automotive

I led and developed core perception algorithms for a vision-based autonomous ground vehicle as part of Sarnoff’s DARPA Urban Challenge team – Team Autonomous Solutions.

Designed system and algorithms for detecting pedestrians at a high detection rate and low false-positive rate by combining contextual cues from buildings, vehicles and other non-pedestrian classes as part of this project from the Federal Highway Administration (FHWA) Exploratory Advanced Research (EAR) Program (FHWA-HRT-11-056). The research results are included in the FHWA EAR summary.

Developed Pedestrian Tracking from vehicle-mounted IR cameras that has been deployed on the BMW 7-Series. Developed Stereo-based Adaptive Cruise Control from a vehicle-mounted camera that performs real-time detection, tracking and range-estimation of vehicles ahead of the host vehicle. Designed a stereo analysis framework that allows empirical evaluation of any given stereo algorithm. The evaluation results can be used to choose appropriate stereo rig design parameters thus allowing for use of stereo vision sensors in practical automotive environments.