In areas where fencing or crossing structures are not present or feasible, roadside animal detection systems (RADS) can alert drivers and sometimes the animals to the presence of the other and reduce the risk of collision. There is an increasing array of technologies that detect animals on the roadside, classify animal and human road user types, map movement trajectories, and warn drivers with emerging technologies, especially AI and machine learning.
However, our engineers-interview study also found that many research papers, reports, and articles that present the latest developments in sensing technologies, big data, and Artificial Intelligence (AI) superpowers still need to provide expected capabilities to traffic agencies or other community partners. Traffic safety engineering requires research and innovation to provide practical solutions, tools, and clear road maps to implement new technologies.
The author’s research program has been reviewing, developing, implementing, and field-deploying AI technologies for roadside LiDAR sensing and other advanced sensing technologies for multiple years. This presentation will introduce the major AI technology types, their difference, applications, pros & cons, and experience of implementing the different AI technologies in practical roadside sensing systems.