Sustainable LA Grand Challenge

The Challenge

In 2018, the transportation sector represented over 28%, the largest share, of the total U.S. greenhouse gas (GHG) emissions. Automated vehicles, a rapidly evolving technology, have been modeled to reduce GHG emissions up to 94% and bring accident prevention, smoother traffic and better service to people facing driving difficulties other projected benefits of automated vehicles include reduced infrastructure needs but serving the same demand and potentially more efficient public transportation systems that eliminate transit stops. However, as of 2016 there had not been a field experiment study conducted locally to test and verify the impacts of automated vehicles within Los Angeles County. In this project, researchers ran an automated vehicle prototype on various routes in the county to document the effects on GHG emissions and sustainable transportation.

The Solution

The study put an existing automated vehicle prototype in the real-world environment, which had not been readily tried out due to the high cost of the Light Detection and Ranging (LiDAR) laser sensing system the technology requires. Thus, a low-cost, high-performance LiDAR sensor chip advanced at UCLA was instead embedded in the prototype, collecting various types of data such as emissions, energy efficiency statistics and human mobility patterns. Researchers then analyzed the collected first-hand data to 1) build scenarios of sustainable transportation evolution by 2030 in Los Angeles County and 2) assess the regional impacts on GHG emissions and road traffic congestion. Performing these tasks will help understand if there are any changes required in today's transportation system (e.g. finance, planning and management) to enable the successful adoption of automated vehicle technology across Los Angeles County.

Next Steps

Researchers are collaborating with Southern California Association of Governments (SCAG), the metropolitan planning organization for Los Angeles County, for future research that involves real-time traffic and road mapping data generated from the LiDAR sensors.