Turns out one of the tougher feature identification problems for autonomous vehicles is detecting bicycles in an urban environment.This may not seem like a major problem here in the U.S., but it certainly is in other countries.
A grad student at Northeastern University is trying to better understand the problem, but he is not getting much help from the private sector. Perhaps one of our readers can provide some data.
Detecting bicycles is particularly challenging for a number of reasons. First is their relatively transparent, thin profile. Second is the fact that the profile is constantly changing as the bicycle moves. In addition bicycles can quickly maneuver in cluttered urban environments generating inaccurate tracking models and faulty prediction estimates.
Significant work has been done in sensor and algorithm development to solve the bicycle detection, tracking, and prediction problem, yet problems remain as datasets and algorithm analysis are not accessible to academic researchers. This information is instead considered proprietary. Of the published work in this field, most approaches use idealistic datasets that do not accurately represent real world conditions in order to improve the quality of their results.
To further the development of LiDAR sensors and algorithms the research paper introduces the first open LiDAR dataset, collected in real world environments. The author presents realistic datasets taken with affordable sensors, along with qualitative performance results of leading algorithms.
Easy access to this dataset and analysis allows researchers and developers to create systems and algorithms that perform in real world scenarios.