Probabilistic inference of traffic participants' lane change intention for enhancing adaptive cruise control

Adaptive cruise control is currently one of the most widely accepted vehicle driver assistance technologies. However, the main challenge is still due to the uncertainty about other traffic participants' intended driving maneuvers such as unexpected lane changes. In this paper, graphical modeling and unsupervised learning techniques are utilized to obtain a model for inferring the intention of drivers in surrounding vehicles. In particular, real-world driving data collected on public roads with only standard production sensors is utilized to synthesize a hidden Markov model with the aid of unscented Kalman filtering and the expectation maximization technique. Exact probabilistic inference is used to obtain a posterior probability distribution over the driver's intention. The proposed inference framework can be utilized to faster detect lane changes when compared to commercial rule-based approaches. This, in turn, gives the adaptive cruise control system more reaction time which yields a potential for improving safety and ride comfort.