For the most part, human detection capabilities of modern NNs are very good. This would include detections from a variety of camera angles and resolutions. There are already a lot of labeled training sets available with people in various poses, heights, clothing types, etc.
Bicycle detection is probably one of the more challenging elements as it relates to pedestrians and things you don't want a SD car to run into. Depending on the angle and color of the bicycle, rider position, and background elements, it can be challenging to discern the rider from the bicycle reliably. For the most part, just knowing there is a human present is a good start, but being able to anticipate movement speeds and directions of pedestrians vs. bikers is helpful in anticipating collision paths and distances, and also figuring out distances and terrain (person on bike is slightly elevated above ground, which can cause range perception issues, among other things).
source: have been working in AI/MV space for security/safety applications for 12+ years.
Bicycle detection is probably one of the more challenging elements as it relates to pedestrians and things you don't want a SD car to run into. Depending on the angle and color of the bicycle, rider position, and background elements, it can be challenging to discern the rider from the bicycle reliably. For the most part, just knowing there is a human present is a good start, but being able to anticipate movement speeds and directions of pedestrians vs. bikers is helpful in anticipating collision paths and distances, and also figuring out distances and terrain (person on bike is slightly elevated above ground, which can cause range perception issues, among other things).
source: have been working in AI/MV space for security/safety applications for 12+ years.