Technical expertise
Crash Warning Systems
I have extensive contractual experience as the principal investigator with vehicle crash-warning systems for run-off-road (ROR) and intersection collisions dealing with automobiles, as well as crash-warning systems for inner-city transit buses. These programs were a precursor development leading to driverless vehicles. The National Highway Traffic Safety Administration (NHTSA) and the Federal Transit Administration (FTA) awarded contracts for these crash-warning programs for which I was the principal investigator:
- Run-Off-Road, awarded by NHTSA, prime contractor Carnegie Mellon, subcontractor Battelle
- Intersection, awarded by NHTSA, prime contractor Calspan, subcontractor Battelle
- Transit Buses, awarded by FTA, prime contractor U. California–Berkeley, subcontractor Foster-Miller
On-Board Vehicle Sensors
Sensors for these vehicles were selected from visual/infrared, acoustic, radar, and light detection and ranging (LIDAR) technologies. Data from these sensors served as input to on-board computers equipped with algorithms designed to issue warnings for crash avoidance. Warning modalities included visual, audio and haptic (that is, vibrating driver seat or steering wheel).
Computer Simulations
The ROR project involved a computer simulation to test various driver-warning algorithms. The simulation included human factor inputs for driver steering, throttling and braking. The simulated automobile was a Ford Taurus that was operated on a roadway designed with various curves and straight segments.
Test Vehicle Design
Reviewed state-of-the-art sensing, processing and driver interface technologies for their applicability to run-off-road collision prevention
Driver Training Simulator for Algorithm Warning Evaluation
My work on inner-city transit buses involved a test of warning algorithms integrated into a driver-training simulator utilized by the New York City Transit Authority. Transit operators were recruited as study participants. My work also included a statistical analysis of transit operator responses to alerts as a function of warning modality and timing with transit operator experience, age, and gender as control parameters.
Weather Effects on Sensor Performance
A serious issue for driverless vehicles is operation during inclement weather. I worked on this problem in relation to sensor performance and weather effects modeling.
My Experience with Autonomous Vehicles
According to the Society of Automotive Engineers (SAE), there are six levels of vehicle automation, starting with 0 (that is, no automation) and ending with 5 (complete system-level automation with no driver involvement). My work described previously spans levels 0–2, and overlaps levels 3–5.
My Future Work
I continue to follow driverless vehicle developments regarding testing, technology, machine learning, policy, weather, and cyber security.