A vehicle is no longer a fixed object in a flow model — it is a moving system whose perception, control, and powertrain shape its behavior. We characterize ADAS-equipped and electric vehicles empirically and analytically, then quantify what their adoption implies for capacity, stability, and safety on existing roadways.

Specific work includes: car-following calibration for production ACC and LKA systems; the energy-efficiency / throughput trade-off introduced by EV speed-power profiles; and how partial market penetration of automated features changes the distribution of headways and the probability of rear-end conflicts. We collaborate with vehicle dynamics researchers and rely on a mix of test-track data, naturalistic driving data, and microsimulation.

OpenLKA — released January 2025 — is the first open dataset of lane-keeping-assist (LKA) behavior collected from market autonomous vehicles. It pairs CAN-bus signals with synchronized video and lane-perception data across multiple production vehicles, supporting research on LKA performance variability, mixed-autonomy traffic, and disengagement modeling.

Selected papers

See the full publications page for the rest.