collaborators

13 papers

cs.ET2026

Scale-CDA: A Scalable Retrofit Platform for Cooperative Driving Automation in Production Vehicles

Hao Zhou, Shengming Yuan, Yuhang Wang +2

Scaling cooperative driving automation (CDA) to production passenger vehicles requires an affordable retrofit platform that can accommodate heterogeneous OEM Controller Area Networ…

cs.LG2026

DriveDNA: A Large-Scale Multimodal Naturalistic Driving Dataset and Benchmark for Driving Style Identification

Yuhang Wang, Lingyao Li, Hao Zhou

Driving style captures stable, driver-specific patterns in how a vehicle is driven. In naturalistic data, however, this signal is hard to isolate because drivers are observed in di…

cs.LG2026

A Closed-loop, State-centric, Multi-agent Framework for Passenger Load Estimation from Heterogeneous Data Streams

Yiyao Xu, Hao Zhou, Yuhang Wang +1

To support operations and passenger-facing services, transit agencies need reliable passenger load trajectories. Currently, load estimates are typically inferred from imperfect sen…

cs.RO2026

Cut-In Gap Acceptance Toward Autonomous vs. Human-Driven Vehicles: Evidence from the Waymo Open Motion Dataset

Abdulaziz Alhuraish, Yuhang Wang, Hao Zhou

Autonomous vehicles (AVs) are widely known to follow conservative, rule-based motion policies that surrounding drivers can learn to anticipate. A direct consequence is that human d…

cs.HC2026

BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving

Yuhang Wang, Yiyao Xu, Chaoyun Yang +3

Existing driving automation (DA) systems on production vehicles rely on human drivers to decide when to engage DA while requiring them to remain continuously attentive and ready to…

cs.HC2026

ADAS-TO: A Large-Scale Multimodal Naturalistic Dataset and Empirical Characterization of Human Takeovers during ADAS Engagement

Yuhang Wang, Yiyao Xu, Jingran Sun +1

Takeovers remain a key safety vulnerability in production ADAS, yet existing public resources rarely provide takeover-centered, real-world data. We present ADAS-TO, the first large…