstrate the superior performance of the GAIHAD framework in trajectory prediction accuracy. For example, under nighttime conditions, the ADE values of GAIHAD are 0.03, 0.11, and 0.20 meters for prediction horizons of 3, 5, and 10 seconds, respectively, outperforming CSM and IMMTP by a large margin. © 2000-2011 IEEE. |
