| Summary: | The achievement of level 5 autonomous vehicles on urban roads requires performance equal to that of a human driver in every scenario. In order to achieve this level of autonomy many challenging obstacles must be overcome. In this paper, we will address the specific challenges bicycles pose for self-driving cars in urban environments. One of the most prevalent challenges is detection and tracking of bicycles. Their relatively transparent profile, which changes as the bicycle moves, and their slight frames make detection a difficult problem. Furthermore, their ability to quickly maneuver in cluttered urban environments can generate 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.
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