  Detecting and predicting the behavior of pedestrians is extremely crucial for
self-driving vehicles to plan and interact with them safely. Although there
have been several research works in this area, it is important to have fast and
memory efficient models such that it can operate in embedded hardware in these
autonomous machines. In this work, we propose a novel architecture using
spatial-temporal multi-tasking to do camera based pedestrian detection and
intention prediction. Our approach significantly reduces the latency by being
able to detect and predict all pedestrians' intention in a single shot manner
while also being able to attain better accuracy by sharing features with
relevant object level information and interactions.
