Transportation remains one of the major sources of global air pollution and climate change, accounting for approximately 59% of oil consumption and 22% of carbon dioxide emissions.
Therefore, finding effective strategies to reduce vehicle fuel consumption will not only help reduce pollution, but also alleviate global energy shortages.
According to foreign media reports, researchers at The Hong Kong University of Science and Technology have recently begun using computational models based on reinforcement learning to address this challenge.
The model, outlined by the researchers in a paper published on the arXiv preprint server, is designed to optimize fuel consumption in car-following scenarios, especially when semi-autonomous and autonomous vehicles are driving close and need to adjust vehicle speed to maintain a safe distance.
, Photo source: arXiv, return to the first electric network home page>,.
