Research
AI for mobility
Computer vision and machine learning for traffic monitoring, road hazard detection, and operations support.
Modern vision and learning systems extend the reach of traditional traffic monitoring. We develop and evaluate AI tools that turn dash-cam video, roadside cameras, and probe data into operational signals — vehicle counts, incident alerts, pavement and roadside hazard detections — at a fraction of the cost of fixed instrumentation.
Our emphasis is not on novelty in the model architecture alone but on closing the loop with traffic operations: what false-positive rate is tolerable for an alert that triggers a maintenance dispatch? How do edge-device and bandwidth constraints shape what is actually deployable? How do we evaluate detection performance against the messy ground truth available from agencies?
Featured prototype: Scale-CDA
Scale-CDA is an open-hardware / open-software toolchain that retrofits production cars for generative-AI-assisted cooperative driving automation using under US $1,000 of off-the-shelf parts. An edge-deployed multimodal LLM reads synchronized vision, CAN, and V2X streams and issues structured advisories to verified Openpilot planner hooks — end-to-end decision latency stayed below 60 ms in multi-vehicle road tests, with all inference kept on-board.
Agency platform: DoTPilot
DoTPilot is the lab’s open-source in-vehicle platform for state and county transportation agency fleets. It runs on vehicles an agency already owns and makes each one a two-way link with the agency: an on-board AI dashcam reports roadway conditions as structured findings, while agency feeds — travel advisories, work zone data, incident reports — are delivered back to the driver as in-vehicle warnings. The post-storm damage inspection pipeline, work zone safety warnings, and traffic incident management all run on it.
Selected papers
- Zhou, Yuan, Wang, Wen. Scale-CDA: a scalable prototype to democratize AI-assisted cooperative driving automation (CDA) for production cars. arXiv preprint.
- Zhou, Laval, A. Zhou, Wang, Wu, Qing, Peeta. Review of learning-based longitudinal motion planning for autonomous vehicles: implications on traffic congestion. Transportation Research Record.
- Chen, Tang, Zhou, Cheng. Extracting topographic data from online sources to generate a digital elevation model for highway preliminary geometric design. Journal of Transportation Engineering, Part A.
- Laval, Zhou. Congested urban networks tend to be insensitive to signal settings: implications for learning-based control. IEEE Transactions on Intelligent Transportation Systems.
See the full publications page for the rest.