Rural AV Deployment

We are advancing the frontier of autonomous vehicle (AV) technology by addressing the unique challenges of AV deployment in rural environments, where sparse infrastructure, unpredictable hazards, limited connectivity, and adverse weather conditions pose substantial operational barriers. Our research tackles these issues through a multifaceted approach combining advanced Vision-Language Models (VLMs) for semantic perception, reinforcement learning (RL) for adaptive control, and infrastructure-aware localization techniques. Specifically, we have developed a robust hazard detection framework—INSIGHT—that integrates semantic and visual inputs to detect and interpret rural-specific challenges such as wildlife crossings, agricultural machinery, poorly marked curves, and narrow bridges. To enhance CAV decision-making, we also implemented deep reinforcement learning algorithms (DDPG and TD3) trained on high-fidelity simulations in CARLA, enabling safe longitudinal control and emergency response in edge-case scenarios, such as multi-vehicle pileups and sudden braking events. These models are supported by advanced sensor calibration pipelines, adaptive sensor fusion for adverse visibility, and edge computing strategies that ensure autonomy in low-connectivity settings. Our unified control framework integrates adaptive cruise control and emergency braking, significantly outperforming conventional ADAS systems in rural testbeds.