AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Software
Shengming Yuan, Hao Zhou
TL;DR
AI-CDA4ALL addresses the accessibility and interoperability gaps in cooperative driving automation by leveraging affordable dashcam-based edge devices and open-source software. The approach fuses an OpenPilot-derived driving stack, LTE/WiFi-based V2X communications, and edge-local GenAI via a MetaAction API to convert reasoning into executable vehicle commands, all within a modular, standards-aligned architecture. Key contributions include a BOM under $1000, SAE J2735-compliant V2X messaging over lightweight networks, and edge-local GenAI capabilities with mean $T_{net}=5.07$ s and median $T_{net}=4.61$ s for LLaMA 3.2 on the AGX Orin, preserving privacy by avoiding cloud dependence. The work enables retrofitting of legacy vehicles and provides infrastructure-friendly tools for agencies to pilot cooperative driving features, potentially accelerating safe, inclusive deployment of smart transportation systems $T_{net}$.
Abstract
As transportation technology advances, the demand for connected vehicle infrastructure has greatly increased to improve their efficiency and safety. One area of advancement, Cooperative Driving Automation (CDA) still relies on expensive autonomy sensors or connectivity units and are not interoperable across existing market car makes/models, limiting its scalability on public roads. To fill these gaps, this paper presents a novel approach to democratizing CDA technology, it leverages low-cost, commercially available edge devices such as vehicle dash-cams and open-source software to make the technology accessible and scalable to be used in transportation infrastructure and broader public domains. This study also investigates the feasibility of utilizing cost-effective communication protocols based on LTE and WiFi. These technologies enable lightweight Vehicle-to-Everything (V2X) communications, facilitating real-time data exchange between vehicles and infrastructure. Our research and development efforts are aligned with industrial standards to ensure compatibility and future integration into existing transportation ecosystems. By prioritizing infrastructure-oriented applications, such as improved traffic flow management, this approach seeks to deliver tangible societal benefits without directly competing with vehicle OEMs. As recent advancement of Generative AI (GenAI), there is no standardized integration of GenAI technologies into open-source CDAs, as the current trends of muiltimodal large language models gain popularity, we demonstrated a feasible locally deployed edge LLM models can enhance driving experience while preserving privacy and security compared to cloud-connected solutions. The proposed system underscores the potential of low-cost, scalable solutions in advancing CDA functionality, paving the way for smarter, safer, and more inclusive transportation networks.