Self-driving Vehicles
Mining vehicle behaviors from multi-sensor data for scenario parsing, surveillance, and collision warning.
Back in college, in 2008, I worked on self-driving vehicles, mining vehicle behaviors from raw multi-sensor data (Liu et al., 2008). The resulting qualitative descriptions of vehicle state feed downstream tasks like scenario parsing, vehicle surveillance, and forward-collision warning. Earlier approaches either assumed the number of behavior categories was known in advance or had to rediscover it by hand whenever the training data changed. We instead treat behaviors as the hidden states of a hidden Markov model and choose the number of states by cross-validated likelihood with a complexity penalty, so the behavior categories and their number are learned together. Experiments on real sensor data bear out the approach.