Alina Hagen

Dongdong Wang, Alina Hagen, Isabelle Gatmaitan et al.

Assessing built-environment interaction, such as wheelchair accessibility, is difficult because real-world mobility is shaped by distributed, context-dependent, and temporary barriers that are hard to capture at scale. To support scalable assessment, this paper examines whether vision-language models (VLMs) can identify accessibility barriers from Google Street View (GSV) imagery. We propose an expert-guided retrieval-augmented framework that combines GSV images, ADA-informed guidance, and expert-derived rubrics to evaluate accessibility dimensions. We collect a campus-scale dataset at the University of Florida, linking 407 unique GSV locations with GPS-derived wheelchair dwell behavior as a mobility-friction signal. Results show that VLM ratings are both negatively correlated and distributionally similar with dwell time, indicating partial but consistent alignment with a behavioral proxy for mobility friction. Visual cue analysis shows that certain environmental objects, such as curb ramps and crosswalks, are associated with higher VLM accessibility scores, while alignment remains limited for subtle surface conditions, transient obstructions, and viewpoint-dependent barriers. Overall, our findings show the potential of expert-guided VLMs for scalable accessibility assessment aligning with sensor-derived indicators of real-world wheelchair navigation.

Hao Zhou, Shengming Yuan, Yuhang Wang et al.

This study presents Scale-CDA, an open-hardware/open-software tool-chain that democratizes a functional version of Generative-AI-assisted Cooperative Driving Automation (CDA). Built on the community-maintained OpenDBC interface (300+ car models) and Openpilot Level-2 ADAS, Scale-CDA achieves plug-and-play retrofitting with off-the-shelf parts that cost under US \$1,000 (edge PC, webcam, CAN adapter, optional LTE/Wi-Fi radios). A lightweight Vehicle-to-Everything (V2X) stack using MQTT over Wi-Fi 6/LTE provides bidirectional connectivity. Field experiments in a 7.5 km test loop demonstrated mean round-trip latency of 5.25 ms and link speeds near 100 Mb/s, validating Wi-Fi 6 as a viable, low-cost medium for non-safety-critical CDA messaging . At the intelligence layer, an edge-deployed multimodal large-language model (MLLM) ingests synchronized vision, CAN, and V2X streams via a Model-Context-Protocol (MCP) bridge, then issues structured JSON advisories and motion primitives. A library of meta-action executors translates these high-level commands into verified Openpilot planner hooks, enabling lane changes, gap management, and emergency stops without altering the safety-certified core. In multi-vehicle road tests the full stack maintained end-to-end decision latencies below 60 ms, while preserving data privacy by keeping inference on-board. Collectively, Scale-CDA closes two critical gaps that have limited CDA R\&D: (i) affordable, interoperable hardware for large-scale field trials, and (ii) a standardized interface that lets GenAI reason and act on everyday cars. By releasing the bills-of-materials, connectivity APIs, and GenAI bridges as open resources, this work offers transportation agencies and researchers a practical blueprint for democratizing cooperative autonomy, accelerating deployments that enhance traffic efficiency and safety.